WorldWideScience

Sample records for satellite-based precipitation estimates

  1. Assessment of satellite-based precipitation estimates over Paraguay

    Science.gov (United States)

    Oreggioni Weiberlen, Fiorella; Báez Benítez, Julián

    2018-04-01

    Satellite-based precipitation estimates represent a potential alternative source of input data in a plethora of meteorological and hydrological applications, especially in regions characterized by a low density of rain gauge stations. Paraguay provides a good example of a case where the use of satellite-based precipitation could be advantageous. This study aims to evaluate the version 7 of the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TMPA V7; 3B42 V7) and the version 1.0 of the purely satellite-based product of the Climate Prediction Center Morphing Technique (CMORPH RAW) through their comparison with daily in situ precipitation measurements from 1998 to 2012 over Paraguay. The statistical assessment is conducted with several commonly used indexes. Specifically, to evaluate the accuracy of daily precipitation amounts, mean error (ME), root mean square error (RMSE), BIAS, and coefficient of determination (R 2) are used, and to analyze the capability to correctly detect different precipitation intensities, false alarm ratio (FAR), frequency bias index (FBI), and probability of detection (POD) are applied to various rainfall rates (0, 0.1, 0.5, 1, 2, 5, 10, 20, 40, 60, and 80 mm/day). Results indicate that TMPA V7 has a better performance than CMORPH RAW over Paraguay. TMPA V7 has higher accuracy in the estimation of daily rainfall volumes and greater precision in the detection of wet days (> 0 mm/day). However, both satellite products show a lower ability to appropriately detect high intensity precipitation events.

  2. Connecting Satellite-Based Precipitation Estimates to Users

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    Huffman, George J.; Bolvin, David T.; Nelkin, Eric

    2018-01-01

    Beginning in 1997, the Merged Precipitation Group at NASA Goddard has distributed gridded global precipitation products built by combining satellite and surface gauge data. This started with the Global Precipitation Climatology Project (GPCP), then the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), and recently the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG). This 20+-year (and on-going) activity has yielded an important set of insights and lessons learned for making state-of-the-art precipitation data accessible to the diverse communities of users. Merged-data products critically depend on the input sensors and the retrieval algorithms providing accurate, reliable estimates, but it is also important to provide ancillary information that helps users determine suitability for their application. We typically provide fields of estimated random error, and recently reintroduced the quality index concept at user request. Also at user request we have added a (diagnostic) field of estimated precipitation phase. Over time, increasingly more ancillary fields have been introduced for intermediate products that give expert users insight into the detailed performance of the combination algorithm, such as individual merged microwave and microwave-calibrated infrared estimates, the contributing microwave sensor types, and the relative influence of the infrared estimate.

  3. The Effectiveness of Using Limited Gauge Measurements for Bias Adjustment of Satellite-Based Precipitation Estimation over Saudi Arabia

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    Alharbi, Raied; Hsu, Kuolin; Sorooshian, Soroosh; Braithwaite, Dan

    2018-01-01

    Precipitation is a key input variable for hydrological and climate studies. Rain gauges are capable of providing reliable precipitation measurements at point scale. However, the uncertainty of rain measurements increases when the rain gauge network is sparse. Satellite -based precipitation estimations appear to be an alternative source of precipitation measurements, but they are influenced by systematic bias. In this study, a method for removing the bias from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated. The method consists of monthly empirical quantile mapping, climate classification, and inverse-weighted distance method. Daily PERSIANN-CCS is selected to test the capability of the method for removing the bias over Saudi Arabia during the period of 2010 to 2016. The first six years (2010 - 2015) are calibrated years and 2016 is used for validation. The results show that the yearly correlation coefficient was enhanced by 12%, the yearly mean bias was reduced by 93% during validated year. Root mean square error was reduced by 73% during validated year. The correlation coefficient, the mean bias, and the root mean square error show that the proposed method removes the bias on PERSIANN-CCS effectively that the method can be applied to other regions where the rain gauge network is sparse.

  4. Satellite-Based Precipitation Datasets

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    Munchak, S. J.; Huffman, G. J.

    2017-12-01

    Of the possible sources of precipitation data, those based on satellites provide the greatest spatial coverage. There is a wide selection of datasets, algorithms, and versions from which to choose, which can be confusing to non-specialists wishing to use the data. The International Precipitation Working Group (IPWG) maintains tables of the major publicly available, long-term, quasi-global precipitation data sets (http://www.isac.cnr.it/ ipwg/data/datasets.html), and this talk briefly reviews the various categories. As examples, NASA provides two sets of quasi-global precipitation data sets: the older Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and current Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG). Both provide near-real-time and post-real-time products that are uniformly gridded in space and time. The TMPA products are 3-hourly 0.25°x0.25° on the latitude band 50°N-S for about 16 years, while the IMERG products are half-hourly 0.1°x0.1° on 60°N-S for over 3 years (with plans to go to 16+ years in Spring 2018). In addition to the precipitation estimates, each data set provides fields of other variables, such as the satellite sensor providing estimates and estimated random error. The discussion concludes with advice about determining suitability for use, the necessity of being clear about product names and versions, and the need for continued support for satellite- and surface-based observation.

  5. Seasonal to Interannual Variability of Satellite-Based Precipitation Estimates in the Pacific Ocean Associated with ENSO from 1998 to 2014

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    Xueyan Hou

    2016-10-01

    Full Text Available Based on a widely used satellite precipitation product (TRMM Multi-satellite Precipitation Analysis 3B43, we analyzed the spatiotemporal variability of precipitation over the Pacific Ocean for 1998–2014 at seasonal and interannual timescales, separately, using the conventional empirical orthogonal function (EOF and investigated the seasonal patterns associated with El Niño–Southern Oscillation (ENSO cycles using season-reliant empirical orthogonal function (SEOF analysis. Lagged correlation analysis was also applied to derive the lead/lag correlations of the first two SEOF modes for precipitation with Pacific Decadal Oscillation (PDO and two types of El Niño, i.e., central Pacific (CP El Niño and eastern Pacific (EP El Niño. We found that: (1 The first two seasonal EOF modes for precipitation represent the annual cycle of precipitation variations for the Pacific Ocean and the first interannual EOF mode shows the spatiotemporal variability associated with ENSO; (2 The first SEOF mode for precipitation is simultaneously associated with the development of El Niño and most likely coincides with CP El Niño. The second SEOF mode lagged behind ENSO by one year and is associated with post-El Niño years. PDO modulates precipitation variability significantly only when ENSO occurs by strengthening and prolonging the impacts of ENSO; (3 Seasonally evolving patterns of the first two SEOF modes represent the consecutive precipitation patterns associated with the entire development of EP El Niño and the following recovery year. The most significant variation occurs over the tropical Pacific, especially in the Intertropical Convergence Zone (ITCZ and South Pacific Convergence Zone (SPCZ; (4 Dry conditions in the western basin of the warm pool and wet conditions along the ITCZ and SPCZ bands during the mature phase of El Niño are associated with warm sea surface temperatures in the central tropical Pacific, and a subtropical anticyclone dominating

  6. Groundwater Modelling For Recharge Estimation Using Satellite Based Evapotranspiration

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    Soheili, Mahmoud; (Tom) Rientjes, T. H. M.; (Christiaan) van der Tol, C.

    2017-04-01

    Groundwater movement is influenced by several factors and processes in the hydrological cycle, from which, recharge is of high relevance. Since the amount of aquifer extractable water directly relates to the recharge amount, estimation of recharge is a perquisite of groundwater resources management. Recharge is highly affected by water loss mechanisms the major of which is actual evapotranspiration (ETa). It is, therefore, essential to have detailed assessment of ETa impact on groundwater recharge. The objective of this study was to evaluate how recharge was affected when satellite-based evapotranspiration was used instead of in-situ based ETa in the Salland area, the Netherlands. The Methodology for Interactive Planning for Water Management (MIPWA) model setup which includes a groundwater model for the northern part of the Netherlands was used for recharge estimation. The Surface Energy Balance Algorithm for Land (SEBAL) based actual evapotranspiration maps from Waterschap Groot Salland were also used. Comparison of SEBAL based ETa estimates with in-situ abased estimates in the Netherlands showed that these SEBAL estimates were not reliable. As such results could not serve for calibrating root zone parameters in the CAPSIM model. The annual cumulative ETa map produced by the model showed that the maximum amount of evapotranspiration occurs in mixed forest areas in the northeast and a portion of central parts. Estimates ranged from 579 mm to a minimum of 0 mm in the highest elevated areas with woody vegetation in the southeast of the region. Variations in mean seasonal hydraulic head and groundwater level for each layer showed that the hydraulic gradient follows elevation in the Salland area from southeast (maximum) to northwest (minimum) of the region which depicts the groundwater flow direction. The mean seasonal water balance in CAPSIM part was evaluated to represent recharge estimation in the first layer. The highest recharge estimated flux was for autumn

  7. Validation of satellite based precipitation over diverse topography of Pakistan

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    Iqbal, Muhammad Farooq; Athar, H.

    2018-03-01

    This study evaluates the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product data with 0.25° × 0.25° spatial and post-real-time 3 h temporal resolution using point-based Surface Precipitation Gauge (SPG) data from 40 stations, for the period 1998-2013, and using gridded Asian Precipitation ˗ Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) data abbreviated as APH data with 0.25° × 0.25° spatial and daily temporal resolution for the period 1998-2007, over vulnerable and data sparse regions of Pakistan (24-37° N and 62-75° E). To evaluate the performance of TMPA relative to SPG and APH, four commonly used statistical indicator metrics including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Correlation Coefficient (CC) are employed on daily, monthly, seasonal as well as on annual timescales. The TMPA slightly overestimated both SPG and APH at daily, monthly, and annual timescales, however close results were obtained between TMPA and SPG as compared to those between TMPA and APH, on the same timescale. The TMPA overestimated both SPG and APH during the Pre-Monsoon and Monsoon seasons, whereas it underestimated during the Post-Monsoon and Winter seasons, with different magnitudes. Agreement between TMPA and SPG was good in plain and medium elevation regions, whereas TMPA overestimated APH in 31 stations. The magnitudes of MAE and RMSE were high at daily timescale as compared to monthly and annual timescales. Relatively large MAE was observed in stations located over high elevation regions, whereas minor MAE was recorded in plain area stations at daily, monthly, and annual timescales. A strong positive linear relationship between TMPA and SPG was established at monthly (0.98), seasonal (0.93 to 0.98) and annual (0.97) timescales. Precipitation increased with the increase of elevation, and not only elevation but latitude also affected the

  8. Fine-tuning satellite-based rainfall estimates

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    Harsa, Hastuadi; Buono, Agus; Hidayat, Rahmat; Achyar, Jaumil; Noviati, Sri; Kurniawan, Roni; Praja, Alfan S.

    2018-05-01

    Rainfall datasets are available from various sources, including satellite estimates and ground observation. The locations of ground observation scatter sparsely. Therefore, the use of satellite estimates is advantageous, because satellite estimates can provide data on places where the ground observations do not present. However, in general, the satellite estimates data contain bias, since they are product of algorithms that transform the sensors response into rainfall values. Another cause may come from the number of ground observations used by the algorithms as the reference in determining the rainfall values. This paper describe the application of bias correction method to modify the satellite-based dataset by adding a number of ground observation locations that have not been used before by the algorithm. The bias correction was performed by utilizing Quantile Mapping procedure between ground observation data and satellite estimates data. Since Quantile Mapping required mean and standard deviation of both the reference and the being-corrected data, thus the Inverse Distance Weighting scheme was applied beforehand to the mean and standard deviation of the observation data in order to provide a spatial composition of them, which were originally scattered. Therefore, it was possible to provide a reference data point at the same location with that of the satellite estimates. The results show that the new dataset have statistically better representation of the rainfall values recorded by the ground observation than the previous dataset.

  9. Japanese Global Precipitation Measurement (GPM) mission status and application of satellite-based global rainfall map

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    Kachi, Misako; Shimizu, Shuji; Kubota, Takuji; Yoshida, Naofumi; Oki, Riko; Kojima, Masahiro; Iguchi, Toshio; Nakamura, Kenji

    2010-05-01

    . Collaboration with GCOM-W is not only limited to its participation to GPM constellation but also coordination in areas of algorithm development and validation in Japan. Generation of high-temporal and high-accurate global rainfall map is one of targets of the GPM mission. As a proto-type for GPM era, JAXA has developed and operates the Global Precipitation Map algorithm in near-real-time since October 2008, and hourly and 0.1-degree resolution binary data and images available at http://sharaku.eorc.jaxa.jp/GSMaP/ four hours after observation. The algorithms are based on outcomes from the Global Satellite Mapping for Precipitation (GSMaP) project, which was sponsored by the Japan Science and Technology Agency (JST) under the Core Research for Evolutional Science and Technology (CREST) framework between 2002 and 2007 (Okamoto et al., 2005; Aonashi et al., 2009; Ushio et al., 2009). Target of GSMaP project is to produce global rainfall maps that are highly accurate and in high temporal and spatial resolution through the development of rain rate retrieval algorithms based on reliable precipitation physical models by using several microwave radiometer data, and comprehensive use of precipitation radar and geostationary infrared imager data. Near-real-time GSMaP data is distributed via internet and utilized by end users. Purpose of data utilization by each user covers broad areas and in world wide; Science researches (model validation, data assimilation, typhoon study, etc.), weather forecast/service, flood warning and rain analysis over river basin, oceanographic condition forecast, agriculture, and education. Toward the GPM era, operational application should be further emphasized as well as science application. JAXA continues collaboration with hydrological communities to utilize satellite-based precipitation data as inputs to future flood prediction and warning system, as well as with meteorological agencies to proceed further data utilization in numerical weather prediction

  10. Developing Information Services and Tools to Access and Evaluate Data Quality in Global Satellite-based Precipitation Products

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    Liu, Z.; Shie, C. L.; Meyer, D. J.

    2017-12-01

    Global satellite-based precipitation products have been widely used in research and applications around the world. Compared to ground-based observations, satellite-based measurements provide precipitation data on a global scale, especially in remote continents and over oceans. Over the years, satellite-based precipitation products have evolved from single sensor and single algorithm to multi-sensors and multi-algorithms. As a result, many satellite-based precipitation products have been enhanced such as spatial and temporal coverages. With inclusion of ground-based measurements, biases of satellite-based precipitation products have been significantly reduced. However, data quality issues still exist and can be caused by many factors such as observations, satellite platform anomaly, algorithms, production, calibration, validation, data services, etc. The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) is home to NASA global precipitation product archives including the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM), as well as other global and regional precipitation products. Precipitation is one of the top downloaded and accessed parameters in the GES DISC data archive. Meanwhile, users want to easily locate and obtain data quality information at regional and global scales to better understand how precipitation products perform and how reliable they are. As data service providers, it is necessary to provide an easy access to data quality information, however, such information normally is not available, and when it is available, it is not in one place and difficult to locate. In this presentation, we will present challenges and activities at the GES DISC to address precipitation data quality issues.

  11. Development and validation of satellite based estimates of surface visibility

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    Brunner, J.; Pierce, R. B.; Lenzen, A.

    2015-10-01

    A satellite based surface visibility retrieval has been developed using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements as a proxy for Advanced Baseline Imager (ABI) data from the next generation of Geostationary Operational Environmental Satellites (GOES-R). The retrieval uses a multiple linear regression approach to relate satellite aerosol optical depth, fog/low cloud probability and thickness retrievals, and meteorological variables from numerical weather prediction forecasts to National Weather Service Automated Surface Observing System (ASOS) surface visibility measurements. Validation using independent ASOS measurements shows that the GOES-R ABI surface visibility retrieval (V) has an overall success rate of 64.5% for classifying Clear (V ≥ 30 km), Moderate (10 km ≤ V United States Environmental Protection Agency (EPA) and National Park Service (NPS) Interagency Monitoring of Protected Visual Environments (IMPROVE) network, and provide useful information to the regional planning offices responsible for developing mitigation strategies required under the EPA's Regional Haze Rule, particularly during regional haze events associated with smoke from wildfires.

  12. Improving satellite-based post-fire evapotranspiration estimates in semi-arid regions

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    Poon, P.; Kinoshita, A. M.

    2017-12-01

    Climate change and anthropogenic factors contribute to the increased frequency, duration, and size of wildfires, which can alter ecosystem and hydrological processes. The loss of vegetation canopy and ground cover reduces interception and alters evapotranspiration (ET) dynamics in riparian areas, which can impact rainfall-runoff partitioning. Previous research evaluated the spatial and temporal trends of ET based on burn severity and observed an annual decrease of 120 mm on average for three years after fire. Building upon these results, this research focuses on the Coyote Fire in San Diego, California (USA), which burned a total of 76 km2 in 2003 to calibrate and improve satellite-based ET estimates in semi-arid regions affected by wildfire. The current work utilizes satellite-based products and techniques such as the Google Earth Engine Application programming interface (API). Various ET models (ie. Operational Simplified Surface Energy Balance Model (SSEBop)) are compared to the latent heat flux from two AmeriFlux eddy covariance towers, Sky Oaks Young (US-SO3), and Old Stand (US-SO2), from 2000 - 2015. The Old Stand tower has a low burn severity and the Young Stand tower has a moderate to high burn severity. Both towers are used to validate spatial ET estimates. Furthermore, variables and indices, such as Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and the Normalized Burn Ratio (NBR) are utilized to evaluate satellite-based ET through a multivariate statistical analysis at both sites. This point-scale study will able to improve ET estimates in spatially diverse regions. Results from this research will contribute to the development of a post-wildfire ET model for semi-arid regions. Accurate estimates of post-fire ET will provide a better representation of vegetation and hydrologic recovery, which can be used to improve hydrologic models and predictions.

  13. Evaluation of Satellite-Based Precipitation Products from IMERG V04A and V03D, CMORPH and TMPA with Gauged Rainfall in Three Climatologic Zones in China

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    Guanghua Wei

    2017-12-01

    Full Text Available A critical evaluation of the newly released precipitation data set is very important for both the end users and data developers. Meanwhile, the evaluation may provide a benchmark for the product’s continued development and future improvement. To these ends, the four precipitation estimates including IMERG (the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement V04A, IMERG V03D, CMORPH (the Climate Prediction Center Morphing technique-CRT and TRMM (the Tropical Rainfall Measuring Mission 3B42 are systematically evaluated against the gauge precipitation estimates at multiple spatiotemporal scales from 1 June 2014 to 30 November 2015 over three different topographic and climatic watersheds in China. Meanwhile, the statistical methods are utilized to quantize the performance of the four satellite-based precipitation estimates. The results show that: (1 over the Tibetan Plateau cold region, among all products, IMERG V04A underestimates precipitation with the largest RB (−46.98% during the study period and the similar results are seen at the seasonal scale. However, IMERG V03D demonstrates the best performance according to RB (7.46%, RMSE (0.44 mm/day and RRMSE (28.37%. Except for in summer, TRMM 3B42 perform better than CMORPH according to RMSEs, RRMSEs and Rs; (2 within the semi-humid Huaihe River Basin, IMERG V04A has a slight advantage over the other three satellite-based precipitation products with the lowest RMSE (0.32 mm/day during the evaluation period and followed by IMERG V03D, TRMM 3B42 and CMORPH orderly; (3 over the arid/semi-arid Weihe River Basin, in comparison with the other three products, TRMM 3B42 demonstrates the best performance with the lowest RMSE (0.1 mm/day, RRMSE (8.44% and highest R (0.92 during the study period. Meanwhile, IMERG V03D perform better than IMERG V04A according all the statistical indicators; (4 in winter, IMERG V04A and IMERG V03D tend to underestimate the total precipitation

  14. Evaluation of Clear Sky Models for Satellite-Based Irradiance Estimates

    Energy Technology Data Exchange (ETDEWEB)

    Sengupta, Manajit [National Renewable Energy Lab. (NREL), Golden, CO (United States); Gotseff, Peter [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2013-12-01

    This report describes an intercomparison of three popular broadband clear sky solar irradiance model results with measured data, as well as satellite-based model clear sky results compared to measured clear sky data. The authors conclude that one of the popular clear sky models (the Bird clear sky model developed by Richard Bird and Roland Hulstrom) could serve as a more accurate replacement for current satellite-model clear sky estimations. Additionally, the analysis of the model results with respect to model input parameters indicates that rather than climatological, annual, or monthly mean input data, higher-time-resolution input parameters improve the general clear sky model performance.

  15. Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations

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    Mehran, A.; AghaKouchak, A.; Phillips, T. J.

    2014-02-01

    The objective of this study is to cross-validate 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations of precipitation against the Global Precipitation Climatology Project (GPCP) data, quantifying model pattern discrepancies, and biases for both entire distributions and their upper tails. The results of the volumetric hit index (VHI) analysis of the total monthly precipitation amounts show that most CMIP5 simulations are in good agreement with GPCP patterns in many areas but that their replication of observed precipitation over arid regions and certain subcontinental regions (e.g., northern Eurasia, eastern Russia, and central Australia) is problematical. Overall, the VHI of the multimodel ensemble mean and median also are superior to that of the individual CMIP5 models. However, at high quantiles of reference data (75th and 90th percentiles), all climate models display low skill in simulating precipitation, except over North America, the Amazon, and Central Africa. Analyses of total bias (B) in CMIP5 simulations reveal that most models overestimate precipitation over regions of complex topography (e.g., western North and South America and southern Africa and Asia), while underestimating it over arid regions. Also, while most climate model simulations show low biases over Europe, intermodel variations in bias over Australia and Amazonia are considerable. The quantile bias analyses indicate that CMIP5 simulations are even more biased at high quantiles of precipitation. It is found that a simple mean field bias removal improves the overall B and VHI values but does not make a significant improvement at high quantiles of precipitation.

  16. An intercomparison and validation of satellite-based surface radiative energy flux estimates over the Arctic

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    Riihelä, Aku; Key, Jeffrey R.; Meirink, Jan Fokke; Kuipers Munneke, Peter; Palo, Timo; Karlsson, Karl-Göran

    2017-05-01

    Accurate determination of radiative energy fluxes over the Arctic is of crucial importance for understanding atmosphere-surface interactions, melt and refreezing cycles of the snow and ice cover, and the role of the Arctic in the global energy budget. Satellite-based estimates can provide comprehensive spatiotemporal coverage, but the accuracy and comparability of the existing data sets must be ascertained to facilitate their use. Here we compare radiative flux estimates from Clouds and the Earth's Radiant Energy System (CERES) Synoptic 1-degree (SYN1deg)/Energy Balanced and Filled, Global Energy and Water Cycle Experiment (GEWEX) surface energy budget, and our own experimental FluxNet / Satellite Application Facility on Climate Monitoring cLoud, Albedo and RAdiation (CLARA) data against in situ observations over Arctic sea ice and the Greenland Ice Sheet during summer of 2007. In general, CERES SYN1deg flux estimates agree best with in situ measurements, although with two particular limitations: (1) over sea ice the upwelling shortwave flux in CERES SYN1deg appears to be underestimated because of an underestimated surface albedo and (2) the CERES SYN1deg upwelling longwave flux over sea ice saturates during midsummer. The Advanced Very High Resolution Radiometer-based GEWEX and FluxNet-CLARA flux estimates generally show a larger range in retrieval errors relative to CERES, with contrasting tendencies relative to each other. The largest source of retrieval error in the FluxNet-CLARA downwelling shortwave flux is shown to be an overestimated cloud optical thickness. The results illustrate that satellite-based flux estimates over the Arctic are not yet homogeneous and that further efforts are necessary to investigate the differences in the surface and cloud properties which lead to disagreements in flux retrievals.

  17. Examining the utility of satellite-based wind sheltering estimates for lake hydrodynamic modeling

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    Van Den Hoek, Jamon; Read, Jordan S.; Winslow, Luke A.; Montesano, Paul; Markfort, Corey D.

    2015-01-01

    Satellite-based measurements of vegetation canopy structure have been in common use for the last decade but have never been used to estimate canopy's impact on wind sheltering of individual lakes. Wind sheltering is caused by slower winds in the wake of topography and shoreline obstacles (e.g. forest canopy) and influences heat loss and the flux of wind-driven mixing energy into lakes, which control lake temperatures and indirectly structure lake ecosystem processes, including carbon cycling and thermal habitat partitioning. Lakeshore wind sheltering has often been parameterized by lake surface area but such empirical relationships are only based on forested lakeshores and overlook the contributions of local land cover and terrain to wind sheltering. This study is the first to examine the utility of satellite imagery-derived broad-scale estimates of wind sheltering across a diversity of land covers. Using 30 m spatial resolution ASTER GDEM2 elevation data, the mean sheltering height, hs, being the combination of local topographic rise and canopy height above the lake surface, is calculated within 100 m-wide buffers surrounding 76,000 lakes in the U.S. state of Wisconsin. Uncertainty of GDEM2-derived hs was compared to SRTM-, high-resolution G-LiHT lidar-, and ICESat-derived estimates of hs, respective influences of land cover type and buffer width on hsare examined; and the effect of including satellite-based hs on the accuracy of a statewide lake hydrodynamic model was discussed. Though GDEM2 hs uncertainty was comparable to or better than other satellite-based measures of hs, its higher spatial resolution and broader spatial coverage allowed more lakes to be included in modeling efforts. GDEM2 was shown to offer superior utility for estimating hs compared to other satellite-derived data, but was limited by its consistent underestimation of hs, inability to detect within-buffer hs variability, and differing accuracy across land cover types. Nonetheless

  18. Evaluation of TRMM satellite-based precipitation indexes for flood forecasting over Riyadh City, Saudi Arabia

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    Tekeli, Ahmet Emre; Fouli, Hesham

    2016-10-01

    Floods are among the most common disasters harming humanity. In particular, flash floods cause hazards to life, property and any type of structures. Arid and semi-arid regions are equally prone to flash floods like regions with abundant rainfall. Despite rareness of intensive and frequent rainfall events over Kingdom of Saudi Arabia (KSA); an arid/semi-arid region, occasional flash floods occur and result in large amounts of damaging surface runoff. The flooding of 16 November, 2013 in Riyadh; the capital city of KSA, resulted in killing some people and led to much property damage. The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) Real Time (RT) data (3B42RT) are used herein for flash flood forecasting. 3B42RT detected high-intensity rainfall events matching with the distribution of observed floods over KSA. A flood early warning system based on exceedance of threshold limits on 3B42RT data is proposed for Riyadh. Three different indexes: Constant Threshold (CT), Cumulative Distribution Functions (CDF) and Riyadh Flood Precipitation Index (RFPI) are developed using 14-year 3B42RT data from 2000 to 2013. RFPI and CDF with 90% captured the three major flooding events that occurred in February 2005, May 2010 and November 2013 in Riyadh. CT with 3 mm/h intensity indicated the 2013 flooding, but missed those of 2005 and 2010. The methodology implemented herein is a first-step simple and accurate way for flash flood forecasting over Riyadh. The simplicity of the methodology enables its applicability for the TRMM follow-on missions like Global Precipitation Measurement (GPM) mission.

  19. Satellite-based ET estimation using Landsat 8 images and SEBAL model

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    Bruno Bonemberger da Silva

    Full Text Available ABSTRACT Estimation of evapotranspiration is a key factor to achieve sustainable water management in irrigated agriculture because it represents water use of crops. Satellite-based estimations provide advantages compared to direct methods as lysimeters especially when the objective is to calculate evapotranspiration at a regional scale. The present study aimed to estimate the actual evapotranspiration (ET at a regional scale, using Landsat 8 - OLI/TIRS images and complementary data collected from a weather station. SEBAL model was used in South-West Paraná, region composed of irrigated and dry agricultural areas, native vegetation and urban areas. Five Landsat 8 images, row 223 and path 78, DOY 336/2013, 19/2014, 35/2014, 131/2014 and 195/2014 were used, from which ET at daily scale was estimated as a residual of the surface energy balance to produce ET maps. The steps for obtain ET using SEBAL include radiometric calibration, calculation of the reflectance, surface albedo, vegetation indexes (NDVI, SAVI and LAI and emissivity. These parameters were obtained based on the reflective bands of the orbital sensor with temperature surface estimated from thermal band. The estimated ET values in agricultural areas, native vegetation and urban areas using SEBAL algorithm were compatible with those shown in the literature and ET errors between the ET estimates from SEBAL model and Penman Monteith FAO 56 equation were less than or equal to 1.00 mm day-1.

  20. Using satellite-based rainfall estimates for streamflow modelling: Bagmati Basin

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    Shrestha, M.S.; Artan, Guleid A.; Bajracharya, S.R.; Sharma, R. R.

    2008-01-01

    In this study, we have described a hydrologic modelling system that uses satellite-based rainfall estimates and weather forecast data for the Bagmati River Basin of Nepal. The hydrologic model described is the US Geological Survey (USGS) Geospatial Stream Flow Model (GeoSFM). The GeoSFM is a spatially semidistributed, physically based hydrologic model. We have used the GeoSFM to estimate the streamflow of the Bagmati Basin at Pandhera Dovan hydrometric station. To determine the hydrologic connectivity, we have used the USGS Hydro1k DEM dataset. The model was forced by daily estimates of rainfall and evapotranspiration derived from weather model data. The rainfall estimates used for the modelling are those produced by the National Oceanic and Atmospheric Administration Climate Prediction Centre and observed at ground rain gauge stations. The model parameters were estimated from globally available soil and land cover datasets – the Digital Soil Map of the World by FAO and the USGS Global Land Cover dataset. The model predicted the daily streamflow at Pandhera Dovan gauging station. The comparison of the simulated and observed flows at Pandhera Dovan showed that the GeoSFM model performed well in simulating the flows of the Bagmati Basin.

  1. Satellite-based Estimates of Ambient Air Pollution and Global Variations in Childhood Asthma Prevalence

    Science.gov (United States)

    Anderson, H. Ross; Butland, Barbara K.; Donkelaar, Aaron Matthew Van; Brauer, Michael; Strachan, David P.; Clayton, Tadd; van Dingenen, Rita; Amann, Marcus; Brunekreef, Bert; Cohen, Aaron; hide

    2012-01-01

    Background: The effect of ambient air pollution on global variations and trends in asthma prevalence is unclear. Objectives: Our goal was to investigate community-level associations between asthma prevalence data from the International Study of Asthma and Allergies in Childhood (ISAAC) and satellite-based estimates of particulate matter with aerodynamic diameter < 2.5 microm (PM2.5) and nitrogen dioxide (NO2), and modelled estimates of ozone. Methods: We assigned satellite-based estimates of PM2.5 and NO2 at a spatial resolution of 0.1deg × 0.1deg and modeled estimates of ozone at a resolution of 1deg × 1deg to 183 ISAAC centers. We used center-level prevalence of severe asthma as the outcome and multilevel models to adjust for gross national income (GNI) and center- and country-level sex, climate, and population density. We examined associations (adjusting for GNI) between air pollution and asthma prevalence over time in centers with data from ISAAC Phase One (mid-1900s) and Phase Three (2001-2003). Results: For the 13- to 14-year age group (128 centers in 28 countries), the estimated average within-country change in center-level asthma prevalence per 100 children per 10% increase in center-level PM2.5 and NO2 was -0.043 [95% confidence interval (CI): -0.139, 0.053] and 0.017 (95% CI: -0.030, 0.064) respectively. For ozone the estimated change in prevalence per parts per billion by volume was -0.116 (95% CI: -0.234, 0.001). Equivalent results for the 6- to 7-year age group (83 centers in 20 countries), though slightly different, were not significantly positive. For the 13- to 14-year age group, change in center-level asthma prevalence over time per 100 children per 10% increase in PM2.5 from Phase One to Phase Three was -0.139 (95% CI: -0.347, 0.068). The corresponding association with ozone (per ppbV) was -0.171 (95% CI: -0.275, -0.067). Conclusion: In contrast to reports from within-community studies of individuals exposed to traffic pollution, we did not find

  2. Air-sea fluxes and satellite-based estimation of water masses formation

    Science.gov (United States)

    Sabia, Roberto; Klockmann, Marlene; Fernandez-Prieto, Diego; Donlon, Craig

    2015-04-01

    Recent work linking satellite-based measurements of sea surface salinity (SSS) and sea surface temperature (SST) with traditional physical oceanography has demonstrated the capability of generating routinely satellite-derived surface T-S diagrams [1] and analyze the distribution/dynamics of SSS and its relative surface density with respect to in-situ measurements. Even more recently [2,3], this framework has been extended by exploiting these T-S diagrams as a diagnostic tool to derive water masses formation rates and areas. A water mass describes a water body with physical properties distinct from the surrounding water, formed at the ocean surface under specific conditions which determine its temperature and salinity. The SST and SSS (and thus also density) at the ocean surface are largely determined by fluxes of heat and freshwater. The surface density flux is a function of the latter two and describes the change of the density of seawater at the surface. To obtain observations of water mass formation is of great interest, since they serve as indirect observations of the thermo-haline circulation. The SSS data which has become available through the SMOS [4] and Aquarius [5] satellite missions will provide the possibility of studying also the effect of temporally-varying SSS fields on water mass formation. In the present study, the formation of water masses as a function of SST and SSS is derived from the surface density flux by integrating the latter over a specific area and time period in bins of SST and SSS and then taking the derivative of the total density flux with respect to density. This study presents a test case using SMOS SSS, OSTIA SST, as well as Argo ISAS SST and SSS for comparison, heat fluxes from the NOCS Surface Flux Data Set v2.0, OAFlux evaporation and CMORPH precipitation. The study area, initially referred to the North Atlantic, is extended over two additional ocean basins and the study period covers the 2011-2012 timeframe. Yearly, seasonal

  3. Hydrological modeling of the Peruvian–Ecuadorian Amazon Basin using GPM-IMERG satellite-based precipitation dataset

    Directory of Open Access Journals (Sweden)

    R. Zubieta

    2017-07-01

    Full Text Available In the last two decades, rainfall estimates provided by the Tropical Rainfall Measurement Mission (TRMM have proven applicable in hydrological studies. The Global Precipitation Measurement (GPM mission, which provides the new generation of rainfall estimates, is now considered a global successor to TRMM. The usefulness of GPM data in hydrological applications, however, has not yet been evaluated over the Andean and Amazonian regions. This study uses GPM data provided by the Integrated Multi-satellite Retrievals (IMERG (product/final run as input to a distributed hydrological model for the Amazon Basin of Peru and Ecuador for a 16-month period (from March 2014 to June 2015 when all datasets are available. TRMM products (TMPA V7 and TMPA RT datasets and a gridded precipitation dataset processed from observed rainfall are used for comparison. The results indicate that precipitation data derived from GPM-IMERG correspond more closely to TMPA V7 than TMPA RT datasets, but both GPM-IMERG and TMPA V7 precipitation data tend to overestimate, compared to observed rainfall (by 11.1 and 15.7 %, respectively. In general, GPM-IMERG, TMPA V7 and TMPA RT correlate with observed rainfall, with a similar number of rain events correctly detected ( ∼  20 %. Statistical analysis of modeled streamflows indicates that GPM-IMERG is as useful as TMPA V7 or TMPA RT datasets in southern regions (Ucayali Basin. GPM-IMERG, TMPA V7 and TMPA RT do not properly simulate streamflows in northern regions (Marañón and Napo basins, probably because of the lack of adequate rainfall estimates in northern Peru and the Ecuadorian Amazon.

  4. Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX

    Science.gov (United States)

    Real-time rainfall accumulation estimates at the global scale is useful for many applications. However, the real-time versions of satellite-based rainfall products are known to contain errors relative to real rainfall observed in situ. Recent studies have demonstrated how information about rainfall ...

  5. Estimating Total Discharge in the Yangtze River Basin Using Satellite-Based Observations

    Directory of Open Access Journals (Sweden)

    Samuel A. Andam‑Akorful

    2013-07-01

    Full Text Available The measurement of total basin discharge along coastal regions is necessary for understanding the hydrological and oceanographic issues related to the water and energy cycles. However, only the observed streamflow (gauge-based observation is used to estimate the total fluxes from the river basin to the ocean, neglecting the portion of discharge that infiltrates to underground and directly discharges into the ocean. Hence, the aim of this study is to assess the total discharge of the Yangtze River (Chang Jiang basin. In this study, we explore the potential response of total discharge to changes in precipitation (from the Tropical Rainfall Measuring Mission—TRMM, evaporation (from four versions of the Global Land Data Assimilation—GLDAS, namely, CLM, Mosaic, Noah and VIC, and water-storage changes (from the Gravity Recovery and Climate Experiment—GRACE by using the terrestrial water budget method. This method has been validated by comparison with the observed streamflow, and shows an agreement with a root mean square error (RMSE of 14.30 mm/month for GRACE-based discharge and 20.98 mm/month for that derived from precipitation minus evaporation (P − E. This improvement of approximately 32% indicates that monthly terrestrial water-storage changes, as estimated by GRACE, cannot be considered negligible over Yangtze basin. The results for the proposed method are more accurate than the results previously reported in the literature.

  6. Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

    Science.gov (United States)

    Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

    2017-08-01

    Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2  = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.

  7. Development and validation of satellite-based estimates of surface visibility

    Science.gov (United States)

    Brunner, J.; Pierce, R. B.; Lenzen, A.

    2016-02-01

    A satellite-based surface visibility retrieval has been developed using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements as a proxy for Advanced Baseline Imager (ABI) data from the next generation of Geostationary Operational Environmental Satellites (GOES-R). The retrieval uses a multiple linear regression approach to relate satellite aerosol optical depth, fog/low cloud probability and thickness retrievals, and meteorological variables from numerical weather prediction forecasts to National Weather Service Automated Surface Observing System (ASOS) surface visibility measurements. Validation using independent ASOS measurements shows that the GOES-R ABI surface visibility retrieval (V) has an overall success rate of 64.5 % for classifying clear (V ≥ 30 km), moderate (10 km ≤ V United States Environmental Protection Agency (EPA) and National Park Service (NPS) Interagency Monitoring of Protected Visual Environments (IMPROVE) network and provide useful information to the regional planning offices responsible for developing mitigation strategies required under the EPA's Regional Haze Rule, particularly during regional haze events associated with smoke from wildfires.

  8. Estimating Tropical Cyclone Precipitation from Station Observations

    Institute of Scientific and Technical Information of China (English)

    REN Fumin; WANG Yongmei; WANG Xiaoling; LI Weijing

    2007-01-01

    In this paper, an objective technique for estimating the tropical cyclone (TC) precipitation from station observations is proposed. Based on a comparison between the Original Objective Method (OOM) and the Expert Subjective Method (ESM), the Objective Synoptic Analysis Technique (OSAT) for partitioning TC precipitation was developed by analyzing the western North Pacific (WNP) TC historical track and the daily precipitation datasets. Being an objective way of the ESM, OSAT overcomes the main problems in OOM,by changing two fixed parameters in OOM, the thresholds for the distance of the absolute TC precipitation (D0) and the TC size (D1), into variable parameters.Case verification for OSAT was also carried out by applying CMORPH (Climate Prediction Center MORPHing technique) daily precipitation measurements, which is NOAA's combined satellite precipitation measurement system. This indicates that OSAT is capable of distinguishing simultaneous TC precipitation rain-belts from those associated with different TCs or with middle-latitude weather systems.

  9. Estimating ionospheric delay using kriging: 2. Impact on satellite-based augmentation system availability

    Science.gov (United States)

    Sparks, Lawrence; Blanch, Juan; Pandya, Nitin

    2011-12-01

    An augmentation of the Global Positioning System, the Wide Area Augmentation System (WAAS) broadcasts, at each node of an ionospheric grid, an estimate of the vertical ionospheric delay and an integrity bound on the vertical delay error. To date, these quantities have been determined from a planar fit of slant delay measurements, projected to vertical using an obliquity factor specified by the standard thin shell model of the ionosphere. In a future WAAS upgrade (WAAS Follow-On Release 3), however, they will be calculated using an established, geo-statistical estimation technique known as kriging that generally provides higher estimate accuracy than planar fit estimation. This paper analyzes the impact of kriging on system availability. In a preliminary assessment, kriging is found to produce improvements in availability of up to 15%.

  10. An improvement of satellite-based algorithm for gross primary production estimation optimized over Korea

    Science.gov (United States)

    Pi, Kyoung-Jin; Han, Kyung-Soo; Kim, In-Hwan; Kim, Sang-Il; Lee, Min-Ji

    2011-11-01

    Monitoring the global gross primary production (GPP) is relevant to understanding the global carbon cycle and evaluating the effects of interannual climate variation on food and fiber production. GPP, the flux of carbon into ecosystems via photosynthetic assimilation, is an important variable in the global carbon cycle and a key process in land surface-atmosphere interactions. The Moderate-resolution Imaging Spectroradiometer (MODIS) is one of the primary global monitoring sensors. MODIS GPP has some of the problems that have been proven in several studies. Therefore this study was to solve the regional mismatch that occurs when using the MODIS GPP global product over Korea. To solve this problem, we estimated each of the GPP component variables separately to improve the GPP estimates. We compared our GPP estimates with validation GPP data to assess their accuracy. For all sites, the correlation was close with high significance (R2 = 0.8164, RMSE = 0.6126 g.C.m-2.d-1, bias = -0.0271 g.C.m-2.d-1). We also compared our results to those of other models. The component variables tended to be either over- or under-estimated when compared to those in other studies over the Korean peninsula, although the estimated GPP was better. The results of this study will likely improve carbon cycle modeling by capturing finer patterns with an integrated method of remote sensing. Keywords: VEGETATION, Gross Primary Production, MODIS.

  11. Assimilating satellite-based canopy height within an ecosystem model to estimate aboveground forest biomass

    Science.gov (United States)

    Joetzjer, E.; Pillet, M.; Ciais, P.; Barbier, N.; Chave, J.; Schlund, M.; Maignan, F.; Barichivich, J.; Luyssaert, S.; Hérault, B.; von Poncet, F.; Poulter, B.

    2017-07-01

    Despite advances in Earth observation and modeling, estimating tropical biomass remains a challenge. Recent work suggests that integrating satellite measurements of canopy height within ecosystem models is a promising approach to infer biomass. We tested the feasibility of this approach to retrieve aboveground biomass (AGB) at three tropical forest sites by assimilating remotely sensed canopy height derived from a texture analysis algorithm applied to the high-resolution Pleiades imager in the Organizing Carbon and Hydrology in Dynamic Ecosystems Canopy (ORCHIDEE-CAN) ecosystem model. While mean AGB could be estimated within 10% of AGB derived from census data in average across sites, canopy height derived from Pleiades product was spatially too smooth, thus unable to accurately resolve large height (and biomass) variations within the site considered. The error budget was evaluated in details, and systematic errors related to the ORCHIDEE-CAN structure contribute as a secondary source of error and could be overcome by using improved allometric equations.

  12. Leveraging Machine Learning to Estimate Soil Salinity through Satellite-Based Remote Sensing

    Science.gov (United States)

    Welle, P.; Ravanbakhsh, S.; Póczos, B.; Mauter, M.

    2016-12-01

    Human-induced salinization of agricultural soils is a growing problem which now affects an estimated 76 million hectares and causes billions of dollars of lost agricultural revenues annually. While there are indications that soil salinization is increasing in extent, current assessments of global salinity levels are outdated and rely heavily on expert opinion due to the prohibitive cost of a worldwide sampling campaign. A more practical alternative to field sampling may be earth observation through remote sensing, which takes advantage of the distinct spectral signature of salts in order to estimate soil conductivity. Recent efforts to map salinity using remote sensing have been met with limited success due to tractability issues of managing the computational load associated with large amounts of satellite data. In this study, we use Google Earth Engine to create composite satellite soil datasets, which combine data from multiple sources and sensors. These composite datasets contain pixel-level surface reflectance values for dates in which the algorithm is most confident that the surface contains bare soil. We leverage the detailed soil maps created and updated by the United States Geological Survey as label data and apply machine learning regression techniques such as Gaussian processes to learn a smooth mapping from surface reflection to noisy estimates of salinity. We also explore a semi-supervised approach using deep generative convolutional networks to leverage the abundance of unlabeled satellite images in producing better estimates for salinity values where we have relatively fewer measurements across the globe. The general method results in two significant contributions: (1) an algorithm that can be used to predict levels of soil salinity in regions without detailed soil maps and (2) a general framework that serves as an example for how remote sensing can be paired with extensive label data to generate methods for prediction of physical phenomenon.

  13. Antecedent precipitation index determined from CST estimates of rainfall

    Science.gov (United States)

    Martin, David W.

    1992-01-01

    This paper deals with an experimental calculation of a satellite-based antecedent precipitation index (API). The index is also derived from daily rain images produced from infrared images using an improved version of GSFC's Convective/Stratiform Technique (CST). API is a measure of soil moisture, and is based on the notion that the amount of moisture in the soil at a given time is related to precipitation at earlier times. Four different CST programs as well as the Geostationary Operational Enviroment Satellite (GOES) Precipitation Index developed by Arkin in 1979 are compared to experimental results, for the Mississippi Valley during the month of July. Rain images are shown for the best CST code and the ARK program. Comparisons are made as to the accuracy and detail of the results for the two codes. This project demonstrates the feasibility of running the CST on a synoptic scale. The Mississippi Valley case is well suited for testing the feasibility of monitoring soil moisture by means of CST. Preliminary comparisons of CST and ARK indicate significant differences in estimates of rain amount and distribution.

  14. Radar-Derived Quantitative Precipitation Estimation Based on Precipitation Classification

    Directory of Open Access Journals (Sweden)

    Lili Yang

    2016-01-01

    Full Text Available A method for improving radar-derived quantitative precipitation estimation is proposed. Tropical vertical profiles of reflectivity (VPRs are first determined from multiple VPRs. Upon identifying a tropical VPR, the event can be further classified as either tropical-stratiform or tropical-convective rainfall by a fuzzy logic (FL algorithm. Based on the precipitation-type fields, the reflectivity values are converted into rainfall rate using a Z-R relationship. In order to evaluate the performance of this rainfall classification scheme, three experiments were conducted using three months of data and two study cases. In Experiment I, the Weather Surveillance Radar-1988 Doppler (WSR-88D default Z-R relationship was applied. In Experiment II, the precipitation regime was separated into convective and stratiform rainfall using the FL algorithm, and corresponding Z-R relationships were used. In Experiment III, the precipitation regime was separated into convective, stratiform, and tropical rainfall, and the corresponding Z-R relationships were applied. The results show that the rainfall rates obtained from all three experiments match closely with the gauge observations, although Experiment II could solve the underestimation, when compared to Experiment I. Experiment III significantly reduced this underestimation and generated the most accurate radar estimates of rain rate among the three experiments.

  15. Satellite-based estimates of surface water dynamics in the Congo River Basin

    Science.gov (United States)

    Becker, M.; Papa, F.; Frappart, F.; Alsdorf, D.; Calmant, S.; da Silva, J. Santos; Prigent, C.; Seyler, F.

    2018-04-01

    In the Congo River Basin (CRB), due to the lack of contemporary in situ observations, there is a limited understanding of the large-scale variability of its present-day hydrologic components and their link with climate. In this context, remote sensing observations provide a unique opportunity to better characterize those dynamics. Analyzing the Global Inundation Extent Multi-Satellite (GIEMS) time series, we first show that surface water extent (SWE) exhibits marked seasonal patterns, well distributed along the major rivers and their tributaries, and with two annual maxima located: i) in the lakes region of the Lwalaba sub-basin and ii) in the "Cuvette Centrale", including Tumba and Mai-Ndombe Lakes. At an interannual time scale, we show that SWE variability is influenced by ENSO and the Indian Ocean dipole events. We then estimate water level maps and surface water storage (SWS) in floodplains, lakes, rivers and wetlands of the CRB, over the period 2003-2007, using a multi-satellite approach, which combines the GIEMS dataset with the water level measurements derived from the ENVISAT altimeter heights. The mean annual variation in SWS in the CRB is 81 ± 24 km3 and contributes to 19 ± 5% of the annual variations of GRACE-derived terrestrial water storage (33 ± 7% in the Middle Congo). It represents also ∼6 ± 2% of the annual water volume that flows from the Congo River into the Atlantic Ocean.

  16. A New Temperature-Vegetation Triangle Algorithm with Variable Edges (TAVE for Satellite-Based Actual Evapotranspiration Estimation

    Directory of Open Access Journals (Sweden)

    Hua Zhang

    2016-09-01

    Full Text Available The estimation of spatially-variable actual evapotranspiration (AET is a critical challenge to regional water resources management. We propose a new remote sensing method, the Triangle Algorithm with Variable Edges (TAVE, to generate daily AET estimates based on satellite-derived land surface temperature and the vegetation index NDVI. The TAVE captures heterogeneity in AET across elevation zones and permits variability in determining local values of wet and dry end-member classes (known as edges. Compared to traditional triangle methods, TAVE introduces three unique features: (i the discretization of the domain as overlapping elevation zones; (ii a variable wet edge that is a function of elevation zone; and (iii variable values of a combined-effect parameter (that accounts for aerodynamic and surface resistance, vapor pressure gradient, and soil moisture availability along both wet and dry edges. With these features, TAVE effectively addresses the combined influence of terrain and water stress on semi-arid environment AET estimates. We demonstrate the effectiveness of this method in one of the driest countries in the world—Jordan, and compare it to a traditional triangle method (TA and a global AET product (MOD16 over different land use types. In irrigated agricultural lands, TAVE matched the results of the single crop coefficient model (−3%, in contrast to substantial overestimation by TA (+234% and underestimation by MOD16 (−50%. In forested (non-irrigated, water consuming regions, TA and MOD16 produced AET average deviations 15.5 times and −3.5 times of those based on TAVE. As TAVE has a simple structure and low data requirements, it provides an efficient means to satisfy the increasing need for evapotranspiration estimation in data-scarce semi-arid regions. This study constitutes a much needed step towards the satellite-based quantification of agricultural water consumption in Jordan.

  17. A Comparison of Two Above-Ground Biomass Estimation Techniques Integrating Satellite-Based Remotely Sensed Data and Ground Data for Tropical and Semiarid Forests in Puerto Rico

    Science.gov (United States)

    Two above-ground forest biomass estimation techniques were evaluated for the United States Territory of Puerto Rico using predictor variables acquired from satellite based remotely sensed data and ground data from the U.S. Department of Agriculture Forest Inventory Analysis (FIA)...

  18. Precipitation Estimation Using L-Band and C-Band Soil Moisture Retrievals

    Science.gov (United States)

    Koster, Randal D.; Brocca, Luca; Crow, Wade T.; Burgin, Mariko S.; De Lannoy, Gabrielle J. M.

    2016-01-01

    An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge-based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L-band SMAP and SMOS data sets is higher than that obtained with the C-band product, as might be expected given that L-band is sensitive to a thicker layer of soil and thereby provides more information on the response of soil moisture to precipitation. The square of the correlation coefficient between the SMAP-based precipitation estimates and the observations (for aggregations to approximately100 km and 5 days) is on average about 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor soil moisture thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.

  19. Understanding satellite-based monthly-to-seasonal reservoir outflow estimation as a function of hydrologic controls

    Science.gov (United States)

    Bonnema, Matthew; Sikder, Safat; Miao, Yabin; Chen, Xiaodong; Hossain, Faisal; Ara Pervin, Ismat; Mahbubur Rahman, S. M.; Lee, Hyongki

    2016-05-01

    Growing population and increased demand for water is causing an increase in dam and reservoir construction in developing nations. When rivers cross international boundaries, the downstream stakeholders often have little knowledge of upstream reservoir operation practices. Satellite remote sensing in the form of radar altimetry and multisensor precipitation products can be used as a practical way to provide downstream stakeholders with the fundamentally elusive upstream information on reservoir outflow needed to make important and proactive water management decisions. This study uses a mass balance approach of three hydrologic controls to estimate reservoir outflow from satellite data at monthly and annual time scales: precipitation-induced inflow, evaporation, and reservoir storage change. Furthermore, this study explores the importance of each of these hydrologic controls to the accuracy of outflow estimation. The hydrologic controls found to be unimportant could potentially be neglected from similar future studies. Two reservoirs were examined in contrasting regions of the world, the Hungry Horse Reservoir in a mountainous region in northwest U.S. and the Kaptai Reservoir in a low-lying, forested region of Bangladesh. It was found that this mass balance method estimated the annual outflow of both reservoirs with reasonable skill. The estimation of monthly outflow from both reservoirs was however less accurate. The Kaptai basin exhibited a shift in basin behavior resulting in variable accuracy across the 9 year study period. Monthly outflow estimation from Hungry Horse Reservoir was compounded by snow accumulation and melt processes, reflected by relatively low accuracy in summer and fall, when snow processes control runoff. Furthermore, it was found that the important hydrologic controls for reservoir outflow estimation at the monthly time scale differs between the two reservoirs, with precipitation-induced inflow being the most important control for the Kaptai

  20. Global Precipitation Measurement (GPM) Core Observatory Falling Snow Estimates

    Science.gov (United States)

    Skofronick Jackson, G.; Kulie, M.; Milani, L.; Munchak, S. J.; Wood, N.; Levizzani, V.

    2017-12-01

    Retrievals of falling snow from space represent an important data set for understanding and linking the Earth's atmospheric, hydrological, and energy cycles. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. This work focuses on comparing the first stable falling snow retrieval products (released May 2017) for the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO), which was launched February 2014, and carries both an active dual frequency (Ku- and Ka-band) precipitation radar (DPR) and a passive microwave radiometer (GPM Microwave Imager-GMI). Five separate GPM-CO falling snow retrieval algorithm products are analyzed including those from DPR Matched (Ka+Ku) Scan, DPR Normal Scan (Ku), DPR High Sensitivity Scan (Ka), combined DPR+GMI, and GMI. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new, the different on-orbit instruments don't capture all snow rates equally, and retrieval algorithms differ. Thus a detailed comparison among the GPM-CO products elucidates advantages and disadvantages of the retrievals. GPM and CloudSat global snowfall evaluation exercises are natural investigative pathways to explore, but caution must be undertaken when analyzing these datasets for comparative purposes. This work includes outlining the challenges associated with comparing GPM-CO to CloudSat satellite snow estimates due to the different sampling, algorithms, and instrument capabilities. We will highlight some factors and assumptions that can be altered or statistically normalized and applied in an effort to make comparisons between GPM and CloudSat global satellite falling snow products as equitable as possible.

  1. Satellite precipitation estimation over the Tibetan Plateau

    Science.gov (United States)

    Porcu, F.; Gjoka, U.

    2012-04-01

    Precipitation characteristics over the Tibetan Plateau are very little known, given the scarcity of reliable and widely distributed ground observation, thus the satellite approach is a valuable choice for large scale precipitation analysis and hydrological cycle studies. However,the satellite perspective undergoes various shortcomings at the different wavelengths used in atmospheric remote sensing. In the microwave spectrum often the high soil emissivity masks or hides the atmospheric signal upwelling from light-moderate precipitation layers, while low and relatively thin precipitating clouds are not well detected in the visible-infrared, because of their low contrast with cold and bright (if snow covered) background. In this work an IR-based, statistical rainfall estimation technique is trained and applied over the Tibetan Plateau hydrological basin to retrive precipitation intensity at different spatial and temporal scales. The technique is based on a simple artificial neural network scheme trained with two supervised training sets assembled for monsoon season and for the rest of the year. For the monsoon season (estimated from June to September), the ground radar precipitation data for few case studies are used to build the training set: four days in summer 2009 are considered. For the rest of the year, CloudSat-CPR derived snowfall rate has been used as reference precipitation data, following the Kulie and Bennartz (2009) algorithm. METEOSAT-7 infrared channels radiance (at 6.7 and 11 micometers) and derived local variability features (such as local standard deviation and local average) are used as input and the actual rainrate is obtained as output for each satellite slot, every 30 minutes on the satellite grid. The satellite rainrate maps for three years (2008-2010) are computed and compared with available global precipitation products (such as C-MORPH and TMPA products) and with other techniques applied to the Plateau area: similarities and differences are

  2. Augmenting Satellite Precipitation Estimation with Lightning Information

    Energy Technology Data Exchange (ETDEWEB)

    Mahrooghy, Majid [Mississippi State University (MSU); Anantharaj, Valentine G [ORNL; Younan, Nicolas H. [Mississippi State University (MSU); Petersen, Walter A. [NASA Marshall Space Flight Center, Huntsville, AL; Hsu, Kuo-Lin [University of California, Irvine; Behrangi, Ali [Jet Propulsion Laboratory, Pasadena, CA; Aanstoos, James [Mississippi State University (MSU)

    2013-01-01

    We have used lightning information to augment the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network - Cloud Classification System (PERSIANN-CCS). Co-located lightning data are used to segregate cloud patches, segmented from GOES-12 infrared data, into either electrified (EL) or non-electrified (NEL) patches. A set of features is extracted separately for the EL and NEL cloud patches. The features for the EL cloud patches include new features based on the lightning information. The cloud patches are classified and clustered using self-organizing maps (SOM). Then brightness temperature and rain rate (T-R) relationships are derived for the different clusters. Rain rates are estimated for the cloud patches based on their representative T-R relationship. The Equitable Threat Score (ETS) for daily precipitation estimates is improved by almost 12% for the winter season. In the summer, no significant improvements in ETS are noted.

  3. Satellite based radar interferometry to estimate large-scale soil water depletion from clay shrinkage: possibilities and limitations

    NARCIS (Netherlands)

    Brake, te B.; Hanssen, R.F.; Ploeg, van der M.J.; Rooij, de G.H.

    2013-01-01

    Satellite-based radar interferometry is a technique capable of measuring small surface elevation changes at large scales and with a high resolution. In vadose zone hydrology, it has been recognized for a long time that surface elevation changes due to swell and shrinkage of clayey soils can serve as

  4. Estimation of snowpack matching ground-truth data and MODIS satellite-based observations by using regression kriging

    Science.gov (United States)

    Juan Collados-Lara, Antonio; Pardo-Iguzquiza, Eulogio; Pulido-Velazquez, David

    2016-04-01

    The estimation of Snow Water Equivalent (SWE) is essential for an appropriate assessment of the available water resources in Alpine catchment. The hydrologic regime in these areas is dominated by the storage of water in the snowpack, which is discharged to rivers throughout the melt season. An accurate estimation of the resources will be necessary for an appropriate analysis of the system operation alternatives using basin scale management models. In order to obtain an appropriate estimation of the SWE we need to know the spatial distribution snowpack and snow density within the Snow Cover Area (SCA). Data for these snow variables can be extracted from in-situ point measurements and air-borne/space-borne remote sensing observations. Different interpolation and simulation techniques have been employed for the estimation of the cited variables. In this paper we propose to estimate snowpack from a reduced number of ground-truth data (1 or 2 campaigns per year with 23 observation point from 2000-2014) and MODIS satellite-based observations in the Sierra Nevada Mountain (Southern Spain). Regression based methodologies has been used to study snowpack distribution using different kind of explicative variables: geographic, topographic, climatic. 40 explicative variables were considered: the longitude, latitude, altitude, slope, eastness, northness, radiation, maximum upwind slope and some mathematical transformation of each of them [Ln(v), (v)^-1; (v)^2; (v)^0.5). Eight different structure of regression models have been tested (combining 1, 2, 3 or 4 explicative variables). Y=B0+B1Xi (1); Y=B0+B1XiXj (2); Y=B0+B1Xi+B2Xj (3); Y=B0+B1Xi+B2XjXl (4); Y=B0+B1XiXk+B2XjXl (5); Y=B0+B1Xi+B2Xj+B3Xl (6); Y=B0+B1Xi+B2Xj+B3XlXk (7); Y=B0+B1Xi+B2Xj+B3Xl+B4Xk (8). Where: Y is the snow depth; (Xi, Xj, Xl, Xk) are the prediction variables (any of the 40 variables); (B0, B1, B2, B3) are the coefficients to be estimated. The ground data are employed to calibrate the multiple regressions. In

  5. Global Precipitation Measurement. Report 7; Bridging from TRMM to GPM to 3-Hourly Precipitation Estimates

    Science.gov (United States)

    Shepherd, J. Marshall; Smith, Eric A.; Adams, W. James (Editor)

    2002-01-01

    Historically, multi-decadal measurements of precipitation from surface-based rain gauges have been available over continents. However oceans remained largely unobserved prior to the beginning of the satellite era. Only after the launch of the first Defense Meteorological Satellite Program (DMSP) satellite in 1987 carrying a well-calibrated and multi-frequency passive microwave radiometer called Special Sensor Microwave/Imager (SSM/I) have systematic and accurate precipitation measurements over oceans become available on a regular basis; see Smith et al. (1994, 1998). Recognizing that satellite-based data are a foremost tool for measuring precipitation, NASA initiated a new research program to measure precipitation from space under its Mission to Planet Earth program in the 1990s. As a result, the Tropical Rainfall Measuring Mission (TRMM), a collaborative mission between NASA and NASDA, was launched in 1997 to measure tropical and subtropical rain. See Simpson et al. (1996) and Kummerow et al. (2000). Motivated by the success of TRMM, and recognizing the need for more comprehensive global precipitation measurements, NASA and NASDA have now planned a new mission, i.e., the Global Precipitation Measurement (GPM) mission. The primary goal of GPM is to extend TRMM's rainfall time series while making substantial improvements in precipitation observations, specifically in terms of measurement accuracy, sampling frequency, Earth coverage, and spatial resolution. This report addresses four fundamental questions related to the transition from current to future global precipitation observations as denoted by the TRMM and GPM eras, respectively.

  6. Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

    Science.gov (United States)

    Li, Zhengpeng; Liu, Shuguang; Tan, Zhengxi; Bliss, Norman B.; Young, Claudia J.; West, Tristram O.; Ogle, Stephen M.

    2014-01-01

    Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m−2 yr−1and total NPP in the range of 318–490 Tg C yr−1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m−2 yr−1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m−2 yr−1. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. We suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.

  7. Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm

    Directory of Open Access Journals (Sweden)

    Yuxiang He

    2018-01-01

    Full Text Available This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE that would objectively blend real-time satellite quantitative precipitation estimates (SQPE with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a five-year period between 2003–2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS (National Weather Service operations over two regions: (I Inside radar effective coverage and (II immediately outside radar coverage. The outcomes of the evaluation indicate (a ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and (b blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

  8. Tree Canopy Light Interception Estimates in Almond and a Walnut Orchards Using Ground, Low Flying Aircraft, and Satellite Based Methods to Improve Irrigation Scheduling Programs

    Science.gov (United States)

    Rosecrance, Richard C.; Johnson, Lee; Soderstrom, Dominic

    2016-01-01

    Canopy light interception is a main driver of water use and crop yield in almond and walnut production. Fractional green canopy cover (Fc) is a good indicator of light interception and can be estimated remotely from satellite using the normalized difference vegetation index (NDVI) data. Satellite-based Fc estimates could be used to inform crop evapotranspiration models, and hence support improvements in irrigation evaluation and management capabilities. Satellite estimates of Fc in almond and walnut orchards, however, need to be verified before incorporating them into irrigation scheduling or other crop water management programs. In this study, Landsat-based NDVI and Fc from NASA's Satellite Irrigation Management Support (SIMS) were compared with four estimates of canopy cover: 1. light bar measurement, 2. in-situ and image-based dimensional tree-crown analyses, 3. high-resolution NDVI data from low flying aircraft, and 4. orchard photos obtained via Google Earth and processed by an Image J thresholding routine. Correlations between the various estimates are discussed.

  9. Do agrometeorological data improve optical satellite-based estimations of the herbaceous yield in Sahelian semi-arid ecosystems?

    DEFF Research Database (Denmark)

    Diouf, Abdoul Aziz; Hiernaux, Pierre; Brandt, Martin Stefan

    2016-01-01

    evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483...... kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase...

  10. Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island

    Directory of Open Access Journals (Sweden)

    Julien Morel

    2014-07-01

    Full Text Available Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1 an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2 the Kumar-Monteith efficiency model, and (3 a forced-coupling method with a sugarcane crop model (MOSICAS and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.4 t∙ha−1. Because this method is also the simplest to set up and requires less input data, it appears that it is the most suitable for performing operational estimations and forecasts of sugarcane yield at the field scale. The main limitation is the acquisition of a minimum of five satellite images. The upcoming open-access Sentinel-2 Earth observation system should overcome this limitation because it will provide 10-m resolution satellite images with a 5-day frequency.

  11. The AMSR2 Satellite-based Microwave Snow Algorithm (SMSA) to estimate regional to global snow depth and snow water equivalent

    Science.gov (United States)

    Kelly, R. E. J.; Saberi, N.; Li, Q.

    2017-12-01

    With moderate to high spatial resolution (observation approaches yet to be fully scoped and developed, the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach is described for estimating snow depth (SD) and snow water equivalent (SWE). The algorithm, called the Satellite-based Microwave Snow Algorithm (SMSA), uses Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations aboard the Global Change Observation Mission - Water mission launched by the Japan Aerospace Exploration Agency in 2012. The approach is unique since it leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval without requiring parameter constraints from in situ snow depth observations or historical snow depth climatology. After screening snow from non-snow surface targets (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]), moderate and shallow snow depths are estimated by minimizing the difference between Dense Media Radiative Transfer model estimates (Tsang et al., 2000; Picard et al., 2011) and AMSR2 Tb observations to retrieve SWE and SD. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2016-17 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates and approach the performance of the model assimilation-based approach of GlobSnow. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides SWE estimates that are independent of real or near real

  12. Simulation of the Impact of New Aircraft- and Satellite-based Ocean Surface Wind Measurements on Estimates of Hurricane Intensity

    Science.gov (United States)

    Uhlhorn, Eric; Atlas, Robert; Black, Peter; Buckley, Courtney; Chen, Shuyi; El-Nimri, Salem; Hood, Robbie; Johnson, James; Jones, Linwood; Miller, Timothy; hide

    2009-01-01

    The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor currently under development to enhance real-time hurricane ocean surface wind observations. HIRAD builds on the capabilities of the Stepped Frequency Microwave Radiometer (SFMR), which now operates on NOAA P-3, G-4, and AFRC C-130 aircraft. Unlike the SFMR, which measures wind speed and rain rate along the ground track directly beneath the aircraft, HIRAD will provide images of the surface wind and rain field over a wide swath (approximately 3 times the aircraft altitude). To demonstrate potential improvement in the measurement of peak hurricane winds, we present a set of Observing System Simulation Experiments (OSSEs) in which measurements from the new instrument as well as those from existing platforms (air, surface, and space-based) are simulated from the output of a high-resolution (approximately 1.7 km) numerical model. Simulated retrieval errors due to both instrument noise as well as model function accuracy are considered over the expected range of incidence angles, wind speeds and rain rates. Based on numerous simulated flight patterns and data source combinations, statistics are developed to describe relationships between the observed and true (from the model s perspective) peak wind speed. These results have implications for improving the estimation of hurricane intensity (as defined by the peak sustained wind anywhere in the storm), which may often go un-observed due to sampling limitations.

  13. Analysis of Groundwater Anomalies Estimated by GRACE and GLDAS Satellite-based Hydrological Model in the Gulf of Mexico

    Science.gov (United States)

    Lotfata, A.; Ambinakudige, S.

    2017-12-01

    Coastal regions face a higher risk of flooding. A rise in sea-level increases flooding chances in low-lying areas. A major concern is the effect of sea-level rise on the depth of the fresh water/salt water interface in the aquifers of the coastal regions. A sea-level change rise impacts the hydrological system of the aquifers. Salt water intrusion into fresh water aquifers increase water table levels. Flooding prone areas in the coast are at a higher risk of salt water intrusion. The Gulf coast is one of the most vulnerable flood areas due to its natural weather patterns. There is not yet a local assessment of the relation between groundwater level and sea-level rising. This study investigates the projected sea-level rise models and the anomalous groundwater level during January 2002 to December 2016. We used the NASA Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) satellite data in the analysis. We accounted the leakage error and the measurement error in GRACE data. GLDAS data was used to calculate the groundwater storage from the total water storage estimated using GRACE data (ΔGW=ΔTWS (soil moisture, surface water, groundwater, and canopy water) - ΔGLDAS (soil moisture, surface water, and canopy water)). The preliminary results indicate that the total water storage is increasing in parts of the Gulf of Mexico. GRACE data show high soil wetness and groundwater levels in Mississippi, Alabama and Texas coasts. Because sea-level rise increases the probability of flooding in the Gulf coast and affects the groundwater, we will analyze probable interactions between sea-level rise and groundwater in the study area. To understand regional sea-level rise patterns, we will investigate GRACE Ocean data along the Gulf coasts. We will quantify ocean total water storage, its salinity, and its relationship with the groundwater level variations in the Gulf coast.

  14. The development of pan-African food forecasting and the exploration of satellite-based precipitation estimates

    NARCIS (Netherlands)

    Thiemig, Vera

    2014-01-01

    The main objective of this PhD is to contribute to the development of a pan-African flood forecasting system in order to enhance flood forecasting for the whole of Africa. In view of the dimension and complexity of this goal, this research focused on particular aspects of flood forecasting,

  15. Opportunities and challenges for evaluating precipitation estimates during GPM mission

    Energy Technology Data Exchange (ETDEWEB)

    Amitai, E. [George Mason Univ. and NASA Goddard Space Flight Center, Greenbelt, MD (United States); NASA Goddard Space Flight Center, Greenbelt, MD (United States); Llort, X.; Sempere-Torres, D. [GRAHI/Univ. Politecnica de Catalunya, Barcelona (Spain)

    2006-10-15

    Data assimilation in conjunction with numerical weather prediction and a variety of hydrologic applications now depend on satellite observations of precipitation. However, providing values of precipitation is not sufficient unless they are accompanied by the associated uncertainty estimates. The main approach of quantifying satellite precipitation uncertainties generally requires establishment of reliable uncertainty estimates for the ground validation rainfall products. This paper discusses several of the relevant validation concepts evolving from the tropical rainfall measuring mission (TRMM) era to the global precipitation measurement mission (GPM) era in the context of determining and reducing uncertainties of ground and space-based radar rainfall estimates. From comparisons of probability distribution functions of rain rates derived from TRMM precipitation radar and co-located ground based radar data - using the new NASA TRMM radar rainfall products (version 6) - this paper provides (1) a brief review of the importance of comparing pdfs of rain rate for statistical and physical verification of space-borne radar estimates of precipitation; (2) a brief review of how well the ground validation estimates compare to the TRMM radar retrieved estimates; and (3) discussion on opportunities and challenges to determine and reduce the uncertainties in space-based and ground-based radar estimates of rain rate distributions. (orig.)

  16. Comparing NEXRAD Operational Precipitation Estimates and Raingage Observations of Intense Precipitation in the Missouri River Basin.

    Science.gov (United States)

    Young, C. B.

    2002-05-01

    Accurate observation of precipitation is critical to the study and modeling of land surface hydrologic processes. NEXRAD radar-based precipitation estimates are increasingly used in field experiments, hydrologic modeling, and water and energy budget studies due to their high spatial and temporal resolution, national coverage, and perceived accuracy. Extensive development and testing of NEXRAD precipitation algorithms have been carried out in the Southern Plains. Previous studies (Young et al. 2000, Young et al. 1999, Smith et al. 1996) indicate that NEXRAD operational products tend to underestimate precipitation at light rain rates. This study investigates the performance of NEXRAD precipitation estimates of high-intensity rainfall, focusing on flood-producing storms in the Missouri River Basin. NEXRAD estimates for these storms are compared with data from multiple raingage networks, including NWS recording and non-recording gages and ALERT raingage data for the Kansas City metropolitan area. Analyses include comparisons of gage and radar data at a wide range of temporal and spatial scales. Particular attention is paid to the October 4th, 1998, storm that produced severe flooding in Kansas City. NOTE: The phrase `NEXRAD operational products' in this abstract includes precipitation estimates generated using the Stage III and P1 algorithms. Both of these products estimate hourly accumulations on the (approximately) 4 km HRAP grid.

  17. A test for Improvement of high resolution Quantitative Precipitation Estimation for localized heavy precipitation events

    Science.gov (United States)

    Lee, Jung-Hoon; Roh, Joon-Woo; Park, Jeong-Gyun

    2017-04-01

    Accurate estimation of precipitation is one of the most difficult and significant tasks in the area of weather diagnostic and forecasting. In the Korean Peninsula, heavy precipitations are caused by various physical mechanisms, which are affected by shortwave trough, quasi-stationary moisture convergence zone among varying air masses, and a direct/indirect effect of tropical cyclone. In addition to, various geographical and topographical elements make production of temporal and spatial distribution of precipitation is very complicated. Especially, localized heavy rainfall events in South Korea generally arise from mesoscale convective systems embedded in these synoptic scale disturbances. In weather radar data with high temporal and spatial resolution, accurate estimation of rain rate from radar reflectivity data is too difficult. Z-R relationship (Marshal and Palmer 1948) have adapted representatively. In addition to, several methods such as support vector machine (SVM), neural network, Fuzzy logic, Kriging were utilized in order to improve the accuracy of rain rate. These methods show the different quantitative precipitation estimation (QPE) and the performances of accuracy are different for heavy precipitation cases. In this study, in order to improve the accuracy of QPE for localized heavy precipitation, ensemble method for Z-R relationship and various techniques was tested. This QPE ensemble method was developed by a concept based on utilizing each advantage of precipitation calibration methods. The ensemble members were produced for a combination of different Z-R coefficient and calibration method.

  18. Improving multisensor estimation of heavy-to-extreme precipitation via conditional bias-penalized optimal estimation

    Science.gov (United States)

    Kim, Beomgeun; Seo, Dong-Jun; Noh, Seong Jin; Prat, Olivier P.; Nelson, Brian R.

    2018-01-01

    A new technique for merging radar precipitation estimates and rain gauge data is developed and evaluated to improve multisensor quantitative precipitation estimation (QPE), in particular, of heavy-to-extreme precipitation. Unlike the conventional cokriging methods which are susceptible to conditional bias (CB), the proposed technique, referred to herein as conditional bias-penalized cokriging (CBPCK), explicitly minimizes Type-II CB for improved quantitative estimation of heavy-to-extreme precipitation. CBPCK is a bivariate version of extended conditional bias-penalized kriging (ECBPK) developed for gauge-only analysis. To evaluate CBPCK, cross validation and visual examination are carried out using multi-year hourly radar and gauge data in the North Central Texas region in which CBPCK is compared with the variant of the ordinary cokriging (OCK) algorithm used operationally in the National Weather Service Multisensor Precipitation Estimator. The results show that CBPCK significantly reduces Type-II CB for estimation of heavy-to-extreme precipitation, and that the margin of improvement over OCK is larger in areas of higher fractional coverage (FC) of precipitation. When FC > 0.9 and hourly gauge precipitation is > 60 mm, the reduction in root mean squared error (RMSE) by CBPCK over radar-only (RO) is about 12 mm while the reduction in RMSE by OCK over RO is about 7 mm. CBPCK may be used in real-time analysis or in reanalysis of multisensor precipitation for which accurate estimation of heavy-to-extreme precipitation is of particular importance.

  19. Improving precipitation estimates over the western United States using GOES-R precipitation data

    Science.gov (United States)

    Karbalaee, N.; Kirstetter, P. E.; Gourley, J. J.

    2017-12-01

    Satellite remote sensing data with fine spatial and temporal resolution are widely used for precipitation estimation for different applications such as hydrological modeling, storm prediction, and flash flood monitoring. The Geostationary Operational Environmental Satellites-R series (GOES-R) is the next generation of environmental satellites that provides hydrologic, atmospheric, and climatic information every 30 seconds over the western hemisphere. The high-resolution and low-latency of GOES-R observations is essential for the monitoring and prediction of floods, specifically in the Western United States where the vantage point of space can complement the degraded weather radar coverage of the NEXRAD network. The GOES-R rainfall rate algorithm will yield deterministic quantitative precipitation estimates (QPE). Accounting for inherent uncertainties will further advance the GOES-R QPEs since with quantifiable error bars, the rainfall estimates can be more readily fused with ground radar products. On the ground, the high-resolution NEXRAD-based precipitation estimation from the Multi-Radar/Multi-Sensor (MRMS) system, which is now operational in the National Weather Service (NWS), is challenged due to a lack of suitable coverage of operational weather radars over complex terrain. Distribution of QPE uncertainties associated with the GOES-R deterministic retrievals are derived and analyzed using MRMS over regions with good radar coverage. They will be merged with MRMS-based probabilistic QPEs developed to advance multisensor QPE integration. This research aims at improving precipitation estimation over the CONUS by combining the observations from GOES-R and MRMS to provide consistent, accurate and fine resolution precipitation rates with uncertainties over the CONUS.

  20. Estimation of precipitable water from surface dew point temperature

    International Nuclear Information System (INIS)

    Abdel Wahab, M.; Sharif, T.A.

    1991-09-01

    The Reitan (1963) regression equation which is of the form lnw=a+bT d has been examined and tested to estimate precipitable water content from surface dew point temperature at different locations. The study confirms that the slope of this equation (b) remains constant at the value of .0681 deg. C., while the intercept (a) changes rapidly with the latitude. The use of the variable intercept can improve the estimated result by 2%. (author). 6 refs, 4 figs, 3 tabs

  1. Pareto-Optimal Estimates of California Precipitation Change

    Science.gov (United States)

    Langenbrunner, Baird; Neelin, J. David

    2017-12-01

    In seeking constraints on global climate model projections under global warming, one commonly finds that different subsets of models perform well under different objective functions, and these trade-offs are difficult to weigh. Here a multiobjective approach is applied to a large set of subensembles generated from the Climate Model Intercomparison Project phase 5 ensemble. We use observations and reanalyses to constrain tropical Pacific sea surface temperatures, upper level zonal winds in the midlatitude Pacific, and California precipitation. An evolutionary algorithm identifies the set of Pareto-optimal subensembles across these three measures, and these subensembles are used to constrain end-of-century California wet season precipitation change. This methodology narrows the range of projections throughout California, increasing confidence in estimates of positive mean precipitation change. Finally, we show how this technique complements and generalizes emergent constraint approaches for restricting uncertainty in end-of-century projections within multimodel ensembles using multiple criteria for observational constraints.

  2. Statistical evaluation of the performance of gridded monthly precipitation products from reanalysis data, satellite estimates, and merged analyses over China

    Science.gov (United States)

    Deng, Xueliang; Nie, Suping; Deng, Weitao; Cao, Weihua

    2018-04-01

    In this study, we compared the following four different gridded monthly precipitation products: the National Centers for Environmental Prediction version 2 (NCEP-2) reanalysis data, the satellite-based Climate Prediction Center Morphing technique (CMORPH) data, the merged satellite-gauge Global Precipitation Climatology Project (GPCP) data, and the merged satellite-gauge-model data from the Beijing Climate Center Merged Estimation of Precipitation (BMEP). We evaluated the performances of these products using monthly precipitation observations spanning the period of January 2003 to December 2013 from a dense, national, rain gauge network in China. Our assessment involved several statistical techniques, including spatial pattern, temporal variation, bias, root-mean-square error (RMSE), and correlation coefficient (CC) analysis. The results show that NCEP-2, GPCP, and BMEP generally overestimate monthly precipitation at the national scale and CMORPH underestimates it. However, all of the datasets successfully characterized the northwest to southeast increase in the monthly precipitation over China. Because they include precipitation gauge information from the Global Telecommunication System (GTS) network, GPCP and BMEP have much smaller biases, lower RMSEs, and higher CCs than NCEP-2 and CMORPH. When the seasonal and regional variations are considered, NCEP-2 has a larger error over southern China during the summer. CMORPH poorly reproduces the magnitude of the precipitation over southeastern China and the temporal correlation over western and northwestern China during all seasons. BMEP has a lower RMSE and higher CC than GPCP over eastern and southern China, where the station network is dense. In contrast, BMEP has a lower CC than GPCP over western and northwestern China, where the gauge network is relatively sparse.

  3. Pareto-optimal estimates that constrain mean California precipitation change

    Science.gov (United States)

    Langenbrunner, B.; Neelin, J. D.

    2017-12-01

    Global climate model (GCM) projections of greenhouse gas-induced precipitation change can exhibit notable uncertainty at the regional scale, particularly in regions where the mean change is small compared to internal variability. This is especially true for California, which is located in a transition zone between robust precipitation increases to the north and decreases to the south, and where GCMs from the Climate Model Intercomparison Project phase 5 (CMIP5) archive show no consensus on mean change (in either magnitude or sign) across the central and southern parts of the state. With the goal of constraining this uncertainty, we apply a multiobjective approach to a large set of subensembles (subsets of models from the full CMIP5 ensemble). These constraints are based on subensemble performance in three fields important to California precipitation: tropical Pacific sea surface temperatures, upper-level zonal winds in the midlatitude Pacific, and precipitation over the state. An evolutionary algorithm is used to sort through and identify the set of Pareto-optimal subensembles across these three measures in the historical climatology, and we use this information to constrain end-of-century California wet season precipitation change. This technique narrows the range of projections throughout the state and increases confidence in estimates of positive mean change. Furthermore, these methods complement and generalize emergent constraint approaches that aim to restrict uncertainty in end-of-century projections, and they have applications to even broader aspects of uncertainty quantification, including parameter sensitivity and model calibration.

  4. Estimation of the characteristic energy of electron precipitation

    Directory of Open Access Journals (Sweden)

    C. F. del Pozo

    2002-09-01

    Full Text Available Data from simultaneous observations (on 13 February 1996, 9 November 1998, and 12 February 1999 with the IRIS, DASI and EISCAT systems are employed in the study of the energy distribution of the electron precipitation during substorm activity. The estimation of the characteristic energy of the electron precipitation over the common field of view of IRIS and DASI is discussed. In particular, we look closely at the physical basis of the correspondence between the characteristic energy, the flux-averaged energy, as defined below, and the logarithm of the ratio of the green-light intensity to the square of absorption. This study expands and corrects results presented in the paper by Kosch et al. (2001. It is noticed, moreover, that acceleration associated with diffusion processes in the magnetosphere long before precipitation may be controlling the shape of the energy spectrum. We propose and test a "mixed" distribution for the energy-flux spectrum, exponential at the lower energies and Maxwellian or modified power-law at the higher energies, with a threshold energy separating these two regimes. The energy-flux spectrum at Tromsø, in the 1–320 keV range, is derived from EISCAT electron density profiles in the 70–140 km altitude range and is applied in the "calibration" of the optical intensity and absorption distributions, in order to extrapolate the flux and characteristic energy maps.Key words. Ionosphere (auroral ionosphere; particle precipitation; particle acceleration

  5. Estimation of the characteristic energy of electron precipitation

    Directory of Open Access Journals (Sweden)

    C. F. del Pozo

    Full Text Available Data from simultaneous observations (on 13 February 1996, 9 November 1998, and 12 February 1999 with the IRIS, DASI and EISCAT systems are employed in the study of the energy distribution of the electron precipitation during substorm activity. The estimation of the characteristic energy of the electron precipitation over the common field of view of IRIS and DASI is discussed. In particular, we look closely at the physical basis of the correspondence between the characteristic energy, the flux-averaged energy, as defined below, and the logarithm of the ratio of the green-light intensity to the square of absorption. This study expands and corrects results presented in the paper by Kosch et al. (2001. It is noticed, moreover, that acceleration associated with diffusion processes in the magnetosphere long before precipitation may be controlling the shape of the energy spectrum. We propose and test a "mixed" distribution for the energy-flux spectrum, exponential at the lower energies and Maxwellian or modified power-law at the higher energies, with a threshold energy separating these two regimes. The energy-flux spectrum at Tromsø, in the 1–320 keV range, is derived from EISCAT electron density profiles in the 70–140 km altitude range and is applied in the "calibration" of the optical intensity and absorption distributions, in order to extrapolate the flux and characteristic energy maps.

    Key words. Ionosphere (auroral ionosphere; particle precipitation; particle acceleration

  6. Uncertainty Estimation using Bootstrapped Kriging Predictions for Precipitation Isoscapes

    Science.gov (United States)

    Ma, C.; Bowen, G. J.; Vander Zanden, H.; Wunder, M.

    2017-12-01

    Isoscapes are spatial models representing the distribution of stable isotope values across landscapes. Isoscapes of hydrogen and oxygen in precipitation are now widely used in a diversity of fields, including geology, biology, hydrology, and atmospheric science. To generate isoscapes, geostatistical methods are typically applied to extend predictions from limited data measurements. Kriging is a popular method in isoscape modeling, but quantifying the uncertainty associated with the resulting isoscapes is challenging. Applications that use precipitation isoscapes to determine sample origin require estimation of uncertainty. Here we present a simple bootstrap method (SBM) to estimate the mean and uncertainty of the krigged isoscape and compare these results with a generalized bootstrap method (GBM) applied in previous studies. We used hydrogen isotopic data from IsoMAP to explore these two approaches for estimating uncertainty. We conducted 10 simulations for each bootstrap method and found that SBM results in more kriging predictions (9/10) compared to GBM (4/10). Prediction from SBM was closer to the original prediction generated without bootstrapping and had less variance than GBM. SBM was tested on different datasets from IsoMAP with different numbers of observation sites. We determined that predictions from the datasets with fewer than 40 observation sites using SBM were more variable than the original prediction. The approaches we used for estimating uncertainty will be compiled in an R package that is under development. We expect that these robust estimates of precipitation isoscape uncertainty can be applied in diagnosing the origin of samples ranging from various type of waters to migratory animals, food products, and humans.

  7. The estimation of probable maximum precipitation: the case of Catalonia.

    Science.gov (United States)

    Casas, M Carmen; Rodríguez, Raül; Nieto, Raquel; Redaño, Angel

    2008-12-01

    A brief overview of the different techniques used to estimate the probable maximum precipitation (PMP) is presented. As a particular case, the 1-day PMP over Catalonia has been calculated and mapped with a high spatial resolution. For this purpose, the annual maximum daily rainfall series from 145 pluviometric stations of the Instituto Nacional de Meteorología (Spanish Weather Service) in Catalonia have been analyzed. In order to obtain values of PMP, an enveloping frequency factor curve based on the actual rainfall data of stations in the region has been developed. This enveloping curve has been used to estimate 1-day PMP values of all the 145 stations. Applying the Cressman method, the spatial analysis of these values has been achieved. Monthly precipitation climatological data, obtained from the application of Geographic Information Systems techniques, have been used as the initial field for the analysis. The 1-day PMP at 1 km(2) spatial resolution over Catalonia has been objectively determined, varying from 200 to 550 mm. Structures with wavelength longer than approximately 35 km can be identified and, despite their general concordance, the obtained 1-day PMP spatial distribution shows remarkable differences compared to the annual mean precipitation arrangement over Catalonia.

  8. GLUE Based Uncertainty Estimation of Urban Drainage Modeling Using Weather Radar Precipitation Estimates

    DEFF Research Database (Denmark)

    Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.

    2011-01-01

    Distributed weather radar precipitation measurements are used as rainfall input for an urban drainage model, to simulate the runoff from a small catchment of Denmark. It is demonstrated how the Generalized Likelihood Uncertainty Estimation (GLUE) methodology can be implemented and used to estimate...

  9. Improving Frozen Precipitation Density Estimation in Land Surface Modeling

    Science.gov (United States)

    Sparrow, K.; Fall, G. M.

    2017-12-01

    The Office of Water Prediction (OWP) produces high-value water supply and flood risk planning information through the use of operational land surface modeling. Improvements in diagnosing frozen precipitation density will benefit the NWS's meteorological and hydrological services by refining estimates of a significant and vital input into land surface models. A current common practice for handling the density of snow accumulation in a land surface model is to use a standard 10:1 snow-to-liquid-equivalent ratio (SLR). Our research findings suggest the possibility of a more skillful approach for assessing the spatial variability of precipitation density. We developed a 30-year SLR climatology for the coterminous US from version 3.22 of the Daily Global Historical Climatology Network - Daily (GHCN-D) dataset. Our methods followed the approach described by Baxter (2005) to estimate mean climatological SLR values at GHCN-D sites in the US, Canada, and Mexico for the years 1986-2015. In addition to the Baxter criteria, the following refinements were made: tests were performed to eliminate SLR outliers and frequent reports of SLR = 10, a linear SLR vs. elevation trend was fitted to station SLR mean values to remove the elevation trend from the data, and detrended SLR residuals were interpolated using ordinary kriging with a spherical semivariogram model. The elevation values of each station were based on the GMTED 2010 digital elevation model and the elevation trend in the data was established via linear least squares approximation. The ordinary kriging procedure was used to interpolate the data into gridded climatological SLR estimates for each calendar month at a 0.125 degree resolution. To assess the skill of this climatology, we compared estimates from our SLR climatology with observations from the GHCN-D dataset to consider the potential use of this climatology as a first guess of frozen precipitation density in an operational land surface model. The difference in

  10. GPS Estimates of Integrated Precipitable Water Aid Weather Forecasters

    Science.gov (United States)

    Moore, Angelyn W.; Gutman, Seth I.; Holub, Kirk; Bock, Yehuda; Danielson, David; Laber, Jayme; Small, Ivory

    2013-01-01

    Global Positioning System (GPS) meteorology provides enhanced density, low-latency (30-min resolution), integrated precipitable water (IPW) estimates to NOAA NWS (National Oceanic and Atmospheric Adminis tration Nat ional Weather Service) Weather Forecast Offices (WFOs) to provide improved model and satellite data verification capability and more accurate forecasts of extreme weather such as flooding. An early activity of this project was to increase the number of stations contributing to the NOAA Earth System Research Laboratory (ESRL) GPS meteorology observing network in Southern California by about 27 stations. Following this, the Los Angeles/Oxnard and San Diego WFOs began using the enhanced GPS-based IPW measurements provided by ESRL in the 2012 and 2013 monsoon seasons. Forecasters found GPS IPW to be an effective tool in evaluating model performance, and in monitoring monsoon development between weather model runs for improved flood forecasting. GPS stations are multi-purpose, and routine processing for position solutions also yields estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV (precipitable water vapor) using in situ pressure and temperature measurements, the basis for GPS meteorology. NOAA ESRL has implemented this concept with a nationwide distribution of more than 300 "GPSMet" stations providing IPW estimates at sub-hourly resolution currently used in operational weather models in the U.S.

  11. Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

    Science.gov (United States)

    Reichle, Rolf H.; Liu, Qing; Bindlish, Rajat; Cosh, Michael H.; Crow, Wade T.; deJeu, Richard; DeLannoy, Gabrielle J. M.; Huffman, George J.; Jackson, Thomas J.

    2011-01-01

    The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.

  12. Combining Radar and Daily Precipitation Data to Estimate Meaningful Sub-daily Precipitation Extremes

    Science.gov (United States)

    Pegram, G. G. S.; Bardossy, A.

    2016-12-01

    Short duration extreme rainfalls are important for design. The purpose of this presentation is not to improve the day by day estimation of precipitation, but to obtain reasonable statistics for the subdaily extremes at gauge locations. We are interested specifically in daily and sub-daily extreme values of precipitation at gauge locations. We do not employ the common procedure of using time series of control station to determine the missing data values in a target. We are interested in individual rare events, not sequences. The idea is to use radar to disaggregate daily totals to sub-daily amounts. In South Arica, an S-band radar operated relatively continuously at Bethlehem from 1998 to 2003, whose scan at 1.5 km above ground [CAPPI] overlapped a dense (10 km spacing) set of 45 pluviometers recording in the same 6-year period. Using this valuable set of data, we are only interested in rare extremes, therefore small to medium values of rainfall depth were neglected, leaving 12 days of ranked daily maxima in each set per year, whose sum typically comprised about 50% of each annual rainfall total. The method presented here uses radar for disaggregating daily gauge totals in subdaily intervals down to 15 minutes in order to extract the maxima of sub-hourly through to daily rainfall at each of 37 selected radar pixels [1 km square in plan] which contained one of the 45 pluviometers not masked out by the radar foot-print. The pluviometer data were aggregated to daily totals, to act as if they were daily read gauges; their only other task was to help in the cross-validation exercise. The extrema were obtained as quantiles by ordering the 12 daily maxima of each interval per year. The unusual and novel goal was not to obtain the reproduction of the precipitation matching in space and time, but to obtain frequency distributions of the gauge and radar extremes, by matching their ranks, which we found to be stable and meaningful in cross-validation tests. We provide and

  13. Similarities and Improvements of GPM Dual-Frequency Precipitation Radar (DPR upon TRMM Precipitation Radar (PR in Global Precipitation Rate Estimation, Type Classification and Vertical Profiling

    Directory of Open Access Journals (Sweden)

    Jinyu Gao

    2017-11-01

    Full Text Available Spaceborne precipitation radars are powerful tools used to acquire adequate and high-quality precipitation estimates with high spatial resolution for a variety of applications in hydrological research. The Global Precipitation Measurement (GPM mission, which deployed the first spaceborne Ka- and Ku-dual frequency radar (DPR, was launched in February 2014 as the upgraded successor of the Tropical Rainfall Measuring Mission (TRMM. This study matches the swath data of TRMM PR and GPM DPR Level 2 products during their overlapping periods at the global scale to investigate their similarities and DPR’s improvements concerning precipitation amount estimation and type classification of GPM DPR over TRMM PR. Results show that PR and DPR agree very well with each other in the global distribution of precipitation, while DPR improves the detectability of precipitation events significantly, particularly for light precipitation. The occurrences of total precipitation and the light precipitation (rain rates < 1 mm/h detected by GPM DPR are ~1.7 and ~2.53 times more than that of PR. With regard to type classification, the dual-frequency (Ka/Ku and single frequency (Ku methods performed similarly. In both inner (the central 25 beams and outer swaths (1–12 beams and 38–49 beams of DPR, the results are consistent. GPM DPR improves precipitation type classification remarkably, reducing the misclassification of clouds and noise signals as precipitation type “other” from 10.14% of TRMM PR to 0.5%. Generally, GPM DPR exhibits the same type division for around 82.89% (71.02% of stratiform (convective precipitation events recognized by TRMM PR. With regard to the freezing level height and bright band (BB height, both radars correspond with each other very well, contributing to the consistency in stratiform precipitation classification. Both heights show clear latitudinal dependence. Results in this study shall contribute to future development of spaceborne

  14. Site Specific Probable Maximum Precipitation Estimates and Professional Judgement

    Science.gov (United States)

    Hayes, B. D.; Kao, S. C.; Kanney, J. F.; Quinlan, K. R.; DeNeale, S. T.

    2015-12-01

    State and federal regulatory authorities currently rely upon the US National Weather Service Hydrometeorological Reports (HMRs) to determine probable maximum precipitation (PMP) estimates (i.e., rainfall depths and durations) for estimating flooding hazards for relatively broad regions in the US. PMP estimates for the contributing watersheds upstream of vulnerable facilities are used to estimate riverine flooding hazards while site-specific estimates for small water sheds are appropriate for individual facilities such as nuclear power plants. The HMRs are often criticized due to their limitations on basin size, questionable applicability in regions affected by orographic effects, their lack of consist methods, and generally by their age. HMR-51 for generalized PMP estimates for the United States east of the 105th meridian, was published in 1978 and is sometimes perceived as overly conservative. The US Nuclear Regulatory Commission (NRC), is currently reviewing several flood hazard evaluation reports that rely on site specific PMP estimates that have been commercially developed. As such, NRC has recently investigated key areas of expert judgement via a generic audit and one in-depth site specific review as they relate to identifying and quantifying actual and potential storm moisture sources, determining storm transposition limits, and adjusting available moisture during storm transposition. Though much of the approach reviewed was considered a logical extension of HMRs, two key points of expert judgement stood out for further in-depth review. The first relates primarily to small storms and the use of a heuristic for storm representative dew point adjustment developed for the Electric Power Research Institute by North American Weather Consultants in 1993 in order to harmonize historic storms for which only 12 hour dew point data was available with more recent storms in a single database. The second issue relates to the use of climatological averages for spatially

  15. Development of Deep Learning Based Data Fusion Approach for Accurate Rainfall Estimation Using Ground Radar and Satellite Precipitation Products

    Science.gov (United States)

    Chen, H.; Chandra, C. V.; Tan, H.; Cifelli, R.; Xie, P.

    2016-12-01

    Rainfall estimation based on onboard satellite measurements has been an important topic in satellite meteorology for decades. A number of precipitation products at multiple time and space scales have been developed based upon satellite observations. For example, NOAA Climate Prediction Center has developed a morphing technique (i.e., CMORPH) to produce global precipitation products by combining existing space based rainfall estimates. The CMORPH products are essentially derived based on geostationary satellite IR brightness temperature information and retrievals from passive microwave measurements (Joyce et al. 2004). Although the space-based precipitation products provide an excellent tool for regional and global hydrologic and climate studies as well as improved situational awareness for operational forecasts, its accuracy is limited due to the sampling limitations, particularly for extreme events such as very light and/or heavy rain. On the other hand, ground-based radar is more mature science for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization technique and further enhanced by urban scale radar networks. Therefore, ground radars are often critical for providing local scale rainfall estimation and a "heads-up" for operational forecasters to issue watches and warnings as well as validation of various space measurements and products. The CASA DFW QPE system, which is based on dual-polarization X-band CASA radars and a local S-band WSR-88DP radar, has demonstrated its excellent performance during several years of operation in a variety of precipitation regimes. The real-time CASA DFW QPE products are used extensively for localized hydrometeorological applications such as urban flash flood forecasting. In this paper, a neural network based data fusion mechanism is introduced to improve the satellite-based CMORPH precipitation product by taking into account the ground radar measurements. A deep learning system is

  16. Quantitative precipitation estimation in complex orography using quasi-vertical profiles of dual polarization radar variables

    Science.gov (United States)

    Montopoli, Mario; Roberto, Nicoletta; Adirosi, Elisa; Gorgucci, Eugenio; Baldini, Luca

    2017-04-01

    Weather radars are nowadays a unique tool to estimate quantitatively the rain precipitation near the surface. This is an important task for a plenty of applications. For example, to feed hydrological models, mitigate the impact of severe storms at the ground using radar information in modern warning tools as well as aid the validation studies of satellite-based rain products. With respect to the latter application, several ground validation studies of the Global Precipitation Mission (GPM) products have recently highlighted the importance of accurate QPE from ground-based weather radars. To date, a plenty of works analyzed the performance of various QPE algorithms making use of actual and synthetic experiments, possibly trained by measurement of particle size distributions and electromagnetic models. Most of these studies support the use of dual polarization variables not only to ensure a good level of radar data quality but also as a direct input in the rain estimation equations. Among others, one of the most important limiting factors in radar QPE accuracy is the vertical variability of particle size distribution that affects at different levels, all the radar variables acquired as well as rain rates. This is particularly impactful in mountainous areas where the altitudes of the radar sampling is likely several hundred of meters above the surface. In this work, we analyze the impact of the vertical profile variations of rain precipitation on several dual polarization radar QPE algorithms when they are tested a in complex orography scenario. So far, in weather radar studies, more emphasis has been given to the extrapolation strategies that make use of the signature of the vertical profiles in terms of radar co-polar reflectivity. This may limit the use of the radar vertical profiles when dual polarization QPE algorithms are considered because in that case all the radar variables used in the rain estimation process should be consistently extrapolated at the surface

  17. Precipitation Data Merging over Mountainous Areas Using Satellite Estimates and Sparse Gauge Observations (PDMMA-USESGO) for Hydrological Modeling — A Case Study over the Tibetan Plateau

    Science.gov (United States)

    Yang, Z.; Hsu, K. L.; Sorooshian, S.; Xu, X.

    2017-12-01

    Precipitation in mountain regions generally occurs with high-frequency-intensity, whereas it is not well-captured by sparsely distributed rain-gauges imposing a great challenge on water management. Satellite-based Precipitation Estimation (SPE) provides global high-resolution alternative data for hydro-climatic studies, but are subject to considerable biases. In this study, a model named PDMMA-USESGO for Precipitation Data Merging over Mountainous Areas Using Satellite Estimates and Sparse Gauge Observations is developed to support precipitation mapping and hydrological modeling in mountainous catchments. The PDMMA-USESGO framework includes two calculating steps—adjusting SPE biases and merging satellite-gauge estimates—using the quantile mapping approach, a two-dimensional Gaussian weighting scheme (considering elevation effect), and an inverse root mean square error weighting method. The model is applied and evaluated over the Tibetan Plateau (TP) with the PERSIANN-CCS precipitation retrievals (daily, 0.04°×0.04°) and sparse observations from 89 gauges, for the 11-yr period of 2003-2013. To assess the data merging effects on streamflow modeling, a hydrological evaluation is conducted over a watershed in southeast TP based on the Soil and Water Assessment Tool (SWAT). Evaluation results indicate effectiveness of the model in generating high-resolution-accuracy precipitation estimates over mountainous terrain, with the merged estimates (Mer-SG) presenting consistently improved correlation coefficients, root mean square errors and absolute mean biases from original satellite estimates (Ori-CCS). It is found the Mer-SG forced streamflow simulations exhibit great improvements from those simulations using Ori-CCS, with coefficient of determination (R2) and Nash-Sutcliffe efficiency reach to 0.8 and 0.65, respectively. The presented model and case study serve as valuable references for the hydro-climatic applications using remote sensing-gauge information in

  18. Enhancement of regional wet deposition estimates based on modeled precipitation inputs

    Science.gov (United States)

    James A. Lynch; Jeffery W. Grimm; Edward S. Corbett

    1996-01-01

    Application of a variety of two-dimensional interpolation algorithms to precipitation chemistry data gathered at scattered monitoring sites for the purpose of estimating precipitation- born ionic inputs for specific points or regions have failed to produce accurate estimates. The accuracy of these estimates is particularly poor in areas of high topographic relief....

  19. Comparison of direct and precipitation methods for the estimation of ...

    African Journals Online (AJOL)

    Background: There is increase in use of direct assays for analysis of high and low density lipoprotein cholesterol by clinical laboratories despite differences in performance characteristics with conventional precipitation methods. Calculation of low density lipoprotein cholesterol in precipitation methods is based on total ...

  20. Investigation of Weather Radar Quantitative Precipitation Estimation Methodologies in Complex Orography

    Directory of Open Access Journals (Sweden)

    Mario Montopoli

    2017-02-01

    Full Text Available Near surface quantitative precipitation estimation (QPE from weather radar measurements is an important task for feeding hydrological models, limiting the impact of severe rain events at the ground as well as aiding validation studies of satellite-based rain products. To date, several works have analyzed the performance of various QPE algorithms using actual and synthetic experiments, possibly trained by measurement of particle size distributions and electromagnetic models. Most of these studies support the use of dual polarization radar variables not only to ensure a good level of data quality but also as a direct input to rain estimation equations. One of the most important limiting factors in radar QPE accuracy is the vertical variability of particle size distribution, which affects all the acquired radar variables as well as estimated rain rates at different levels. This is particularly impactful in mountainous areas, where the sampled altitudes are likely several hundred meters above the surface. In this work, we analyze the impact of the vertical profile variations of rain precipitation on several dual polarization radar QPE algorithms when they are tested in a complex orography scenario. So far, in weather radar studies, more emphasis has been given to the extrapolation strategies that use the signature of the vertical profiles in terms of radar co-polar reflectivity. This may limit the use of the radar vertical profiles when dual polarization QPE algorithms are considered. In that case, all the radar variables used in the rain estimation process should be consistently extrapolated at the surface to try and maintain the correlations among them. To avoid facing such a complexity, especially with a view to operational implementation, we propose looking at the features of the vertical profile of rain (VPR, i.e., after performing the rain estimation. This procedure allows characterization of a single variable (i.e., rain when dealing with

  1. Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling

    Directory of Open Access Journals (Sweden)

    Hyojin Lee

    2015-01-01

    Full Text Available Precipitation is the main factor that drives hydrologic modeling; therefore, missing precipitation data can cause malfunctions in hydrologic modeling. Although interpolation of missing precipitation data is recognized as an important research topic, only a few methods follow a regression approach. In this study, daily precipitation data were interpolated using five different kernel functions, namely, Epanechnikov, Quartic, Triweight, Tricube, and Cosine, to estimate missing precipitation data. This study also presents an assessment that compares estimation of missing precipitation data through Kth nearest neighborhood (KNN regression to the five different kernel estimations and their performance in simulating streamflow using the Soil Water Assessment Tool (SWAT hydrologic model. The results show that the kernel approaches provide higher quality interpolation of precipitation data compared with the KNN regression approach, in terms of both statistical data assessment and hydrologic modeling performance.

  2. 14 CFR 141.91 - Satellite bases.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 3 2010-01-01 2010-01-01 false Satellite bases. 141.91 Section 141.91... OTHER CERTIFICATED AGENCIES PILOT SCHOOLS Operating Rules § 141.91 Satellite bases. The holder of a... assistant chief instructor is designated for each satellite base, and that assistant chief instructor is...

  3. Estimation of Satellite-Based SO42- and NH4+ Composition of Ambient Fine Particulate Matter Over China Using Chemical Transport Model

    Science.gov (United States)

    Si, Y.; Li, S.; Chen, L.; Yu, C.; Zhu, W.

    2018-04-01

    Epidemiologic and health impact studies have examined the chemical composition of ambient PM2.5 in China but have been constrained by the paucity of long-term ground measurements. Using the GEOS-Chem chemical transport model and satellite-derived PM2.5 data, sulfate and ammonium levels were estimated over China from 2004 to 2014. A comparison of the satellite-estimated dataset with model simulations based on ground measurements obtained from the literature indicated our results are more accurate. Using satellite-derived PM2.5 data with a spatial resolution of 0.1° × 0.1°, we further presented finer satellite-estimated sulfate and ammonium concentrations in anthropogenic polluted regions, including the NCP (the North China Plain), the SCB (the Sichuan Basin) and the PRD (the Pearl River Delta). Linear regression results obtained on a national scale yielded an r value of 0.62, NMB of -35.9 %, NME of 48.2 %, ARB_50 % of 53.68 % for sulfate and an r value of 0.63, slope of 0.67, and intercept of 5.14 for ammonium. In typical regions, the satellite-derived dataset was significantly robust. Based on the satellite-derived dataset, the spatial-temporal variation of 11-year annual average satellite-derived SO42- and NH4+ concentrations and time series of monthly average concentrations were also investigated. On a national scale, both exhibited a downward trend each year between 2004 and 2014 (SO42-: -0.61 %; NH4+: -0.21 %), large values were mainly concentrated in the NCP and SCB. For regions captured at a finer resolution, the inter-annual variation trends presented a positive trend over the periods 2004-2007 and 2008-2011, followed by a negative trend over the period 2012-2014, and sulfate concentrations varied appreciably. Moreover, the seasonal distributions of the 11-year satellite-derived dataset over China were presented. The distribution of both sulfate and ammonium concentrations exhibited seasonal characteristics, with the seasonal concentrations ranking as

  4. Satellite-based estimates of long-term exposure to fine particulate matter are associated with C-reactive protein in 30 034 Taiwanese adults.

    Science.gov (United States)

    Zhang, Zilong; Chang, Ly-Yun; Lau, Alexis K H; Chan, Ta-Chien; Chieh Chuang, Yuan; Chan, Jimmy; Lin, Changqing; Kai Jiang, Wun; Dear, Keith; Zee, Benny C Y; Yeoh, Eng-Kiong; Hoek, Gerard; Tam, Tony; Qian Lao, Xiang

    2017-08-01

    Particulate matter (PM) air pollution is associated with the risk of cardiovascular morbidity and mortality. However, the biological mechanism underlying the associations remains unclear. Atherosclerosis, the underlying pathology of cardiovascular disease, is a chronic inflammatory process. We therefore investigated the association of long-term exposure to fine PM (PM2.5) with C-reactive protein (CRP), a sensitive marker of systemic inflammation, in a large Taiwanese population. Participants were from a large cohort who participated in a standard medical examination programme with measurements of high-sensitivity CRP between 2007 and 2014. We used a spatiotemporal model to estimate 2-year average PM2.5 exposure at each participant's address, based on satellite-derived aerosol optical depth data. General regression models were used for baseline data analysis and mixed-effects linear regression models were used for repeated data analysis to investigate the associations between PM2.5 exposure and CRP, adjusting for a wide range of potential confounders. In this population of 30 034 participants with 39 096 measurements, every 5 μg/m3 PM2.5 increment was associated with a 1.31% increase in CRP [95% confidence interval (CI): 1.00%, 1.63%) after adjusting for confounders. For those participants with repeated CRP measurements, no significant changes were observed between the first and last measurements (0.88 mg/l vs 0.89 mg/l, P = 0.337). The PM2.5 concentrations remained stable over time between 2007 and 2014. Long-term exposure to PM2.5 is associated with increased level of systemic inflammation, supporting the biological link between PM2.5 air pollution and deteriorating cardiovascular health. Air pollution reduction should be an important strategy to prevent cardiovascular disease. © The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association

  5. Assessing the Suitability and Limitations of Satellite-based Measurements for Estimating CO, CO2, NO2 and O3 Concentrations over the Niger Delta

    Science.gov (United States)

    Fagbeja, M. A.; Hill, J. L.; Chatterton, T. J.; Longhurst, J. W.; Akinyede, J. O.

    2011-12-01

    Space-based satellite sensor technology may provide important tools in the study and assessment of national, regional and local air pollution. However, the application of optical satellite sensor observation of atmospheric trace gases, including those considered to be 'air pollutants', within the lower latitudes is limited due to prevailing climatic conditions. The lack of appropriate air pollution ground monitoring stations within the tropical belt reduces the ability to verify and calibrate space-based measurements. This paper considers the suitability of satellite remotely sensed data in estimating concentrations of atmospheric trace gases in view of the prevailing climate over the Niger Delta region. The methodological approach involved identifying suitable satellite data products and using the ArcGIS Geostatistical Analyst kriging interpolation technique to generate surface concentrations from satellite column measurements. The observed results are considered in the context of the climate of the study area. Using data from January 2001 to December 2005, an assessment of the suitability of satellite sensor data to interpolate column concentrations of trace gases over the Niger Delta has been undertaken and indicates varying degrees of reliability. The level of reliability of the interpolated surfaces is predicated on the number and spatial distributions of column measurements. Accounting for the two climatic seasons in the region, the interpolation of total column concentrations of CO and CO2 from SCIAMACHY produced both reliable and unreliable results over inland parts of the region during the dry season, while mainly unreliable results are observed over the coastal parts especially during the rainy season due to inadequate column measurements. The interpolation of tropospheric measurements of NO2 and O3 from GOME and OMI respectively produced reliable results all year. This is thought to be due to the spatial distribution of available column measurements

  6. A spatial approach to the modelling and estimation of areal precipitation

    Energy Technology Data Exchange (ETDEWEB)

    Skaugen, T

    1996-12-31

    In hydroelectric power technology it is important that the mean precipitation that falls in an area can be calculated. This doctoral thesis studies how the morphology of rainfall, described by the spatial statistical parameters, can be used to improve interpolation and estimation procedures. It attempts to formulate a theory which includes the relations between the size of the catchment and the size of the precipitation events in the modelling of areal precipitation. The problem of estimating and modelling areal precipitation can be formulated as the problem of estimating an inhomogeneously distributed flux of a certain spatial extent being measured at points in a randomly placed domain. The information contained in the different morphology of precipitation types is used to improve estimation procedures of areal precipitation, by interpolation (kriging) or by constructing areal reduction factors. A new approach to precipitation modelling is introduced where the analysis of the spatial coverage of precipitation at different intensities plays a key role in the formulation of a stochastic model for extreme areal precipitation and in deriving the probability density function of areal precipitation. 127 refs., 30 figs., 13 tabs.

  7. Satellite-Based Sunshine Duration for Europe

    Directory of Open Access Journals (Sweden)

    Bodo Ahrens

    2013-06-01

    Full Text Available In this study, two different methods were applied to derive daily and monthly sunshine duration based on high-resolution satellite products provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT Satellite Application Facility on Climate Monitoring using data from Meteosat Second Generation (MSG SEVIRI (Spinning Enhanced Visible and Infrared Imager. The satellite products were either hourly cloud type or hourly surface incoming direct radiation. The satellite sunshine duration estimates were not found to be significantly different using the native 15-minute temporal resolution of SEVIRI. The satellite-based sunshine duration products give additional spatial information over the European continent compared with equivalent in situ-based products. An evaluation of the satellite sunshine duration by product intercomparison and against station measurements was carried out to determine their accuracy. The satellite data were found to be within ±1 h/day compared to high-quality Baseline Surface Radiation Network or surface synoptic observations (SYNOP station measurements. The satellite-based products differ more over the oceans than over land, mainly because of the treatment of fractional clouds in the cloud type-based sunshine duration product. This paper presents the methods used to derive the satellite sunshine duration products and the performance of the different retrievals. The main benefits and disadvantages compared to station-based products are also discussed.

  8. Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal

    Directory of Open Access Journals (Sweden)

    Tarendra Lakhankar

    2013-08-01

    Full Text Available Precipitation in mountain regions is often highly variable and poorly observed, limiting abilities to manage water resource challenges. Here, we evaluate remote sensing and ground station-based gridded precipitation products over Nepal against weather station precipitation observations on a monthly timescale. We find that the Tropical Rainfall Measuring Mission (TRMM 3B-43 precipitation product exhibits little mean bias and reasonable skill in giving precipitation over Nepal. Compared to station observations, the TRMM precipitation product showed an overall Nash-Sutcliffe efficiency of 0.49, which is similar to the skill of the gridded station-based product Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE. The other satellite precipitation products considered (Global Satellite Mapping of Precipitation (GSMaP, the Climate Prediction Center Morphing technique (CMORPH, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS were less skillful, as judged by Nash-Sutcliffe efficiency, and, on average, substantially underestimated precipitation compared to station observations, despite their, in some cases, higher nominal spatial resolution compared to TRMM. None of the products fully captured the dependence of mean precipitation on elevation seen in the station observations. Overall, the TRMM product is promising for use in water resources applications.

  9. Estimating mountain basin-mean precipitation from streamflow using Bayesian inference

    Science.gov (United States)

    Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; Lundquist, Jessica D.

    2015-10-01

    Estimating basin-mean precipitation in complex terrain is difficult due to uncertainty in the topographical representativeness of precipitation gauges relative to the basin. To address this issue, we use Bayesian methodology coupled with a multimodel framework to infer basin-mean precipitation from streamflow observations, and we apply this approach to snow-dominated basins in the Sierra Nevada of California. Using streamflow observations, forcing data from lower-elevation stations, the Bayesian Total Error Analysis (BATEA) methodology and the Framework for Understanding Structural Errors (FUSE), we infer basin-mean precipitation, and compare it to basin-mean precipitation estimated using topographically informed interpolation from gauges (PRISM, the Parameter-elevation Regression on Independent Slopes Model). The BATEA-inferred spatial patterns of precipitation show agreement with PRISM in terms of the rank of basins from wet to dry but differ in absolute values. In some of the basins, these differences may reflect biases in PRISM, because some implied PRISM runoff ratios may be inconsistent with the regional climate. We also infer annual time series of basin precipitation using a two-step calibration approach. Assessment of the precision and robustness of the BATEA approach suggests that uncertainty in the BATEA-inferred precipitation is primarily related to uncertainties in hydrologic model structure. Despite these limitations, time series of inferred annual precipitation under different model and parameter assumptions are strongly correlated with one another, suggesting that this approach is capable of resolving year-to-year variability in basin-mean precipitation.

  10. Impact of time displaced precipitation estimates for on-line updated models

    DEFF Research Database (Denmark)

    Borup, Morten; Grum, Morten; Mikkelsen, Peter Steen

    2012-01-01

    When an online runoff model is updated from system measurements the requirements to the precipitation estimates change. Using rain gauge data as precipitation input there will be a displacement between the time where the rain intensity hits the gauge and the time where the rain hits the actual...

  11. Multi-spectral band selection for satellite-based systems

    International Nuclear Information System (INIS)

    Clodius, W.B.; Weber, P.G.; Borel, C.C.; Smith, B.W.

    1998-01-01

    The design of satellite based multispectral imaging systems requires the consideration of a number of tradeoffs between cost and performance. The authors have recently been involved in the design and evaluation of a satellite based multispectral sensor operating from the visible through the long wavelength IR. The criteria that led to some of the proposed designs and the modeling used to evaluate and fine tune the designs will both be discussed. These criteria emphasized the use of bands for surface temperature retrieval and the correction of atmospheric effects. The impact of cost estimate changes on the final design will also be discussed

  12. Satellite-based laser windsounder

    International Nuclear Information System (INIS)

    Schultz, J.F.; Czuchlewski, S.J.; Quick, C.R.

    1997-01-01

    This is the final report of a one-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The project''s primary objective is to determine the technical feasibility of using satellite-based laser wind sensing systems for detailed study of winds, aerosols, and particulates around and downstream of suspected proliferation facilities. Extensive interactions with the relevant operational organization resulted in enthusiastic support and useful guidance with respect to measurement requirements and priorities. Four candidate wind sensing techniques were evaluated, and the incoherent Doppler technique was selected. A small satellite concept design study was completed to identify the technical issues inherent in a proof-of-concept small satellite mission. Use of a Mach-Zehnder interferometer instead of a Fabry-Perot would significantly simplify the optical train and could reduce weight, and possibly power, requirements with no loss of performance. A breadboard Mach-Zehnder interferometer-based system has been built to verify these predictions. Detailed plans were made for resolving other issues through construction and testing of a ground-based lidar system in collaboration with the University of Wisconsin, and through numerical lidar wind data assimilation studies

  13. Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational Awareness

    Science.gov (United States)

    Kirschbaum, Dalia; Stanley, Thomas

    2018-03-01

    Determining the time, location, and severity of natural disaster impacts is fundamental to formulating mitigation strategies, appropriate and timely responses, and robust recovery plans. A Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to indicate potential landslide activity in near real-time. LHASA combines satellite-based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. Precipitation data from the Global Precipitation Measurement (GPM) mission are used to identify rainfall conditions from the past 7 days. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a "nowcast" is issued to indicate the times and places where landslides are more probable. When LHASA nowcasts were evaluated with a Global Landslide Catalog, the probability of detection (POD) ranged from 8% to 60%, depending on the evaluation period, precipitation product used, and the size of the spatial and temporal window considered around each landslide point. Applications of the LHASA system are also discussed, including how LHASA is used to estimate long-term trends in potential landslide activity at a nearly global scale and how it can be used as a tool to support disaster risk assessment. LHASA is intended to provide situational awareness of landslide hazards in near real-time, providing a flexible, open-source framework that can be adapted to other spatial and temporal scales based on data availability.

  14. Rainfall estimation in SWAT: An alternative method to simulate orographic precipitation

    Science.gov (United States)

    Galván, L.; Olías, M.; Izquierdo, T.; Cerón, J. C.; Fernández de Villarán, R.

    2014-02-01

    The input of water from precipitation is one of the most important aspects of a hydrologic model because it controls the basin's water budget. The model should reproduce the amount and distribution of rainfall in the basin, spatially and temporally. SWAT (Soil and Water Assessment Tool) is one of the most widely used hydrologic models. In this paper the rainfall estimation in SWAT is revised, focusing on the treatment of orographic precipitation. SWAT was applied to the Odiel river basin (SW Spain), with a surface of 2300 km2. Results show that SWAT does not reflect reallisticaly the spatial distribution of rainfall in the basin. In relation to orographic precipitation, SWAT estimates the daily precipitation in elevation bands by adding a constant amount to the recorded precipitation in the rain gauge, which depends on the increase in precipitation with altitude and the difference between the mean elevation of each band and the elevation of the recording gauge. This does not reflect rainfall in the subbasin because the increase in precipitation with altitude actually it is not constant, but depends on the amount of rainfall. An alternative methodology to represent the temporal distribution of orographic precipitation is proposed. After simulation, the deviation of runoff volume using the SWAT elevation bands was appreciably higher than that obtained with the proposed methodology.

  15. Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)

    Science.gov (United States)

    Adler, Robert; Gu, Guojun; Huffman, George

    2012-01-01

    A procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within +/- 50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation s of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (s/m, where m is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%-15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (s) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a

  16. Long-Term Precipitation Analysis and Estimation of Precipitation Concentration Index Using Three Support Vector Machine Methods

    Directory of Open Access Journals (Sweden)

    Milan Gocic

    2016-01-01

    Full Text Available The monthly precipitation data from 29 stations in Serbia during the period of 1946–2012 were considered. Precipitation trends were calculated using linear regression method. Three CLINO periods (1961–1990, 1971–2000, and 1981–2010 in three subregions were analysed. The CLINO 1981–2010 period had a significant increasing trend. Spatial pattern of the precipitation concentration index (PCI was presented. For the purpose of PCI prediction, three Support Vector Machine (SVM models, namely, SVM coupled with the discrete wavelet transform (SVM-Wavelet, the firefly algorithm (SVM-FFA, and using the radial basis function (SVM-RBF, were developed and used. The estimation and prediction results of these models were compared with each other using three statistical indicators, that is, root mean square error, coefficient of determination, and coefficient of efficiency. The experimental results showed that an improvement in predictive accuracy and capability of generalization can be achieved by the SVM-Wavelet approach. Moreover, the results indicated the proposed SVM-Wavelet model can adequately predict the PCI.

  17. REAL - Ensemble radar precipitation estimation for hydrology in a mountainous region

    OpenAIRE

    Germann, Urs; Berenguer Ferrer, Marc; Sempere Torres, Daniel; Zappa, Massimiliano

    2009-01-01

    An elegant solution to characterise the residual errors in radar precipitation estimates is to generate an ensemble of precipitation fields. The paper proposes a radar ensemble generator designed for usage in the Alps using LU decomposition (REAL), and presents first results from a real-time implementation coupling the radar ensemble with a semi-distributed rainfall–runoff model for flash flood modelling in a steep Alpine catchment. Each member of the radar ensemble is a possible realisati...

  18. GPM Precipitation Estimates over the Walnut Gulch Experimental Watershed/LTAR site in Southeastern Arizona

    Science.gov (United States)

    Goodrich, D. C.; Tan, J.; Petersen, W. A.; Unkrich, C. C.; Demaria, E. M.; Hazenberg, P.; Lakshmi, V.

    2017-12-01

    Precipitation profiles from the GPM Core Observatory Dual-frequency Precipitation Radar (DPR) form part of the a priori database used in GPM Goddard Profiling (GPROF) algorithm passive microwave radiometer retrievals of rainfall. The GPROF retrievals are in turn used as high quality precipitation estimates in gridded products such as IMERG. Due to the variability in and high surface emissivity of land surfaces, GPROF performs precipitation retrievals as a function of surface classes. As such, different surface types may possess different error characteristics, especially over arid regions where high quality ground measurements are often lacking. Importantly, the emissive properties of land also result in GPROF rainfall estimates being driven primarily by the higher frequency radiometer channels (e.g., > 89 GHz) where precipitation signals are most sensitive to coupling between the ice-phase and rainfall production. In this study, we evaluate the rainfall estimates from the Ku channel of the DPR as well as GPROF estimates from various passive microwave sensors. Our evaluation is conducted at the level of individual satellite pixels (5 to 15 km in diameter), against a dense network of weighing rain gauges (90 in 150 km2) in the USDA-ARS Walnut Gulch Experimental Watershed and Long-Term Agroecosystem Research (LTAR) site in southeastern Arizona. The multiple gauges in each satellite pixel and precise accumulation about the overpass time allow a spatially and temporally representative comparison between the satellite estimates and ground reference. Over Walnut Gulch, both the Ku and GPROF estimates are challenged to delineate between rain and no-rain. Probabilities of detection are relatively high, but false alarm ratios are also high. The rain intensities possess a negative bias across nearly all sensors. It is likely that storm types, arid conditions and the highly variable precipitation regime present a challenge to both rainfall retrieval algorithms. An array of

  19. Surface Runoff Estimation Using SMOS Observations, Rain-gauge Measurements and Satellite Precipitation Estimations. Comparison with Model Predictions

    Science.gov (United States)

    Garcia Leal, Julio A.; Lopez-Baeza, Ernesto; Khodayar, Samiro; Estrela, Teodoro; Fidalgo, Arancha; Gabaldo, Onofre; Kuligowski, Robert; Herrera, Eddy

    Surface runoff is defined as the amount of water that originates from precipitation, does not infiltrates due to soil saturation and therefore circulates over the surface. A good estimation of runoff is useful for the design of draining systems, structures for flood control and soil utilisation. For runoff estimation there exist different methods such as (i) rational method, (ii) isochrone method, (iii) triangular hydrograph, (iv) non-dimensional SCS hydrograph, (v) Temez hydrograph, (vi) kinematic wave model, represented by the dynamics and kinematics equations for a uniforme precipitation regime, and (vii) SCS-CN (Soil Conservation Service Curve Number) model. This work presents a way of estimating precipitation runoff through the SCS-CN model, using SMOS (Soil Moisture and Ocean Salinity) mission soil moisture observations and rain-gauge measurements, as well as satellite precipitation estimations. The area of application is the Jucar River Basin Authority area where one of the objectives is to develop the SCS-CN model in a spatial way. The results were compared to simulations performed with the 7-km COSMO-CLM (COnsortium for Small-scale MOdelling, COSMO model in CLimate Mode) model. The use of SMOS soil moisture as input to the COSMO-CLM model will certainly improve model simulations.

  20. Estimation of the impact of climate change-induced extreme precipitation events on floods

    Science.gov (United States)

    Hlavčová, Kamila; Lapin, Milan; Valent, Peter; Szolgay, Ján; Kohnová, Silvia; Rončák, Peter

    2015-09-01

    In order to estimate possible changes in the flood regime in the mountainous regions of Slovakia, a simple physically-based concept for climate change-induced changes in extreme 5-day precipitation totals is proposed in the paper. It utilizes regionally downscaled scenarios of the long-term monthly means of the air temperature, specific air humidity and precipitation projected for Central Slovakia by two regional (RCM) and two global circulation models (GCM). A simplified physically-based model for the calculation of short-term precipitation totals over the course of changing air temperatures, which is used to drive a conceptual rainfall-runoff model, was proposed. In the paper a case study of this approach in the upper Hron river basin in Central Slovakia is presented. From the 1981-2010 period, 20 events of the basin's most extreme average of 5-day precipitation totals were selected. Only events with continual precipitation during 5 days were considered. These 5-day precipitation totals were modified according to the RCM and GCM-based scenarios for the future time horizons of 2025, 2050 and 2075. For modelling runoff under changed 5-day precipitation totals, a conceptual rainfall-runoff model developed at the Slovak University of Technology was used. Changes in extreme mean daily discharges due to climate change were compared with the original flood events and discussed.

  1. Evaluating the applicability of four recent satellite–gauge combined precipitation estimates for extreme precipitation and streamflow predictions over the upper Yellow river basin in China

    Science.gov (United States)

    This study aimed to statistically and hydrologically assess the performance of four latest and widely used satellite–gauge combined precipitation estimates (SGPEs), namely CRT, BLD, 3B42CDR, and 3B42 for the extreme precipitation and stream'ow scenarios over the upper Yellow river basin (UYRB) in ch...

  2. Improving Satellite Quantitative Precipitation Estimation Using GOES-Retrieved Cloud Optical Depth

    Energy Technology Data Exchange (ETDEWEB)

    Stenz, Ronald; Dong, Xiquan; Xi, Baike; Feng, Zhe; Kuligowski, Robert J.

    2016-02-01

    To address significant gaps in ground-based radar coverage and rain gauge networks in the U.S., geostationary satellite quantitative precipitation estimates (QPEs) such as the Self-Calibrating Multivariate Precipitation Retrievals (SCaMPR) can be used to fill in both the spatial and temporal gaps of ground-based measurements. Additionally, with the launch of GOES-R, the temporal resolution of satellite QPEs may be comparable to that of Weather Service Radar-1988 Doppler (WSR-88D) volume scans as GOES images will be available every five minutes. However, while satellite QPEs have strengths in spatial coverage and temporal resolution, they face limitations particularly during convective events. Deep Convective Systems (DCSs) have large cloud shields with similar brightness temperatures (BTs) over nearly the entire system, but widely varying precipitation rates beneath these clouds. Geostationary satellite QPEs relying on the indirect relationship between BTs and precipitation rates often suffer from large errors because anvil regions (little/no precipitation) cannot be distinguished from rain-cores (heavy precipitation) using only BTs. However, a combination of BTs and optical depth (τ) has been found to reduce overestimates of precipitation in anvil regions (Stenz et al. 2014). A new rain mask algorithm incorporating both τ and BTs has been developed, and its application to the existing SCaMPR algorithm was evaluated. The performance of the modified SCaMPR was evaluated using traditional skill scores and a more detailed analysis of performance in individual DCS components by utilizing the Feng et al. (2012) classification algorithm. SCaMPR estimates with the new rain mask applied benefited from significantly reduced overestimates of precipitation in anvil regions and overall improvements in skill scores.

  3. Satellite Based Cropland Carbon Monitoring System

    Science.gov (United States)

    Bandaru, V.; Jones, C. D.; Sedano, F.; Sahajpal, R.; Jin, H.; Skakun, S.; Pnvr, K.; Kommareddy, A.; Reddy, A.; Hurtt, G. C.; Izaurralde, R. C.

    2017-12-01

    Agricultural croplands act as both sources and sinks of atmospheric carbon dioxide (CO2); absorbing CO2 through photosynthesis, releasing CO2 through autotrophic and heterotrophic respiration, and sequestering CO2 in vegetation and soils. Part of the carbon captured in vegetation can be transported and utilized elsewhere through the activities of food, fiber, and energy production. As well, a portion of carbon in soils can be exported somewhere else by wind, water, and tillage erosion. Thus, it is important to quantify how land use and land management practices affect the net carbon balance of croplands. To monitor the impacts of various agricultural activities on carbon balance and to develop management strategies to make croplands to behave as net carbon sinks, it is of paramount importance to develop consistent and high resolution cropland carbon flux estimates. Croplands are typically characterized by fine scale heterogeneity; therefore, for accurate carbon flux estimates, it is necessary to account for the contribution of each crop type and their spatial distribution. As part of NASA CMS funded project, a satellite based Cropland Carbon Monitoring System (CCMS) was developed to estimate spatially resolved crop specific carbon fluxes over large regions. This modeling framework uses remote sensing version of Environmental Policy Integrated Climate Model and satellite derived crop parameters (e.g. leaf area index (LAI)) to determine vertical and lateral carbon fluxes. The crop type LAI product was developed based on the inversion of PRO-SAIL radiative transfer model and downscaled MODIS reflectance. The crop emergence and harvesting dates were estimated based on MODIS NDVI and crop growing degree days. To evaluate the performance of CCMS framework, it was implemented over croplands of Nebraska, and estimated carbon fluxes for major crops (i.e. corn, soybean, winter wheat, grain sorghum, alfalfa) grown in 2015. Key findings of the CCMS framework will be presented

  4. Estimating Reservoir Inflow Using RADAR Forecasted Precipitation and Adaptive Neuro Fuzzy Inference System

    Science.gov (United States)

    Yi, J.; Choi, C.

    2014-12-01

    Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.

  5. Radar rainfall estimation of stratiform winter precipitation in the Belgian Ardennes

    NARCIS (Netherlands)

    Hazenberg, P.; Leijnse, H.; Uijlenhoet, R.

    2011-01-01

    Radars are known for their ability to obtain a wealth of information about spatial storm field characteristics. Unfortunately, rainfall estimates obtained by this instrument are known to be affected by multiple sources of error. Especially for stratiform precipitation systems, the quality of radar

  6. Improving real-time estimation of heavy-to-extreme precipitation using rain gauge data via conditional bias-penalized optimal estimation

    Science.gov (United States)

    Seo, Dong-Jun; Siddique, Ridwan; Zhang, Yu; Kim, Dongsoo

    2014-11-01

    A new technique for gauge-only precipitation analysis for improved estimation of heavy-to-extreme precipitation is described and evaluated. The technique is based on a novel extension of classical optimal linear estimation theory in which, in addition to error variance, Type-II conditional bias (CB) is explicitly minimized. When cast in the form of well-known kriging, the methodology yields a new kriging estimator, referred to as CB-penalized kriging (CBPK). CBPK, however, tends to yield negative estimates in areas of no or light precipitation. To address this, an extension of CBPK, referred to herein as extended conditional bias penalized kriging (ECBPK), has been developed which combines the CBPK estimate with a trivial estimate of zero precipitation. To evaluate ECBPK, we carried out real-world and synthetic experiments in which ECBPK and the gauge-only precipitation analysis procedure used in the NWS's Multisensor Precipitation Estimator (MPE) were compared for estimation of point precipitation and mean areal precipitation (MAP), respectively. The results indicate that ECBPK improves hourly gauge-only estimation of heavy-to-extreme precipitation significantly. The improvement is particularly large for estimation of MAP for a range of combinations of basin size and rain gauge network density. This paper describes the technique, summarizes the results and shares ideas for future research.

  7. Radar-derived quantitative precipitation estimation in complex terrain over the eastern Tibetan Plateau

    Science.gov (United States)

    Gou, Yabin; Ma, Yingzhao; Chen, Haonan; Wen, Yixin

    2018-05-01

    Quantitative precipitation estimation (QPE) is one of the important applications of weather radars. However, in complex terrain such as Tibetan Plateau, it is a challenging task to obtain an optimal Z-R relation due to the complex spatial and temporal variability in precipitation microphysics. This paper develops two radar QPE schemes respectively based on Reflectivity Threshold (RT) and Storm Cell Identification and Tracking (SCIT) algorithms using observations from 11 Doppler weather radars and 3264 rain gauges over the Eastern Tibetan Plateau (ETP). These two QPE methodologies are evaluated extensively using four precipitation events that are characterized by different meteorological features. Precipitation characteristics of independent storm cells associated with these four events, as well as the storm-scale differences, are investigated using short-term vertical profile of reflectivity (VPR) clusters. Evaluation results show that the SCIT-based rainfall approach performs better than the simple RT-based method for all precipitation events in terms of score comparison using validation gauge measurements as references. It is also found that the SCIT-based approach can effectively mitigate the local error of radar QPE and represent the precipitation spatiotemporal variability better than the RT-based scheme.

  8. Estimation of precipitable water at different locations using surface dew-point

    Science.gov (United States)

    Abdel Wahab, M.; Sharif, T. A.

    1995-09-01

    The Reitan (1963) regression equation of the form ln w = a + bT d has been examined and tested to estimate precipitable water vapor content from the surface dew point temperature at different locations. The results of this study indicate that the slope b of the above equation has a constant value of 0.0681, while the intercept a changes rapidly with latitude. The use of the variable intercept technique can improve the estimated result by about 2%.

  9. Evaluation of spatial and spatiotemporal estimation methods in simulation of precipitation variability patterns

    Science.gov (United States)

    Bayat, Bardia; Zahraie, Banafsheh; Taghavi, Farahnaz; Nasseri, Mohsen

    2013-08-01

    Identification of spatial and spatiotemporal precipitation variations plays an important role in different hydrological applications such as missing data estimation. In this paper, the results of Bayesian maximum entropy (BME) and ordinary kriging (OK) are compared for modeling spatial and spatiotemporal variations of annual precipitation with and without incorporating elevation variations. The study area of this research is Namak Lake watershed located in the central part of Iran with an area of approximately 90,000 km2. The BME and OK methods have been used to model the spatial and spatiotemporal variations of precipitation in this watershed, and their performances have been evaluated using cross-validation statistics. The results of the case study have shown the superiority of BME over OK in both spatial and spatiotemporal modes. The results have shown that BME estimates are less biased and more accurate than OK. The improvements in the BME estimates are mostly related to incorporating hard and soft data in the estimation process, which resulted in more detailed and reliable results. Estimation error variance for BME results is less than OK estimations in the study area in both spatial and spatiotemporal modes.

  10. Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

    Science.gov (United States)

    Akbar, Ruzbeh; Short Gianotti, Daniel; McColl, Kaighin A.; Haghighi, Erfan; Salvucci, Guido D.; Entekhabi, Dara

    2018-03-01

    The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface-only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface-only soil moisture observations. To proceed, first an observation-based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry-downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root-mean-squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation-driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge-corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east-west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.

  11. Local likelihood estimation of complex tail dependence structures in high dimensions, applied to US precipitation extremes

    KAUST Repository

    Camilo, Daniela Castro

    2017-10-02

    In order to model the complex non-stationary dependence structure of precipitation extremes over the entire contiguous U.S., we propose a flexible local approach based on factor copula models. Our sub-asymptotic spatial modeling framework yields non-trivial tail dependence structures, with a weakening dependence strength as events become more extreme, a feature commonly observed with precipitation data but not accounted for in classical asymptotic extreme-value models. To estimate the local extremal behavior, we fit the proposed model in small regional neighborhoods to high threshold exceedances, under the assumption of local stationarity. This allows us to gain in flexibility, while making inference for such a large and complex dataset feasible. Adopting a local censored likelihood approach, inference is made on a fine spatial grid, and local estimation is performed taking advantage of distributed computing resources and of the embarrassingly parallel nature of this estimation procedure. The local model is efficiently fitted at all grid points, and uncertainty is measured using a block bootstrap procedure. An extensive simulation study shows that our approach is able to adequately capture complex, non-stationary dependencies, while our study of U.S. winter precipitation data reveals interesting differences in local tail structures over space, which has important implications on regional risk assessment of extreme precipitation events. A comparison between past and current data suggests that extremes in certain areas might be slightly wider in extent nowadays than during the first half of the twentieth century.

  12. Local likelihood estimation of complex tail dependence structures in high dimensions, applied to US precipitation extremes

    KAUST Repository

    Camilo, Daniela Castro; Huser, Raphaë l

    2017-01-01

    In order to model the complex non-stationary dependence structure of precipitation extremes over the entire contiguous U.S., we propose a flexible local approach based on factor copula models. Our sub-asymptotic spatial modeling framework yields non-trivial tail dependence structures, with a weakening dependence strength as events become more extreme, a feature commonly observed with precipitation data but not accounted for in classical asymptotic extreme-value models. To estimate the local extremal behavior, we fit the proposed model in small regional neighborhoods to high threshold exceedances, under the assumption of local stationarity. This allows us to gain in flexibility, while making inference for such a large and complex dataset feasible. Adopting a local censored likelihood approach, inference is made on a fine spatial grid, and local estimation is performed taking advantage of distributed computing resources and of the embarrassingly parallel nature of this estimation procedure. The local model is efficiently fitted at all grid points, and uncertainty is measured using a block bootstrap procedure. An extensive simulation study shows that our approach is able to adequately capture complex, non-stationary dependencies, while our study of U.S. winter precipitation data reveals interesting differences in local tail structures over space, which has important implications on regional risk assessment of extreme precipitation events. A comparison between past and current data suggests that extremes in certain areas might be slightly wider in extent nowadays than during the first half of the twentieth century.

  13. Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

    Directory of Open Access Journals (Sweden)

    Shaowei Ning

    2016-10-01

    Full Text Available The Global Precipitation Mission (GPM Core Observatory that was launched on 27 February 2014 ushered in a new era for estimating precipitation from satellites. Based on their high spatial–temporal resolution and near global coverage, satellite-based precipitation products have been applied in many research fields. The goal of this study was to quantitatively compare two of the latest GPM-era satellite precipitation products (GPM IMERG and GSMap-Gauge Ver. 6 with a network of 840 precipitation gauges over the Chinese mainland. Direct comparisons of satellite-based precipitation products with rain gauge observations over a 20 month period from April 2014 to November 2015 at 0.1° and daily/monthly resolutions showed the following results: Both of the products were capable of capturing the overall spatial pattern of the 20 month mean daily precipitation, which was characterized by a decreasing trend from the southeast to the northwest. GPM IMERG overestimated precipitation by approximately 0.09 mm/day while GSMap-Gauge Ver. 6 underestimated precipitation by −0.04 mm/day. The two satellite-based precipitation products performed better over wet southern regions than over dry northern regions. They also showed better performance in summer than in winter. In terms of mean error, root mean square error, correlation coefficient, and probability of detection, GSMap-Gauge was better able to estimate precipitation and had more stable quality results than GPM IMERG on both daily and monthly scales. GPM IMERG was more sensitive to conditions of no rain or light rainfall and demonstrated good capability of capturing the behavior of extreme precipitation events. Overall, the results revealed some limitations of these two latest satellite-based precipitation products when used over the Chinese mainland, helping to characterize some of the error features in these datasets for potential users.

  14. Precipitation areal-reduction factor estimation using an annual-maxima centered approach

    Science.gov (United States)

    Asquith, W.H.; Famiglietti, J.S.

    2000-01-01

    The adjustment of precipitation depth of a point storm to an effective (mean) depth over a watershed is important for characterizing rainfall-runoff relations and for cost-effective designs of hydraulic structures when design storms are considered. A design storm is the precipitation point depth having a specified duration and frequency (recurrence interval). Effective depths are often computed by multiplying point depths by areal-reduction factors (ARF). ARF range from 0 to 1, vary according to storm characteristics, such as recurrence interval; and are a function of watershed characteristics, such as watershed size, shape, and geographic location. This paper presents a new approach for estimating ARF and includes applications for the 1-day design storm in Austin, Dallas, and Houston, Texas. The approach, termed 'annual-maxima centered,' specifically considers the distribution of concurrent precipitation surrounding an annual-precipitation maxima, which is a feature not seen in other approaches. The approach does not require the prior spatial averaging of precipitation, explicit determination of spatial correlation coefficients, nor explicit definition of a representative area of a particular storm in the analysis. The annual-maxima centered approach was designed to exploit the wide availability of dense precipitation gauge data in many regions of the world. The approach produces ARF that decrease more rapidly than those from TP-29. Furthermore, the ARF from the approach decay rapidly with increasing recurrence interval of the annual-precipitation maxima. (C) 2000 Elsevier Science B.V.The adjustment of precipitation depth of a point storm to an effective (mean) depth over a watershed is important for characterizing rainfall-runoff relations and for cost-effective designs of hydraulic structures when design storms are considered. A design storm is the precipitation point depth having a specified duration and frequency (recurrence interval). Effective depths are

  15. An "Ensemble Approach" to Modernizing Extreme Precipitation Estimation for Dam Safety Decision-Making

    Science.gov (United States)

    Cifelli, R.; Mahoney, K. M.; Webb, R. S.; McCormick, B.

    2017-12-01

    To ensure structural and operational safety of dams and other water management infrastructure, water resources managers and engineers require information about the potential for heavy precipitation. The methods and data used to estimate extreme rainfall amounts for managing risk are based on 40-year-old science and in need of improvement. The need to evaluate new approaches based on the best science available has led the states of Colorado and New Mexico to engage a body of scientists and engineers in an innovative "ensemble approach" to updating extreme precipitation estimates. NOAA is at the forefront of one of three technical approaches that make up the "ensemble study"; the three approaches are conducted concurrently and in collaboration with each other. One approach is the conventional deterministic, "storm-based" method, another is a risk-based regional precipitation frequency estimation tool, and the third is an experimental approach utilizing NOAA's state-of-the-art High Resolution Rapid Refresh (HRRR) physically-based dynamical weather prediction model. The goal of the overall project is to use the individual strengths of these different methods to define an updated and broadly acceptable state of the practice for evaluation and design of dam spillways. This talk will highlight the NOAA research and NOAA's role in the overarching goal to better understand and characterizing extreme precipitation estimation uncertainty. The research led by NOAA explores a novel high-resolution dataset and post-processing techniques using a super-ensemble of hourly forecasts from the HRRR model. We also investigate how this rich dataset may be combined with statistical methods to optimally cast the data in probabilistic frameworks. NOAA expertise in the physical processes that drive extreme precipitation is also employed to develop careful testing and improved understanding of the limitations of older estimation methods and assumptions. The process of decision making in the

  16. Radar rainfall estimation of stratiform winter precipitation in the Belgian Ardennes

    Science.gov (United States)

    Hazenberg, P.; Leijnse, H.; Uijlenhoet, R.

    2011-02-01

    Radars are known for their ability to obtain a wealth of information about spatial storm field characteristics. Unfortunately, rainfall estimates obtained by this instrument are known to be affected by multiple sources of error. Especially for stratiform precipitation systems, the quality of radar rainfall estimates starts to decrease at relatively close ranges. In the current study, the hydrological potential of weather radar is analyzed during a winter half-year for the hilly region of the Belgian Ardennes. A correction algorithm is proposed which corrects the radar data for errors related to attenuation, ground clutter, anomalous propagation, the vertical profile of reflectivity (VPR), and advection. No final bias correction with respect to rain gauge data was implemented because such an adjustment would not add to a better understanding of the quality of the radar data. The impact of the different corrections is assessed using rainfall information sampled by 42 hourly rain gauges. The largest improvement in the quality of the radar data is obtained by correcting for ground clutter. The impact of VPR correction and advection depends on the spatial variability and velocity of the precipitation system. Overall during the winter period, the radar underestimates the amount of precipitation as compared to the rain gauges. Remaining differences between both instruments can be attributed to spatial and temporal variability in the type of precipitation, which has not been taken into account.

  17. Kriging and local polynomial methods for blending satellite-derived and gauge precipitation estimates to support hydrologic early warning systems

    Science.gov (United States)

    Verdin, Andrew; Funk, Christopher C.; Rajagopalan, Balaji; Kleiber, William

    2016-01-01

    Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and κ-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.

  18. The assessment of Global Precipitation Measurement estimates over the Indian subcontinent

    Science.gov (United States)

    Murali Krishna, U. V.; Das, Subrata Kumar; Deshpande, Sachin M.; Doiphode, S. L.; Pandithurai, G.

    2017-08-01

    Accurate and real-time precipitation estimation is a challenging task for current and future spaceborne measurements, which is essential to understand the global hydrological cycle. Recently, the Global Precipitation Measurement (GPM) satellites were launched as a next-generation rainfall mission for observing the global precipitation characteristics. The purpose of the GPM is to enhance the spatiotemporal resolution of global precipitation. The main objective of the present study is to assess the rainfall products from the GPM, especially the Integrated Multi-satellitE Retrievals for the GPM (IMERG) data by comparing with the ground-based observations. The multitemporal scale evaluations of rainfall involving subdaily, diurnal, monthly, and seasonal scales were performed over the Indian subcontinent. The comparison shows that the IMERG performed better than the Tropical Rainfall Measuring Mission (TRMM)-3B42, although both rainfall products underestimated the observed rainfall compared to the ground-based measurements. The analyses also reveal that the TRMM-3B42 and IMERG data sets are able to represent the large-scale monsoon rainfall spatial features but are having region-specific biases. The IMERG shows significant improvement in low rainfall estimates compared to the TRMM-3B42 for selected regions. In the spatial distribution, the IMERG shows higher rain rates compared to the TRMM-3B42, due to its enhanced spatial and temporal resolutions. Apart from this, the characteristics of raindrop size distribution (DSD) obtained from the GPM mission dual-frequency precipitation radar is assessed over the complex mountain terrain site in the Western Ghats, India, using the DSD measured by a Joss-Waldvogel disdrometer.

  19. Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

    Directory of Open Access Journals (Sweden)

    Qiutong Jin

    2016-06-01

    Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

  20. Estimating Probable Maximum Precipitation by Considering Combined Effect of Typhoon and Southwesterly Air Flow

    Directory of Open Access Journals (Sweden)

    Cheng-Chin Liu

    2016-01-01

    Full Text Available Typhoon Morakot hit southern Taiwan in 2009, bringing 48-hr of heavy rainfall [close to the Probable Maximum Precipitation (PMP] to the Tsengwen Reservoir catchment. This extreme rainfall event resulted from the combined (co-movement effect of two climate systems (i.e., typhoon and southwesterly air flow. Based on the traditional PMP estimation method (i.e., the storm transposition method, STM, two PMP estimation approaches, i.e., Amplification Index (AI and Independent System (IS approaches, which consider the combined effect are proposed in this work. The AI approach assumes that the southwesterly air flow precipitation in a typhoon event could reach its maximum value. The IS approach assumes that the typhoon and southwesterly air flow are independent weather systems. Based on these assumptions, calculation procedures for the two approaches were constructed for a case study on the Tsengwen Reservoir catchment. The results show that the PMP estimates for 6- to 60-hr durations using the two approaches are approximately 30% larger than the PMP estimates using the traditional STM without considering the combined effect. This work is a pioneer PMP estimation method that considers the combined effect of a typhoon and southwesterly air flow. Further studies on this issue are essential and encouraged.

  1. Precipitation Estimation Using Combined Radar/Radiometer Measurements Within the GPM Framework

    Science.gov (United States)

    Hou, Arthur

    2012-01-01

    satellite of JAXA, (3) the Multi-Frequency Microwave Scanning Radiometer (MADRAS) and the multi-channel microwave humidity sounder (SAPHIR) on the French-Indian Megha- Tropiques satellite, (4) the Microwave Humidity Sounder (MHS) on the National Oceanic and Atmospheric Administration (NOAA)-19, (5) MHS instruments on MetOp satellites launched by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), (6) the Advanced Technology Microwave Sounder (ATMS) on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), and (7) ATMS instruments on the NOAA-NASA Joint Polar Satellite System (JPSS) satellites. Data from Chinese and Russian microwave radiometers may also become available through international collaboration under the auspices of the Committee on Earth Observation Satellites (CEOS) and Group on Earth Observations (GEO). The current generation of global rainfall products combines observations from a network of uncoordinated satellite missions using a variety of merging techniques. GPM will provide next-generation precipitation products characterized by: (1) more accurate instantaneous precipitation estimate (especially for light rain and cold-season solid precipitation), (2) intercalibrated microwave brightness temperatures from constellation radiometers within a consistent framework, and (3) unified precipitation retrievals from constellation radiometers using a common a priori hydrometeor database constrained by combined radar/radiometer measurements provided by the GPM Core Observatory.

  2. Leo satellite-based telecommunication network concepts

    Science.gov (United States)

    Aiken, John G.; Swan, Peter A.; Leopold, Ray J.

    1991-01-01

    Design considerations are discussed for Low Earth Orbit (LEO) satellite based telecommunications networks. The satellites are assumed to be connected to each other via intersatellite links. They are connected to the end user either directly or through gateways to other networks. Frequency reuse, circuit switching, packet switching, call handoff, and routing for these systems are discussed by analogy with terrestrial cellular (mobile radio) telecommunication systems.

  3. Evaluating the MSG satellite Multi-Sensor Precipitation Estimate for extreme rainfall monitoring over northern Tunisia

    Directory of Open Access Journals (Sweden)

    Saoussen Dhib

    2017-06-01

    Full Text Available Knowledge and evaluation of extreme precipitation is important for water resources and flood risk management, soil and land degradation, and other environmental issues. Due to the high potential threat to local infrastructure, such as buildings, roads and power supplies, heavy precipitation can have an important social and economic impact on society. At present, satellite derived precipitation estimates are becoming more readily available. This paper aims to investigate the potential use of the Meteosat Second Generation (MSG Multi-Sensor Precipitation Estimate (MPE for extreme rainfall assessment in Tunisia. The MSGMPE data combine microwave rain rate estimations with SEVIRI thermal infrared channel data, using an EUMETSAT production chain in near real time mode. The MPE data can therefore be used in a now-casting mode, and are potentially useful for extreme weather early warning and monitoring. Daily precipitation observed across an in situ gauge network in the north of Tunisia were used during the period 2007–2009 for validation of the MPE extreme event data. As a first test of the MSGMPE product's performance, very light to moderate rainfall classes, occurring between January and October 2007, were evaluated. Extreme rainfall events were then selected, using a threshold criterion for large rainfall depth (>50 mm/day occurring at least at one ground station. Spatial interpolation methods were applied to generate rainfall maps for the drier summer season (from May to October and the wet winter season (from November to April. Interpolated gauge rainfall maps were then compared to MSGMPE data available from the EUMETSAT UMARF archive or from the GEONETCast direct dissemination system. The summation of the MPE data at 5 and/or 15 min time intervals over a 24 h period, provided a basis for comparison. The MSGMPE product was not very effective in the detection of very light and light rain events. Better results were obtained for the slightly

  4. Near-real-time Estimation and Forecast of Total Precipitable Water in Europe

    Science.gov (United States)

    Bartholy, J.; Kern, A.; Barcza, Z.; Pongracz, R.; Ihasz, I.; Kovacs, R.; Ferencz, C.

    2013-12-01

    Information about the amount and spatial distribution of atmospheric water vapor (or total precipitable water) is essential for understanding weather and the environment including the greenhouse effect, the climate system with its feedbacks and the hydrological cycle. Numerical weather prediction (NWP) models need accurate estimations of water vapor content to provide realistic forecasts including representation of clouds and precipitation. In the present study we introduce our research activity for the estimation and forecast of atmospheric water vapor in Central Europe using both observations and models. The Eötvös Loránd University (Hungary) operates a polar orbiting satellite receiving station in Budapest since 2002. This station receives Earth observation data from polar orbiting satellites including MODerate resolution Imaging Spectroradiometer (MODIS) Direct Broadcast (DB) data stream from satellites Terra and Aqua. The received DB MODIS data are automatically processed using freely distributed software packages. Using the IMAPP Level2 software total precipitable water is calculated operationally using two different methods. Quality of the TPW estimations is a crucial question for further application of the results, thus validation of the remotely sensed total precipitable water fields is presented using radiosonde data. In a current research project in Hungary we aim to compare different estimations of atmospheric water vapor content. Within the frame of the project we use a NWP model (DBCRAS; Direct Broadcast CIMSS Regional Assimilation System numerical weather prediction software developed by the University of Wisconsin, Madison) to forecast TPW. DBCRAS uses near real time Level2 products from the MODIS data processing chain. From the wide range of the derived Level2 products the MODIS TPW parameter found within the so-called mod07 results (Atmospheric Profiles Product) and the cloud top pressure and cloud effective emissivity parameters from the so

  5. Detecting weather radar clutter using satellite-based nowcasting products

    DEFF Research Database (Denmark)

    Jensen, Thomas B.S.; Gill, Rashpal S.; Overgaard, Søren

    2006-01-01

    This contribution presents the initial results from experiments with detection of weather radar clutter by information fusion with satellite based nowcasting products. Previous studies using information fusion of weather radar data and first generation Meteosat imagery have shown promising results...... for the detecting and removal of clutter. Naturally, the improved spatio-temporal resolution of the Meteosat Second Generation sensors, coupled with its increased number of spectral bands, is expected to yield even better detection accuracies. Weather radar data from three C-band Doppler weather radars...... Application Facility' of EUMETSAT and is based on multispectral images from the SEVIRI sensor of the Meteosat-8 platform. Of special interest is the 'Precipitating Clouds' product, which uses the spectral information coupled with surface temperatures from Numerical Weather Predictions to assign probabilities...

  6. Component Analysis of Errors on PERSIANN Precipitation Estimates over Urmia Lake Basin, IRAN

    Science.gov (United States)

    Ghajarnia, N.; Daneshkar Arasteh, P.; Liaghat, A. M.; Araghinejad, S.

    2016-12-01

    In this study, PERSIANN daily dataset is evaluated from 2000 to 2011 in 69 pixels over Urmia Lake basin in northwest of Iran. Different analytical approaches and indexes are used to examine PERSIANN precision in detection and estimation of rainfall rate. The residuals are decomposed into Hit, Miss and FA estimation biases while continues decomposition of systematic and random error components are also analyzed seasonally and categorically. New interpretation of estimation accuracy named "reliability on PERSIANN estimations" is introduced while the changing manners of existing categorical/statistical measures and error components are also seasonally analyzed over different rainfall rate categories. This study yields new insights into the nature of PERSIANN errors over Urmia lake basin as a semi-arid region in the middle-east, including the followings: - The analyzed contingency table indexes indicate better detection precision during spring and fall. - A relatively constant level of error is generally observed among different categories. The range of precipitation estimates at different rainfall rate categories is nearly invariant as a sign for the existence of systematic error. - Low level of reliability is observed on PERSIANN estimations at different categories which are mostly associated with high level of FA error. However, it is observed that as the rate of precipitation increase, the ability and precision of PERSIANN in rainfall detection also increases. - The systematic and random error decomposition in this area shows that PERSIANN has more difficulty in modeling the system and pattern of rainfall rather than to have bias due to rainfall uncertainties. The level of systematic error also considerably increases in heavier rainfalls. It is also important to note that PERSIANN error characteristics at each season varies due to the condition and rainfall patterns of that season which shows the necessity of seasonally different approach for the calibration of

  7. Improved Lower Mekong River Basin Hydrological Decision Making Using NASA Satellite-based Earth Observation Systems

    Science.gov (United States)

    Bolten, J. D.; Mohammed, I. N.; Srinivasan, R.; Lakshmi, V.

    2017-12-01

    Better understanding of the hydrological cycle of the Lower Mekong River Basin (LMRB) and addressing the value-added information of using remote sensing data on the spatial variability of soil moisture over the Mekong Basin is the objective of this work. In this work, we present the development and assessment of the LMRB (drainage area of 495,000 km2) Soil and Water Assessment Tool (SWAT). The coupled model framework presented is part of SERVIR, a joint capacity building venture between NASA and the U.S. Agency for International Development, providing state-of-the-art, satellite-based earth monitoring, imaging and mapping data, geospatial information, predictive models, and science applications to improve environmental decision-making among multiple developing nations. The developed LMRB SWAT model enables the integration of satellite-based daily gridded precipitation, air temperature, digital elevation model, soil texture, and land cover and land use data to drive SWAT model simulations over the Lower Mekong River Basin. The LMRB SWAT model driven by remote sensing climate data was calibrated and verified with observed runoff data at the watershed outlet as well as at multiple sites along the main river course. Another LMRB SWAT model set driven by in-situ climate observations was also calibrated and verified to streamflow data. Simulated soil moisture estimates from the two models were then examined and compared to a downscaled Soil Moisture Active Passive Sensor (SMAP) 36 km radiometer products. Results from this work present a framework for improving SWAT performance by utilizing a downscaled SMAP soil moisture products used for model calibration and validation. Index Terms: 1622: Earth system modeling; 1631: Land/atmosphere interactions; 1800: Hydrology; 1836 Hydrological cycles and budgets; 1840 Hydrometeorology; 1855: Remote sensing; 1866: Soil moisture; 6334: Regional Planning

  8. A satellite-based global landslide model

    Directory of Open Access Journals (Sweden)

    A. Farahmand

    2013-05-01

    Full Text Available Landslides are devastating phenomena that cause huge damage around the world. This paper presents a quasi-global landslide model derived using satellite precipitation data, land-use land cover maps, and 250 m topography information. This suggested landslide model is based on the Support Vector Machines (SVM, a machine learning algorithm. The National Aeronautics and Space Administration (NASA Goddard Space Flight Center (GSFC landslide inventory data is used as observations and reference data. In all, 70% of the data are used for model development and training, whereas 30% are used for validation and verification. The results of 100 random subsamples of available landslide observations revealed that the suggested landslide model can predict historical landslides reliably. The average error of 100 iterations of landslide prediction is estimated to be approximately 7%, while approximately 2% false landslide events are observed.

  9. Errors and parameter estimation in precipitation-runoff modeling: 1. Theory

    Science.gov (United States)

    Troutman, Brent M.

    1985-01-01

    Errors in complex conceptual precipitation-runoff models may be analyzed by placing them into a statistical framework. This amounts to treating the errors as random variables and defining the probabilistic structure of the errors. By using such a framework, a large array of techniques, many of which have been presented in the statistical literature, becomes available to the modeler for quantifying and analyzing the various sources of error. A number of these techniques are reviewed in this paper, with special attention to the peculiarities of hydrologic models. Known methodologies for parameter estimation (calibration) are particularly applicable for obtaining physically meaningful estimates and for explaining how bias in runoff prediction caused by model error and input error may contribute to bias in parameter estimation.

  10. Assessment of Satellite Precipitation Products in the Philippine Archipelago

    Science.gov (United States)

    Ramos, M. D.; Tendencia, E.; Espana, K.; Sabido, J.; Bagtasa, G.

    2016-06-01

    Precipitation is the most important weather parameter in the Philippines. Made up of more than 7100 islands, the Philippine archipelago is an agricultural country that depends on rain-fed crops. Located in the western rim of the North West Pacific Ocean, this tropical island country is very vulnerable to tropical cyclones that lead to severe flooding events. Recently, satellite-based precipitation estimates have improved significantly and can serve as alternatives to ground-based observations. These data can be used to fill data gaps not only for climatic studies, but can also be utilized for disaster risk reduction and management activities. This study characterized the statistical errors of daily precipitation from four satellite-based rainfall products from (1) the Tropical Rainfall Measuring Mission (TRMM), (2) the CPC Morphing technique (CMORPH) of NOAA and (3) the Global Satellite Mapping of Precipitation (GSMAP) and (4) Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks (PERSIANN). Precipitation data were compared to 52 synoptic weather stations located all over the Philippines. Results show GSMAP to have over all lower bias and CMORPH with lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In addition, a dichotomous rainfall test reveals GSMAP and CMORPH have low Proportion Correct (PC) for convective and stratiform rainclouds, respectively. TRMM consistently showed high PC for almost all raincloud types. Moreover, all four satellite precipitation showed high Correct Negatives (CN) values for the north-western part of the country during the North-East monsoon and spring monsoonal transition periods.

  11. Global estimate of lichen and bryophyte contributions to forest precipitation interception

    Science.gov (United States)

    Van Stan, John; Porada, Philipp; Kleidon, Axel

    2017-04-01

    Interception of precipitation by forest canopies plays an important role in its partitioning to evaporation, transpiration and runoff. Field observations show arboreal lichens and bryophytes can substantially enhance forests' precipitation storage and evaporation. However, representations of canopy interception in global land surface models currently ignore arboreal lichen and bryophyte contributions. This study uses the lichen and bryophyte model (LiBry) to provide the first process-based modelling approach estimating these organisms' contributions to canopy water storage and evaporation. The global mean value of forest water storage capacity increased significantly from 0.87 mm to 1.33 mm by the inclusion of arboreal poikilohydric organisms. Global forest canopy evaporation of intercepted precipitation was also greatly enhanced by 44%. Ratio of total versus bare canopy global evaporation exceeded 2 in many forested regions. This altered global patterns in canopy water storage, evaporation, and ultimately the proportion of rainfall evaporated. A sensitivity analysis was also performed. Results indicate rainfall interception is of larger magnitude than previously reported by global land surface modelling work because of the important role of lichen and bryophytes in rainfall interception.

  12. The Relative Performance of High Resolution Quantitative Precipitation Estimates in the Russian River Basin

    Science.gov (United States)

    Bytheway, J. L.; Biswas, S.; Cifelli, R.; Hughes, M.

    2017-12-01

    The Russian River carves a 110 mile path through Mendocino and Sonoma counties in western California, providing water for thousands of residents and acres of agriculture as well as a home for several species of endangered fish. The Russian River basin receives almost all of its precipitation during the October through March wet season, and the systems bringing this precipitation are often impacted by atmospheric river events as well as the complex topography of the region. This study will examine the performance of several high resolution (hourly, products and forecasts over the 2015-2016 and 2016-2017 wet seasons. Comparisons of event total rainfall as well as hourly rainfall will be performed using 1) rain gauges operated by the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Division (PSD), 2) products from the Multi-Radar/Multi-Sensor (MRMS) QPE dataset, and 3) quantitative precipitation forecasts from the High Resolution Rapid Refresh (HRRR) model at 1, 3, 6, and 12 hour lead times. Further attention will be given to cases or locations representing large disparities between the estimates.

  13. Precipitation estimates and comparison of satellite rainfall data to in situ rain gauge observations to further develop the watershed-modeling capabilities for the Lower Mekong River Basin

    Science.gov (United States)

    Dandridge, C.; Lakshmi, V.; Sutton, J. R. P.; Bolten, J. D.

    2017-12-01

    This study focuses on the lower region of the Mekong River Basin (MRB), an area including Burma, Cambodia, Vietnam, Laos, and Thailand. This region is home to expansive agriculture that relies heavily on annual precipitation over the basin for its prosperity. Annual precipitation amounts are regulated by the global monsoon system and therefore vary throughout the year. This research will lead to improved prediction of floods and management of floodwaters for the MRB. We compare different satellite estimates of precipitation to each other and to in-situ precipitation estimates for the Mekong River Basin. These comparisons will help us determine which satellite precipitation estimates are better at predicting precipitation in the MRB and will help further our understanding of watershed-modeling capabilities for the basin. In this study we use: 1) NOAA's PERSIANN daily 0.25° precipitation estimate Climate Data Record (CDR), 2) NASA's Tropical Rainfall Measuring Mission (TRMM) daily 0.25° estimate, and 3) NASA's Global Precipitation Measurement (GPM) daily 0.1 estimate and 4) 488 in-situ stations located in the lower MRB provide daily precipitation estimates. The PERSIANN CDR precipitation estimate was able to provide the longest data record because it is available from 1983 to present. The TRMM precipitation estimate is available from 2000 to present and the GPM precipitation estimates are available from 2015 to present. It is for this reason that we provide several comparisons between our precipitation estimates. Comparisons were done between each satellite product and the in-situ precipitation estimates based on geographical location and date using the entire available data record for each satellite product for daily, monthly, and yearly precipitation estimates. We found that monthly PERSIANN precipitation estimates were able to explain up to 90% of the variability in station precipitation depending on station location.

  14. Recent Progress on the Second Generation CMORPH: LEO-IR Based Precipitation Estimates and Cloud Motion Vector

    Science.gov (United States)

    Xie, Pingping; Joyce, Robert; Wu, Shaorong

    2015-04-01

    As reported at the EGU General Assembly of 2014, a prototype system was developed for the second generation CMORPH to produce global analyses of 30-min precipitation on a 0.05olat/lon grid over the entire globe from pole to pole through integration of information from satellite observations as well as numerical model simulations. The second generation CMORPH is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) as well as LEO platforms, and precipitation simulations from numerical global models. Key to the success of the 2nd generation CMORPH, among a couple of other elements, are the development of a LEO-IR based precipitation estimation to fill in the polar gaps and objectively analyzed cloud motion vectors to capture the cloud movements of various spatial scales over the entire globe. In this presentation, we report our recent work on the refinement for these two important algorithm components. The prototype algorithm for the LEO IR precipitation estimation is refined to achieve improved quantitative accuracy and consistency with PMW retrievals. AVHRR IR TBB data from all LEO satellites are first remapped to a 0.05olat/lon grid over the entire globe and in a 30-min interval. Temporally and spatially co-located data pairs of the LEO TBB and inter-calibrated combined satellite PMW retrievals (MWCOMB) are then collected to construct tables. Precipitation at a grid box is derived from the TBB through matching the PDF tables for the TBB and the MWCOMB. This procedure is implemented for different season, latitude band and underlying surface types to account for the variations in the cloud - precipitation relationship. At the meantime, a sub-system is developed to construct analyzed fields of

  15. Assessing satellite-based start-of-season trends in the US High Plains

    International Nuclear Information System (INIS)

    Lin, X; Sassenrath, G F; Hubbard, K G; Mahmood, R

    2014-01-01

    To adequately assess the effects of global warming it is necessary to address trends and impacts at the local level. This study examines phenological changes in the start-of-season (SOS) derived from satellite observations from 1982–2008 in the US High Plains region. The surface climate-based SOS was also evaluated. The averaged profiles of SOS from 37° to 49°N latitude by satellite- and climate-based methods were in reasonable agreement, especially for areas where croplands were masked out and an additional frost date threshold was adopted. The statistically significant trends of satellite-based SOS show a later spring arrival ranging from 0.1 to 4.9 days decade −1 over nine Level III ecoregions. We found the croplands generally exhibited larger trends (later arrival) than the non-croplands. The area-averaged satellite-based SOS for non-croplands (i.e. mostly grasslands) showed no significant trends. We examined the trends of temperatures, precipitation, and standardized precipitation index (SPI), as well as the strength of correlation between the satellite-based SOS and these climatic drivers. Our results indicate that satellite-based SOS trends are spatially and primarily related to annual maximum normalized difference vegetation index (NDVI, mostly in summertime) and/or annual minimum NDVI (mostly in wintertime) and these trends showed the best correlation with six-month SPI over the period 1982–2008 in the US High Plains region. (letter)

  16. Extreme Precipitation Estimation with Typhoon Morakot Using Frequency and Spatial Analysis

    Directory of Open Access Journals (Sweden)

    Hone-Jay Chu

    2011-01-01

    Full Text Available Typhoon Morakot lashed Taiwan and produced copious amounts of precipitation in 2009. From the point view of hydrological statistics, the impact of the precipitation from typhoon Morakot using a frequency analysis can be analyzed and discussed. The frequency curve, which was fitted mathematically to historical observed data, can be used to estimate the probability of exceedance for runoff events of a certain magnitude. The study integrates frequency analysis and spatial analysis to assess the effect of Typhoon Morakot event on rainfall frequency in the Gaoping River basin of southern Taiwan. First, extreme rainfall data are collected at sixteen stations for durations of 1, 3, 6, 12, and 24 hours and then an appropriate probability distribution was selected to analyze the impact of the extreme hydrological event. Spatial rainfall patterns for a return period of 200-yr with 24-hr duration with and without Typhoon Morakot are estimated. Results show that the rainfall amount is significantly different with long duration with and without the event for frequency analysis. Furthermore, spatial analysis shows that extreme rainfall for a return period of 200-yr is highly dependent on topography and is smaller in the southwest than that in the east. The results not only demonstrate the distinct effect of Typhoon Morakot on frequency analysis, but also could provide reference in future planning of hydrological engineering.

  17. A near real-time satellite-based global drought climate data record

    International Nuclear Information System (INIS)

    AghaKouchak, Amir; Nakhjiri, Navid

    2012-01-01

    Reliable drought monitoring requires long-term and continuous precipitation data. High resolution satellite measurements provide valuable precipitation information on a quasi-global scale. However, their short lengths of records limit their applications in drought monitoring. In addition to this limitation, long-term low resolution satellite-based gauge-adjusted data sets such as the Global Precipitation Climatology Project (GPCP) one are not available in near real-time form for timely drought monitoring. This study bridges the gap between low resolution long-term satellite gauge-adjusted data and the emerging high resolution satellite precipitation data sets to create a long-term climate data record of droughts. To accomplish this, a Bayesian correction algorithm is used to combine GPCP data with real-time satellite precipitation data sets for drought monitoring and analysis. The results showed that the combined data sets after the Bayesian correction were a significant improvement compared to the uncorrected data. Furthermore, several recent major droughts such as the 2011 Texas, 2010 Amazon and 2010 Horn of Africa droughts were detected in the combined real-time and long-term satellite observations. This highlights the potential application of satellite precipitation data for regional to global drought monitoring. The final product is a real-time data-driven satellite-based standardized precipitation index that can be used for drought monitoring especially over remote and/or ungauged regions. (letter)

  18. Depth-area-duration characteristics of storm rainfall in Texas using Multi-Sensor Precipitation Estimates

    Science.gov (United States)

    McEnery, J. A.; Jitkajornwanich, K.

    2012-12-01

    This presentation will describe the methodology and overall system development by which a benchmark dataset of precipitation information has been used to characterize the depth-area-duration relations in heavy rain storms occurring over regions of Texas. Over the past two years project investigators along with the National Weather Service (NWS) West Gulf River Forecast Center (WGRFC) have developed and operated a gateway data system to ingest, store, and disseminate NWS multi-sensor precipitation estimates (MPE). As a pilot project of the Integrated Water Resources Science and Services (IWRSS) initiative, this testbed uses a Standard Query Language (SQL) server to maintain a full archive of current and historic MPE values within the WGRFC service area. These time series values are made available for public access as web services in the standard WaterML format. Having this volume of information maintained in a comprehensive database now allows the use of relational analysis capabilities within SQL to leverage these multi-sensor precipitation values and produce a valuable derivative product. The area of focus for this study is North Texas and will utilize values that originated from the West Gulf River Forecast Center (WGRFC); one of three River Forecast Centers currently represented in the holdings of this data system. Over the past two decades, NEXRAD radar has dramatically improved the ability to record rainfall. The resulting hourly MPE values, distributed over an approximate 4 km by 4 km grid, are considered by the NWS to be the "best estimate" of rainfall. The data server provides an accepted standard interface for internet access to the largest time-series dataset of NEXRAD based MPE values ever assembled. An automated script has been written to search and extract storms over the 18 year period of record from the contents of this massive historical precipitation database. Not only can it extract site-specific storms, but also duration-specific storms and

  19. Satellite-based Drought Reporting on the Navajo Nation

    Science.gov (United States)

    McCullum, A. J. K.; Schmidt, C.; Ly, V.; Green, R.; McClellan, C.

    2017-12-01

    The Navajo Nation (NN) is the largest reservation in the US, and faces challenges related to water management during long-term and widespread drought episodes. The Navajo Nation is a federally recognized tribe, which has boundaries within Arizona, New Mexico, and Utah. The Navajo Nation has a land area of over 70,000 square kilometers. The Navajo Nation Department of Water Resources (NNDWR) reports on drought and climatic conditions through the use of regional Standardized Precipitation Index (SPI) values and a network of in-situ rainfall, streamflow, and climate data. However, these data sources lack the spatial detail and consistent measurements needed to provide a coherent understanding of the drought regime within the Nation's regional boundaries. This project, as part of NASA's Western Water Applications Office (WWAO), improves upon the recently developed Drought Severity Assessment Tool (DSAT) to ingest satellite-based precipitation data to generate SPI values for specific administrative boundaries within the reservation. The tool aims to: (1) generate SPI values and summary statistics for regions of interest on various timescales, (2) to visualize SPI values within a web-map application, and (3) produce maps and comparative statistical outputs in the format required for annual drought reporting. The co-development of the DSAT with NN partners is integral to increasing the sustained use of Earth Observations for water management applications. This tool will provide data to support the NN in allocation of drought contingency dollars to the regions most adversely impacted by declines in water availability.

  20. Radar rainfall estimation for the identification of debris-flow precipitation thresholds

    Science.gov (United States)

    Marra, Francesco; Nikolopoulos, Efthymios I.; Creutin, Jean-Dominique; Borga, Marco

    2014-05-01

    Identification of rainfall thresholds for the prediction of debris-flow occurrence is a common approach for warning procedures. Traditionally the debris-flow triggering rainfall is derived from the closest available raingauge. However, the spatial and temporal variability of intense rainfall on mountainous areas, where debris flows take place, may lead to large uncertainty in point-based estimates. Nikolopoulos et al. (2014) have shown that this uncertainty translates into a systematic underestimation of the rainfall thresholds, leading to a step degradation of the performances of the rainfall threshold for identification of debris flows occurrence under operational conditions. A potential solution to this limitation lies on use of rainfall estimates from weather radar. Thanks to their high spatial and temporal resolutions, these estimates offer the advantage of providing rainfall information over the actual debris flow location. The aim of this study is to analyze the value of radar precipitation estimations for the identification of debris flow precipitation thresholds. Seven rainfall events that triggered debris flows in the Adige river basin (Eastern Italian Alps) are analyzed using data from a dense raingauge network and a C-Band weather radar. Radar data are elaborated by using a set of correction algorithms specifically developed for weather radar rainfall application in mountainous areas. Rainfall thresholds for the triggering of debris flows are identified in the form of average intensity-duration power law curves using a frequentist approach by using both radar rainfall estimates and raingauge data. Sampling uncertainty associated to the derivation of the thresholds is assessed by using a bootstrap technique (Peruccacci et al. 2012). Results show that radar-based rainfall thresholds are largely exceeding those obtained by using raingauge data. Moreover, the differences between the two thresholds may be related to the spatial characteristics (i.e., spatial

  1. Estimating and forecasting the precipitable water vapor from GOES satellite data at high altitude sites

    Science.gov (United States)

    Marín, Julio C.; Pozo, Diana; Curé, Michel

    2015-01-01

    In this work, we describe a method to estimate the precipitable water vapor (PWV) from Geostationary Observational Environmental Satellite (GOES) data at high altitude sites. The method was applied at Atacama Pathfinder Experiment (APEX) and Cerro Toco sites, located above 5000 m altitude in the Chajnantor plateau, in the north of Chile. It was validated using GOES-12 satellite data over the range 0-1.2 mm since submillimeter/millimeter astronomical observations are only useful within this PWV range. The PWV estimated from GOES and the Final Analyses (FNL) at APEX for 2007 and 2009 show root mean square error values of 0.23 mm and 0.36 mm over the ranges 0-0.4 mm and 0.4-1.2 mm, respectively. However, absolute relative errors of 51% and 33% were shown over these PWV ranges, respectively. We recommend using high-resolution thermodynamic profiles from the Global Forecast System (GFS) model to estimate the PWV from GOES data since they are available every three hours and at an earlier time than the FNL data. The estimated PWV from GOES/GFS agrees better with the observed PWV at both sites during night time. The largest errors are shown during daytime. Short-term PWV forecasts were implemented at both sites, applying a simple persistence method to the PWV estimated from GOES/GFS. The 12 h and 24 h PWV forecasts evaluated from August to October 2009 indicates that 25% of them show a very good agreement with observations whereas 50% of them show reasonably good agreement with observations. Transmission uncertainties calculated for PWV estimations and forecasts over the studied sites are larger over the range 0-0.4 mm than over the range 0.4-1.2 mm. Thus, the method can be used over the latter interval with more confidence.

  2. Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

    Science.gov (United States)

    Prat, O. P.; Nelson, B. R.

    2014-10-01

    We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets (bias-adjusted TMPA 3B42, near-real time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (± 6%). However, differences at the RFC are more important in particular for near-real time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near real time counterpart 3B42RT. However, large biases remained for 3B42 over the Western US for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in day-1) over the Northwest. Furthermore, the conditional analysis and the contingency analysis conducted illustrated the challenge of retrieving extreme precipitation from remote sensing estimates.

  3. Development of a methodology for probable maximum precipitation estimation over the American River watershed using the WRF model

    Science.gov (United States)

    Tan, Elcin

    A new physically-based methodology for probable maximum precipitation (PMP) estimation is developed over the American River Watershed (ARW) using the Weather Research and Forecast (WRF-ARW) model. A persistent moisture flux convergence pattern, called Pineapple Express, is analyzed for 42 historical extreme precipitation events, and it is found that Pineapple Express causes extreme precipitation over the basin of interest. An average correlation between moisture flux convergence and maximum precipitation is estimated as 0.71 for 42 events. The performance of the WRF model is verified for precipitation by means of calibration and independent validation of the model. The calibration procedure is performed only for the first ranked flood event 1997 case, whereas the WRF model is validated for 42 historical cases. Three nested model domains are set up with horizontal resolutions of 27 km, 9 km, and 3 km over the basin of interest. As a result of Chi-square goodness-of-fit tests, the hypothesis that "the WRF model can be used in the determination of PMP over the ARW for both areal average and point estimates" is accepted at the 5% level of significance. The sensitivities of model physics options on precipitation are determined using 28 microphysics, atmospheric boundary layer, and cumulus parameterization schemes combinations. It is concluded that the best triplet option is Thompson microphysics, Grell 3D ensemble cumulus, and YSU boundary layer (TGY), based on 42 historical cases, and this TGY triplet is used for all analyses of this research. Four techniques are proposed to evaluate physically possible maximum precipitation using the WRF: 1. Perturbations of atmospheric conditions; 2. Shift in atmospheric conditions; 3. Replacement of atmospheric conditions among historical events; and 4. Thermodynamically possible worst-case scenario creation. Moreover, climate change effect on precipitation is discussed by emphasizing temperature increase in order to determine the

  4. Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast

    Directory of Open Access Journals (Sweden)

    Y. H. Lee

    2006-12-01

    Full Text Available In this study, optimal parameter estimations are performed for both physical and computational parameters in a mesoscale meteorological model, and their impacts on the quantitative precipitation forecasting (QPF are assessed for a heavy rainfall case occurred at the Korean Peninsula in June 2005. Experiments are carried out using the PSU/NCAR MM5 model and the genetic algorithm (GA for two parameters: the reduction rate of the convective available potential energy in the Kain-Fritsch (KF scheme for cumulus parameterization, and the Asselin filter parameter for numerical stability. The fitness function is defined based on a QPF skill score. It turns out that each optimized parameter significantly improves the QPF skill. Such improvement is maximized when the two optimized parameters are used simultaneously. Our results indicate that optimizations of computational parameters as well as physical parameters and their adequate applications are essential in improving model performance.

  5. An operational weather radar-based Quantitative Precipitation Estimation and its application in catchment water resources modeling

    DEFF Research Database (Denmark)

    He, Xin; Vejen, Flemming; Stisen, Simon

    2011-01-01

    of precipitation compared with rain-gauge-based methods, thus providing the basis for better water resources assessments. The radar QPE algorithm called ARNE is a distance-dependent areal estimation method that merges radar data with ground surface observations. The method was applied to the Skjern River catchment...... in western Denmark where alternative precipitation estimates were also used as input to an integrated hydrologic model. The hydrologic responses from the model were analyzed by comparing radar- and ground-based precipitation input scenarios. Results showed that radar QPE products are able to generate...... reliable simulations of stream flow and water balance. The potential of using radar-based precipitation was found to be especially high at a smaller scale, where the impact of spatial resolution was evident from the stream discharge results. Also, groundwater recharge was shown to be sensitive...

  6. Using damage data to estimate the risk from summer convective precipitation extremes

    Science.gov (United States)

    Schroeer, Katharina; Tye, Mari

    2017-04-01

    model to test whether the relationship between extreme rainfall events and damages is robust enough to estimate a potential underrepresentation of high intensity rainfall events in ungauged areas. Risk-relevant factors of socio-economic vulnerability, land cover, streamflow data, and weather type information are included to improve and sharpen the analysis. Within this study, we first aim to identify which rainfall events are most damaging and which factors affect the damages - seen as a proxy for the vulnerability - related to summer convective rainfall extremes in different catchment types. Secondly, we aim to detect potentially unreported damaging rainfall events and estimate the likelihood of such cases. We anticipate this damage perspective on summertime extreme convective precipitation to be beneficial for risk assessment, uncertainty management, and decision making with respect to weather and climate extremes on the regional-to-local level.

  7. A Satellite-Based Sunshine Duration Climate Data Record for Europe and Africa

    Directory of Open Access Journals (Sweden)

    Steffen Kothe

    2017-05-01

    Full Text Available Besides 2 m - temperature and precipitation, sunshine duration is one of the most important and commonly used parameter in climatology, with measured time series of partly more than 100 years in length. EUMETSAT’s Satellite Application Facility on Climate Monitoring (CM SAF presents a climate data record for daily and monthly sunshine duration (SDU for Europe and Africa. Basis for the advanced retrieval is a highly resolved satellite product of the direct solar radiation from measurements by Meteosat satellites 2 to 10. The data record covers the time period 1983 to 2015 with a spatial resolution of 0.05° × 0.05°. The comparison against ground-based data shows high agreement but also some regional differences. Sunshine duration is overestimated by the satellite-based data in many regions, compared to surface data. In West and Central Africa, low clouds seem to be the reason for a stronger overestimation of sunshine duration in this region (up to 20% for monthly sums. For most stations, the overestimation is low, with a bias below 7.5 h for monthly sums and below 0.4 h for daily sums. A high correlation of 0.91 for daily SDU and 0.96 for monthly SDU also proved the high agreement with station data. As SDU is based on a stable and homogeneous climate data record of more than 30 years length, it is highly suitable for climate applications, such as trend estimates.

  8. Improving Radar Quantitative Precipitation Estimation over Complex Terrain in the San Francisco Bay Area

    Science.gov (United States)

    Cifelli, R.; Chen, H.; Chandrasekar, V.

    2017-12-01

    A recent study by the State of California's Department of Water Resources has emphasized that the San Francisco Bay Area is at risk of catastrophic flooding. Therefore, accurate quantitative precipitation estimation (QPE) and forecast (QPF) are critical for protecting life and property in this region. Compared to rain gauge and meteorological satellite, ground based radar has shown great advantages for high-resolution precipitation observations in both space and time domain. In addition, the polarization diversity shows great potential to characterize precipitation microphysics through identification of different hydrometeor types and their size and shape information. Currently, all the radars comprising the U.S. National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) network are operating in dual-polarization mode. Enhancement of QPE is one of the main considerations of the dual-polarization upgrade. The San Francisco Bay Area is covered by two S-band WSR-88D radars, namely, KMUX and KDAX. However, in complex terrain like the Bay Area, it is still challenging to obtain an optimal rainfall algorithm for a given set of dual-polarization measurements. In addition, the accuracy of rain rate estimates is contingent on additional factors such as bright band contamination, vertical profile of reflectivity (VPR) correction, and partial beam blockages. This presentation aims to improve radar QPE for the Bay area using advanced dual-polarization rainfall methodologies. The benefit brought by the dual-polarization upgrade of operational radar network is assessed. In addition, a pilot study of gap fill X-band radar performance is conducted in support of regional QPE system development. This paper also presents a detailed comparison between the dual-polarization radar-derived rainfall products with various operational products including the NSSL's Multi-Radar/Multi-Sensor (MRMS) system. Quantitative evaluation of various rainfall products is achieved

  9. Atmospheric water vapor transport: Estimation of continental precipitation recycling and parameterization of a simple climate model. M.S. Thesis

    Science.gov (United States)

    Brubaker, Kaye L.; Entekhabi, Dara; Eagleson, Peter S.

    1991-01-01

    The advective transport of atmospheric water vapor and its role in global hydrology and the water balance of continental regions are discussed and explored. The data set consists of ten years of global wind and humidity observations interpolated onto a regular grid by objective analysis. Atmospheric water vapor fluxes across the boundaries of selected continental regions are displayed graphically. The water vapor flux data are used to investigate the sources of continental precipitation. The total amount of water that precipitates on large continental regions is supplied by two mechanisms: (1) advection from surrounding areas external to the region; and (2) evaporation and transpiration from the land surface recycling of precipitation over the continental area. The degree to which regional precipitation is supplied by recycled moisture is a potentially significant climate feedback mechanism and land surface-atmosphere interaction, which may contribute to the persistence and intensification of droughts. A simplified model of the atmospheric moisture over continents and simultaneous estimates of regional precipitation are employed to estimate, for several large continental regions, the fraction of precipitation that is locally derived. In a separate, but related, study estimates of ocean to land water vapor transport are used to parameterize an existing simple climate model, containing both land and ocean surfaces, that is intended to mimic the dynamics of continental climates.

  10. Application of Statistical Methods of Rain Rate Estimation to Data From The TRMM Precipitation Radar

    Science.gov (United States)

    Meneghini, R.; Jones, J. A.; Iguchi, T.; Okamoto, K.; Liao, L.; Busalacchi, Antonio J. (Technical Monitor)

    2000-01-01

    The TRMM Precipitation Radar is well suited to statistical methods in that the measurements over any given region are sparsely sampled in time. Moreover, the instantaneous rain rate estimates are often of limited accuracy at high rain rates because of attenuation effects and at light rain rates because of receiver sensitivity. For the estimation of the time-averaged rain characteristics over an area both errors are relevant. By enlarging the space-time region over which the data are collected, the sampling error can be reduced. However. the bias and distortion of the estimated rain distribution generally will remain if estimates at the high and low rain rates are not corrected. In this paper we use the TRMM PR data to investigate the behavior of 2 statistical methods the purpose of which is to estimate the rain rate over large space-time domains. Examination of large-scale rain characteristics provides a useful starting point. The high correlation between the mean and standard deviation of rain rate implies that the conditional distribution of this quantity can be approximated by a one-parameter distribution. This property is used to explore the behavior of the area-time-integral (ATI) methods where fractional area above a threshold is related to the mean rain rate. In the usual application of the ATI method a correlation is established between these quantities. However, if a particular form of the rain rate distribution is assumed and if the ratio of the mean to standard deviation is known, then not only the mean but the full distribution can be extracted from a measurement of fractional area above a threshold. The second method is an extension of this idea where the distribution is estimated from data over a range of rain rates chosen in an intermediate range where the effects of attenuation and poor sensitivity can be neglected. The advantage of estimating the distribution itself rather than the mean value is that it yields the fraction of rain contributed by

  11. Identification and uncertainty estimation of vertical reflectivity profiles using a Lagrangian approach to support quantitative precipitation measurements by weather radar

    Science.gov (United States)

    Hazenberg, P.; Torfs, P. J. J. F.; Leijnse, H.; Delrieu, G.; Uijlenhoet, R.

    2013-09-01

    This paper presents a novel approach to estimate the vertical profile of reflectivity (VPR) from volumetric weather radar data using both a traditional Eulerian as well as a newly proposed Lagrangian implementation. For this latter implementation, the recently developed Rotational Carpenter Square Cluster Algorithm (RoCaSCA) is used to delineate precipitation regions at different reflectivity levels. A piecewise linear VPR is estimated for either stratiform or neither stratiform/convective precipitation. As a second aspect of this paper, a novel approach is presented which is able to account for the impact of VPR uncertainty on the estimated radar rainfall variability. Results show that implementation of the VPR identification and correction procedure has a positive impact on quantitative precipitation estimates from radar. Unfortunately, visibility problems severely limit the impact of the Lagrangian implementation beyond distances of 100 km. However, by combining this procedure with the global Eulerian VPR estimation procedure for a given rainfall type (stratiform and neither stratiform/convective), the quality of the quantitative precipitation estimates increases up to a distance of 150 km. Analyses of the impact of VPR uncertainty shows that this aspect accounts for a large fraction of the differences between weather radar rainfall estimates and rain gauge measurements.

  12. Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration

    Directory of Open Access Journals (Sweden)

    Yunjun Yao

    2014-01-01

    Full Text Available Satellite-based vegetation indices (VIs and Apparent Thermal Inertia (ATI derived from temperature change provide valuable information for estimating evapotranspiration (LE and detecting the onset and severity of drought. The modified satellite-based Priestley-Taylor (MS-PT algorithm that we developed earlier, coupling both VI and ATI, is validated based on observed data from 40 flux towers distributed across the world on all continents. The validation results illustrate that the daily LE can be estimated with the Root Mean Square Error (RMSE varying from 10.7 W/m2 to 87.6 W/m2, and with the square of correlation coefficient (R2 from 0.41 to 0.89 (p < 0.01. Compared with the Priestley-Taylor-based LE (PT-JPL algorithm, the MS-PT algorithm improves the LE estimates at most flux tower sites. Importantly, the MS-PT algorithm is also satisfactory in reproducing the inter-annual variability at flux tower sites with at least five years of data. The R2 between measured and predicted annual LE anomalies is 0.42 (p = 0.02. The MS-PT algorithm is then applied to detect the variations of long-term terrestrial LE over Three-North Shelter Forest Region of China and to monitor global land surface drought. The MS-PT algorithm described here demonstrates the ability to map regional terrestrial LE and identify global soil moisture stress, without requiring precipitation information.

  13. Oxygen and Hydrogen Isotopes of Precipitation in a Rocky Mountainous Area of Beijing to Distinguish and Estimate Spring Recharge

    Directory of Open Access Journals (Sweden)

    Ziqiang Liu

    2018-05-01

    Full Text Available Stable isotopes of oxygen and hydrogen were used to estimate seasonal contributions of precipitation to natural spring recharge in Beijing’s mountainous area. Isotopic compositions were shown to be more positive in the dry season and more negative in the wet season, due to the seasonal patterns in the amount of precipitation. The local meteoric water line (LMWL was δ2H = 7.0 δ18O − 2.3 for the dry season and δ2H = 5.9 δ18O − 10.4 for the wet season. LMWL in the two seasons had a lower slope and intercept than the Global Meteoric Water Line (p < 0.01. The slope and intercept of the LMWL in the wet season were lower than that in the dry season because of the effect of precipitation amount during the wet season (p < 0.01. The mean precipitation effects of −15‰ and −2‰ per 100 mm change in the amount of precipitation for δ2H and δ18O, respectively, were obtained from the monthly total precipitation and its average isotopic value. The isotopic composition of precipitation decreased when precipitation duration increased. Little changes in the isotopic composition of the natural spring were found. By employing isotope conservation of mass, it could be derived that, on average, approximately 7.2% of the natural spring came from the dry season precipitation and the rest of 92.8% came from the wet season precipitation.

  14. Satellite-Based actual evapotranspiration over drying semiarid terrain in West-Africa

    NARCIS (Netherlands)

    Schuttemeyer, D.; Schillings, Ch.; Moene, A.F.; Bruin, de H.A.R.

    2007-01-01

    A simple satellite-based algorithm for estimating actual evaporation based on Makkink¿s equation is applied to a seasonal cycle in 2002 at three test sites in Ghana, West Africa: at a location in the humid tropical southern region and two in the drier northern region. The required input for the

  15. The concurrent multiplicative-additive approach for gauge-radar/satellite multisensor precipitation estimates

    Science.gov (United States)

    Garcia-Pintado, J.; Barberá, G. G.; Erena Arrabal, M.; Castillo, V. M.

    2010-12-01

    Objective analysis schemes (OAS), also called ``succesive correction methods'' or ``observation nudging'', have been proposed for multisensor precipitation estimation combining remote sensing data (meteorological radar or satellite) with data from ground-based raingauge networks. However, opposite to the more complex geostatistical approaches, the OAS techniques for this use are not optimized. On the other hand, geostatistical techniques ideally require, at the least, modelling the covariance from the rain gauge data at every time step evaluated, which commonly cannot be soundly done. Here, we propose a new procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) for operational rainfall estimation using rain gauges and meteorological radar, which does not require explicit modelling of spatial covariances. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on the OAS, whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The approach considers radar estimates as background a priori information (first guess), so that nudging to observations (gauges) may be relaxed smoothly to the first guess, and the relaxation shape is obtained from the sequential

  16. Impact of Precipitating Ice Hydrometeors on Longwave Radiative Effect Estimated by a Global Cloud-System Resolving Model

    Science.gov (United States)

    Chen, Ying-Wen; Seiki, Tatsuya; Kodama, Chihiro; Satoh, Masaki; Noda, Akira T.

    2018-02-01

    Satellite observation and general circulation model (GCM) studies suggest that precipitating ice makes nonnegligible contributions to the radiation balance of the Earth. However, in most GCMs, precipitating ice is diagnosed and its radiative effects are not taken into account. Here we examine the longwave radiative impact of precipitating ice using a global nonhydrostatic atmospheric model with a double-moment cloud microphysics scheme. An off-line radiation model is employed to determine cloud radiative effects according to the amount and altitude of each type of ice hydrometeor. Results show that the snow radiative effect reaches 2 W m-2 in the tropics, which is about half the value estimated by previous studies. This effect is strongly dependent on the vertical separation of ice categories and is partially generated by differences in terminal velocities, which are not represented in GCMs with diagnostic precipitating ice. Results from sensitivity experiments that artificially change the categories and altitudes of precipitating ice show that the simulated longwave heating profile and longwave radiation field are sensitive to the treatment of precipitating ice in models. This study emphasizes the importance of incorporating appropriate treatments for the radiative effects of precipitating ice in cloud and radiation schemes in GCMs in order to capture the cloud radiative effects of upper level clouds.

  17. ESTIMATION OF PHASE DELAY DUE TO PRECIPITABLE WATER FOR DINSARBASED LAND DEFORMATION MONITORING

    Directory of Open Access Journals (Sweden)

    J. Susaki

    2017-09-01

    Full Text Available In this paper, we present a method for using the estimated precipitable water (PW to mitigate atmospheric phase delay in order to improve the accuracy of land-deformation assessment with differential interferometric synthetic aperture radar (DInSAR. The phase difference obtained from multi-temporal synthetic aperture radar images contains errors of several types, and the atmospheric phase delay can be an obstacle to estimating surface subsidence. In this study, we calculate PW from external meteorological data. Firstly, we interpolate the data with regard to their spatial and temporal resolutions. Then, assuming a range direction between a target pixel and the sensor, we derive the cumulative amount of differential PW at the height of the slant range vector at pixels along that direction. The atmospheric phase delay of each interferogram is acquired by taking a residual after a preliminary determination of the linear deformation velocity and digital elevation model (DEM error, and by applying high-pass temporal and low-pass spatial filters. Next, we estimate a regression model that connects the cumulative amount of PW and the atmospheric phase delay. Finally, we subtract the contribution of the atmospheric phase delay from the phase difference of the interferogram, and determine the linear deformation velocity and DEM error. The experimental results show a consistent relationship between the cumulative amount of differential PW and the atmospheric phase delay. An improvement in land-deformation accuracy is observed at a point at which the deformation is relatively large. Although further investigation is necessary, we conclude at this stage that the proposed approach has the potential to improve the accuracy of the DInSAR technique.

  18. Using satellite-based measurements to explore ...

    Science.gov (United States)

    New particle formation (NPF) can potentially alter regional climate by increasing aerosol particle (hereafter particle) number concentrations and ultimately cloud condensation nuclei. The large scales on which NPF is manifest indicate potential to use satellite-based (inherently spatially averaged) measurements of atmospheric conditions to diagnose the occurrence of NPF and NPF characteristics. We demonstrate the potential for using satellite-measurements of insolation (UV), trace gas concentrations (sulfur dioxide (SO2), nitrogen dioxide (NO2), ammonia (NH3), formaldehyde (HCHO), ozone (O3)), aerosol optical properties (aerosol optical depth (AOD), Ångström exponent (AE)), and a proxy of biogenic volatile organic compound emissions (leaf area index (LAI), temperature (T)) as predictors for NPF characteristics: formation rates, growth rates, survival probabilities, and ultrafine particle (UFP) concentrations at five locations across North America. NPF at all sites is most frequent in spring, exhibits a one-day autocorrelation, and is associated with low condensational sink (AOD×AE) and HCHO concentrations, and high UV. However, there are important site-to-site variations in NPF frequency and characteristics, and in which of the predictor variables (particularly gas concentrations) significantly contribute to the explanatory power of regression models built to predict those characteristics. This finding may provide a partial explanation for the reported spatia

  19. Estimating the accuracy of the technique of reconstructing the rotational motion of a satellite based on the measurements of its angular velocity and the magnetic field of the Earth

    Science.gov (United States)

    Belyaev, M. Yu.; Volkov, O. N.; Monakhov, M. I.; Sazonov, V. V.

    2017-09-01

    The paper has studied the accuracy of the technique that allows the rotational motion of the Earth artificial satellites (AES) to be reconstructed based on the data of onboard measurements of angular velocity vectors and the strength of the Earth magnetic field (EMF). The technique is based on kinematic equations of the rotational motion of a rigid body. Both types of measurement data collected over some time interval have been processed jointly. The angular velocity measurements have been approximated using convenient formulas, which are substituted into the kinematic differential equations for the quaternion that specifies the transition from the body-fixed coordinate system of a satellite to the inertial coordinate system. Thus obtained equations represent a kinematic model of the rotational motion of a satellite. The solution of these equations, which approximate real motion, has been found by the least-square method from the condition of best fitting between the data of measurements of the EMF strength vector and its calculated values. The accuracy of the technique has been estimated by processing the data obtained from the board of the service module of the International Space Station ( ISS). The reconstruction of station motion using the aforementioned technique has been compared with the telemetry data on the actual motion of the station. The technique has allowed us to reconstruct the station motion in the orbital orientation mode with a maximum error less than 0.6° and the turns with a maximal error of less than 1.2°.

  20. Processing of next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data for the DuPage County streamflow simulation system

    Science.gov (United States)

    Bera, Maitreyee; Ortel, Terry W.

    2018-01-12

    The U.S. Geological Survey, in cooperation with DuPage County Stormwater Management Department, is testing a near real-time streamflow simulation system that assists in the management and operation of reservoirs and other flood-control structures in the Salt Creek and West Branch DuPage River drainage basins in DuPage County, Illinois. As part of this effort, the U.S. Geological Survey maintains a database of hourly meteorological and hydrologic data for use in this near real-time streamflow simulation system. Among these data are next generation weather radar-multisensor precipitation estimates and quantitative precipitation forecast data, which are retrieved from the North Central River Forecasting Center of the National Weather Service. The DuPage County streamflow simulation system uses these quantitative precipitation forecast data to create streamflow predictions for the two simulated drainage basins. This report discusses in detail how these data are processed for inclusion in the Watershed Data Management files used in the streamflow simulation system for the Salt Creek and West Branch DuPage River drainage basins.

  1. Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

    Directory of Open Access Journals (Sweden)

    M. P. Mittermaier

    2008-05-01

    Full Text Available A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used.

    The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

  2. Quantitative Precipitation Estimation over Ocean Using Bayesian Approach from Microwave Observations during the Typhoon Season

    Directory of Open Access Journals (Sweden)

    Jen-Chi Hu

    2009-01-01

    Full Text Available We have developed a new Bayesian approach to retrieve oceanic rain rate from the Tropical Rainfall Measuring Mission (TRMM Microwave Imager (TMI, with an emphasis on typhoon cases in the West Pacific. Retrieved rain rates are validated with measurements of rain gauges located on Japanese islands. To demonstrate improvement, retrievals are also compared with those from the TRMM/Precipitation Radar (PR, the Goddard Profiling Algorithm (GPROF, and a multi-channel linear regression statistical method (MLRS. We have found that qualitatively, all methods retrieved similar horizontal distributions in terms of locations of eyes and rain bands of typhoons. Quantitatively, our new Bayesian retrievals have the best linearity and the smallest root mean square (RMS error against rain gauge data for 16 typhoon over passes in 2004. The correlation coefficient and RMS of our retrievals are 0.95 and ~2 mm hr-1, respectively. In particular, at heavy rain rates, our Bayesian retrievals out perform those retrieved from GPROF and MLRS. Over all, the new Bayesian approach accurately retrieves surface rain rate for typhoon cases. Ac cu rate rain rate estimates from this method can be assimilated in models to improve forecast and prevent potential damages in Taiwan during typhoon seasons.

  3. On-line estimation of the dissolved zinc concentration during ZnS precipitation in a CSTR

    NARCIS (Netherlands)

    Grootscholten, T.I.M.; Keesman, K.J.; Lens, P.N.L.

    2007-01-01

    Abstract In this paper a method is presented to estimate the reaction term of zinc sulphide precipitation and the zinc concentration in a CSTR, using the read-out signal of a sulphide selective electrode. The reaction between zinc and sulphide is described by a non-linear model and therefore

  4. Where Does the Irrigation Water Go? An Estimate of the Contribution of Irrigation to Precipitation Using MERRA

    Science.gov (United States)

    Wei, Jiangfeng; Dirmeyer, Paul A.; Wisser, Dominik; Bosilovich, Michael G.; Mocko, David M.

    2013-01-01

    Irrigation is an important human activity that may impact local and regional climate, but current climate model simulations and data assimilation systems generally do not explicitly include it. The European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) shows more irrigation signal in surface evapotranspiration (ET) than the Modern-Era Retrospective Analysis for Research and Applications (MERRA) because ERA-Interim adjusts soil moisture according to the observed surface temperature and humidity while MERRA has no explicit consideration of irrigation at the surface. But, when compared with the results from a hydrological model with detailed considerations of agriculture, the ET from both reanalyses show large deficiencies in capturing the impact of irrigation. Here, a back-trajectory method is used to estimate the contribution of irrigation to precipitation over local and surrounding regions, using MERRA with observation-based corrections and added irrigation-caused ET increase from the hydrological model. Results show substantial contributions of irrigation to precipitation over heavily irrigated regions in Asia, but the precipitation increase is much less than the ET increase over most areas, indicating that irrigation could lead to water deficits over these regions. For the same increase in ET, precipitation increases are larger over wetter areas where convection is more easily triggered, but the percentage increase in precipitation is similar for different areas. There are substantial regional differences in the patterns of irrigation impact, but, for all the studied regions, the highest percentage contribution to precipitation is over local land.

  5. Comparing the impact of time displaced and biased precipitation estimates for online updated urban runoff models

    DEFF Research Database (Denmark)

    Borup, Morten; Mikkelsen, Peter Steen; Borup, Morten

    2013-01-01

    When an online runoff model is updated from system measurements, the requirements of the precipitation input change. Using rain gauge data as precipitation input there will be a displacement between the time when the rain hits the gauge and the time where the rain hits the actual catchment, due...

  6. Modelling and on-line estimation of zinc sulphide precipitation in

    NARCIS (Netherlands)

    Grootscholten, T.I.M.; Keesman, K.J.; Lens, P.N.L.

    2008-01-01

    In this paper the ZnS precipitation in a continuously stirred tank reactor (CSTR) is modelled using mass balances. The dynamics analysis of the model reveals that the ZnS precipitation shows a two time-scales behaviour with inherent numerical stability problems, which therefore needs special

  7. Spatial estimation of mean temperature and precipitation in areas of scarce meteorological information

    Energy Technology Data Exchange (ETDEWEB)

    Gomez, J.D. [Universidad Autonoma Chapingo, Chapingo (Mexico)]. E-mail: dgomez@correo.chapingo.mx; Etchevers, J.D. [Instituto de Recursos Naturales, Colegio de Postgraduados, Montecillo, Edo. de Mexico (Mexico); Monterroso, A.I. [departamento de Suelos, Universidad Autonoma Chapingo, Chapingo (Mexico); Gay, G. [Centro de Ciencias de la Atmosfera, Universidad Nacional Autonoma de Mexico, Mexico, D.F. (Mexico); Campo, J. [Instituto de Ecologia, Universidad Nacional Autonoma de Mexico, Mexico, D.F. (Mexico); Martinez, M. [Instituto de Recursos Naturales, Montecillo, Edo. de Mexico (Mexico)

    2008-01-15

    In regions of complex relief and scarce meteorological information it becomes difficult to implement techniques and models of numerical interpolation to elaborate reliable maps of climatic variables essential for the study of natural resources using the new tools of the geographic information systems. This paper presents a method for estimating annual and monthly mean values of temperature and precipitation, taking elements from simple interpolation methods and complementing them with some characteristics of more sophisticated methods. To determine temperature, simple linear regression equations were generated associating temperature with altitude of weather stations in the study region, which had been previously subdivided in accordance with humidity conditions and then applying such equations to the area's digital elevation model to obtain temperatures. The estimation of precipitation was based on the graphic method through the analysis of the meteorological systems that affect the regions of the study area throughout the year and considering the influence of mountain ridges on the movement of prevailing winds. Weather stations with data in nearby regions were analyzed according to their position in the landscape, exposure to humid winds, and false color associated with vegetation types. Weather station sites were used to reference the amount of rainfall; interpolation was attained using analogies with satellite images of false color to which a model of digital elevation was incorporated to find similar conditions within the study area. [Spanish] En las regiones de relieve complejo y con escasa informacion meteorologica se dificulta la aplicacion de las diferentes tecnicas y modelos de interpolacion numericos para elaborar mapas de variables climaticas confiables, indispensables para realizar estudios de los recursos naturales, con la utilizacion de las nuevas herramientas de los sistemas de informacion geografica. En este trabajo se presenta un metodo para

  8. Estimating drizzle drop size and precipitation rate using two-colour lidar measurements

    Directory of Open Access Journals (Sweden)

    C. D. Westbrook

    2010-06-01

    Full Text Available A method to estimate the size and liquid water content of drizzle drops using lidar measurements at two wavelengths is described. The method exploits the differential absorption of infrared light by liquid water at 905 nm and 1.5 μm, which leads to a different backscatter cross section for water drops larger than ≈50 μm. The ratio of backscatter measured from drizzle samples below cloud base at these two wavelengths (the colour ratio provides a measure of the median volume drop diameter D0. This is a strong effect: for D0=200 μm, a colour ratio of ≈6 dB is predicted. Once D0 is known, the measured backscatter at 905 nm can be used to calculate the liquid water content (LWC and other moments of the drizzle drop distribution.

    The method is applied to observations of drizzle falling from stratocumulus and stratus clouds. High resolution (32 s, 36 m profiles of D0, LWC and precipitation rate R are derived. The main sources of error in the technique are the need to assume a value for the dispersion parameter μ in the drop size spectrum (leading to at most a 35% error in R and the influence of aerosol returns on the retrieval (≈10% error in R for the cases considered here. Radar reflectivities are also computed from the lidar data, and compared to independent measurements from a colocated cloud radar, offering independent validation of the derived drop size distributions.

  9. Satellite-based annual evaporation estimates of invasive alien plant ...

    African Journals Online (AJOL)

    ... of densely-invaded riparian areas is likely more pronounced. We concluded that the clearing of IAPs by the WFW programme has a positive effect on the availability of water resources through a reduction in ET. Keywords: invasive alien plants; indigenous vegetation; remote sensing; water use; evapotranspiration; SEBAL; ...

  10. Satellite-based estimation of rainfall erosivity for Africa

    NARCIS (Netherlands)

    Vrieling, A.; Sterk, G.; Jong, S.M. de

    2010-01-01

    Rainfall erosivity is a measure for the erosive force of rainfall. Rainfall kinetic energy determines the erosivity and is in turn greatly dependent on rainfall intensity. Attempts for its large-scale mapping are rare. Most are based on interpolation of erosivity values derived from rain gauge

  11. Steps toward a CONUS-wide reanalysis with archived NEXRAD data using National Mosaic and Multisensor Quantitative Precipitation Estimation (NMQ/Q2) algorithms

    Science.gov (United States)

    Stevens, S. E.; Nelson, B. R.; Langston, C.; Qi, Y.

    2012-12-01

    The National Mosaic and Multisensor QPE (NMQ/Q2) software suite, developed at NOAA's National Severe Storms Laboratory (NSSL) in Norman, OK, addresses a large deficiency in the resolution of currently archived precipitation datasets. Current standards, both radar- and satellite-based, provide for nationwide precipitation data with a spatial resolution of up to 4-5 km, with a temporal resolution as fine as one hour. Efforts are ongoing to process archived NEXRAD data for the period of record (1996 - present), producing a continuous dataset providing precipitation data at a spatial resolution of 1 km, on a timescale of only five minutes. In addition, radar-derived precipitation data are adjusted hourly using a wide variety of automated gauge networks spanning the United States. Applications for such a product range widely, from emergency management and flash flood guidance, to hydrological studies and drought monitoring. Results are presented from a subset of the NEXRAD dataset, providing basic statistics on the distribution of rainrates, relative frequency of precipitation types, and several other variables which demonstrate the variety of output provided by the software. Precipitation data from select case studies are also presented to highlight the increased resolution provided by this reanalysis and the possibilities that arise from the availability of data on such fine scales. A previously completed pilot project and steps toward a nationwide implementation are presented along with proposed strategies for managing and processing such a large dataset. Reprocessing efforts span several institutions in both North Carolina and Oklahoma, and data/software coordination are key in producing a homogeneous record of precipitation to be archived alongside NOAA's other Climate Data Records. Methods are presented for utilizing supercomputing capability in expediting processing, to allow for the iterative nature of a reanalysis effort.

  12. Ranking GCM Estimates of Twentieth Century Precipitation Seasonality in the Western U.S. and its Influence on Floristic Provinces.

    Science.gov (United States)

    Cole, K. L.; Eischeid, J. K.; Garfin, G. M.; Ironside, K.; Cobb, N. S.

    2008-12-01

    Floristic provinces of the western United States (west of 100W) can be segregated into three regions defined by significant seasonal precipitation during the months of: 1) November-March (Mediterranean); 2) July- September (Monsoonal); or, 3) May-June (Rocky Mountain). This third region is best defined by the absence of the late spring-early summer drought that affects regions 1 and 2. Each of these precipitation regimes is characterized by distinct vegetation types and fire seasonality adapted to that particular cycle of seasonal moisture availability and deficit. Further, areas where these regions blend from one to another can support even more complex seasonal patterns and resulting distinctive vegetation types. As a result, modeling the effects of climates on these ecosystems requires confidence that GCMs can at least approximate these sub- continental seasonal precipitation patterns. We evaluated the late Twentieth Century (1950-1999 AD) estimates of annual precipitation seasonality produced by 22 GCMs contained within the IPCC Fourth Assessment (AR4). These modeled estimates were compared to values from the PRISM dataset, extrapolated from station data, over the same historical period for the 3 seasonal periods defined above. The correlations between GCM estimates and PRISM values were ranked using 4 measures: 1) A map pattern relationship based on the correlation coefficient, 2) A map pattern relationship based on the congruence coefficient, 3) The ratio of simulated/observed area averaged precipitation based on the seasonal precipitation amounts, and, 4) The ratio of simulated/observed area averaged precipitation based on the seasonal precipitation percentages of the annual total. For each of the four metrics, the rank order of models was very similar. The ranked order of the performance of the different models quantified aspects of the model performance visible in the mapped results. While some models represented the seasonal patterns very well, others

  13. Evaluating the hydrological consistency of satellite based water cycle components

    KAUST Repository

    Lopez Valencia, Oliver Miguel

    2016-06-15

    Advances in multi-satellite based observations of the earth system have provided the capacity to retrieve information across a wide-range of land surface hydrological components and provided an opportunity to characterize terrestrial processes from a completely new perspective. Given the spatial advantage that space-based observations offer, several regional-to-global scale products have been developed, offering insights into the multi-scale behaviour and variability of hydrological states and fluxes. However, one of the key challenges in the use of satellite-based products is characterizing the degree to which they provide realistic and representative estimates of the underlying retrieval: that is, how accurate are the hydrological components derived from satellite observations? The challenge is intrinsically linked to issues of scale, since the availability of high-quality in-situ data is limited, and even where it does exist, is generally not commensurate to the resolution of the satellite observation. Basin-scale studies have shown considerable variability in achieving water budget closure with any degree of accuracy using satellite estimates of the water cycle. In order to assess the suitability of this type of approach for evaluating hydrological observations, it makes sense to first test it over environments with restricted hydrological inputs, before applying it to more hydrological complex basins. Here we explore the concept of hydrological consistency, i.e. the physical considerations that the water budget impose on the hydrologic fluxes and states to be temporally and spatially linked, to evaluate the reproduction of a set of large-scale evaporation (E) products by using a combination of satellite rainfall (P) and Gravity Recovery and Climate Experiment (GRACE) observations of storage change, focusing on arid and semi-arid environments, where the hydrological flows can be more realistically described. Our results indicate no persistent hydrological

  14. Disdrometer-based C-Band Radar Quantitative Precipitation Estimation (QPE) in a highly complex terrain region in tropical Colombia.

    Science.gov (United States)

    Sepúlveda, J.; Hoyos Ortiz, C. D.

    2017-12-01

    An adequate quantification of precipitation over land is critical for many societal applications including agriculture, hydroelectricity generation, water supply, and risk management associated with extreme events. The use of rain gauges, a traditional method for precipitation estimation, and an excellent one, to estimate the volume of liquid water during a particular precipitation event, does not allow to fully capture the highly spatial variability of the phenomena which is a requirement for almost all practical applications. On the other hand, the weather radar, an active remote sensing sensor, provides a proxy for rainfall with fine spatial resolution and adequate temporary sampling, however, it does not measure surface precipitation. In order to fully exploit the capabilities of the weather radar, it is necessary to develop quantitative precipitation estimation (QPE) techniques combining radar information with in-situ measurements. Different QPE methodologies are explored and adapted to local observations in a highly complex terrain region in tropical Colombia using a C-Band radar and a relatively dense network of rain gauges and disdrometers. One important result is that the expressions reported in the literature for extratropical locations are not representative of the conditions found in the tropical region studied. In addition to reproducing the state-of-the-art techniques, a new multi-stage methodology based on radar-derived variables and disdrometer data is proposed in order to achieve the best QPE possible. The main motivation for this new methodology is based on the fact that most traditional QPE methods do not directly take into account the different uncertainty sources involved in the process. The main advantage of the multi-stage model compared to traditional models is that it allows assessing and quantifying the uncertainty in the surface rain rate estimation. The sub-hourly rainfall estimations using the multi-stage methodology are realistic

  15. The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present)

    Science.gov (United States)

    Adler, Robert F.; Huffman, George J.; Chang, Alfred; Ferraro, Ralph; Xie, Ping-Ping; Janowiak, John; Rudolf, Bruno; Schneider, Udo; Curtis, Scott; Bolvin, David

    2003-01-01

    The Global Precipitation Climatology Project (GPCP) Version 2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5 degrees x 2.5 degrees latitude-longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The data set is extended back into the premicrowave era (before 1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the raingauge analysis. This monthly analysis is the foundation for the GPCP suite of products including those at finer temporal resolution, satellite estimate, and error estimates for each field. The 23-year GPCP climatology is characterized, along with time and space variations of precipitation.

  16. Satellite-based Tropical Cyclone Monitoring Capabilities

    Science.gov (United States)

    Hawkins, J.; Richardson, K.; Surratt, M.; Yang, S.; Lee, T. F.; Sampson, C. R.; Solbrig, J.; Kuciauskas, A. P.; Miller, S. D.; Kent, J.

    2012-12-01

    Satellite remote sensing capabilities to monitor tropical cyclone (TC) location, structure, and intensity have evolved by utilizing a combination of operational and research and development (R&D) sensors. The microwave imagers from the operational Defense Meteorological Satellite Program [Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS)] form the "base" for structure observations due to their ability to view through upper-level clouds, modest size swaths and ability to capture most storm structure features. The NASA TRMM microwave imager and precipitation radar continue their 15+ yearlong missions in serving the TC warning and research communities. The cessation of NASA's QuikSCAT satellite after more than a decade of service is sorely missed, but India's OceanSat-2 scatterometer is now providing crucial ocean surface wind vectors in addition to the Navy's WindSat ocean surface wind vector retrievals. Another Advanced Scatterometer (ASCAT) onboard EUMETSAT's MetOp-2 satellite is slated for launch soon. Passive microwave imagery has received a much needed boost with the launch of the French/Indian Megha Tropiques imager in September 2011, basically greatly supplementing the very successful NASA TRMM pathfinder with a larger swath and more frequent temporal sampling. While initial data issues have delayed data utilization, current news indicates this data will be available in 2013. Future NASA Global Precipitation Mission (GPM) sensors starting in 2014 will provide enhanced capabilities. Also, the inclusion of the new microwave sounder data from the NPP ATMS (Oct 2011) will assist in mapping TC convective structures. The National Polar orbiting Partnership (NPP) program's VIIRS sensor includes a day night band (DNB) with the capability to view TC cloud structure at night when sufficient lunar illumination exits. Examples highlighting this new capability will be discussed in concert with additional data fusion efforts.

  17. Precipitation evidences on X-Band Synthetic Aperture Radar imagery: an approach for quantitative detection and estimation

    Science.gov (United States)

    Mori, Saverio; Marzano, Frank S.; Montopoli, Mario; Pulvirenti, Luca; Pierdicca, Nazzareno

    2017-04-01

    al. 2014 and Mori et al. 2012); ancillary data, such as local incident angle and land cover, are used. This stage is necessary to tune the precipitation map stage and to avoid severe misinterpretations on the precipitation map routines. The second stage consist of estimating the local cloud attenuation. Finally the precipitation map is estimated, using the the retrieval algorithm developed by Marzano et al. (2011), applied only to pixels where rain is known to be present. Within the FP7 project EartH2Observe we have applied this methodology to 14 study cases, acquired within TSX and CSK missions over Italy and United States. This choice allows analysing both hurricane-like intense events and continental mid-latitude precipitations, with the possibility to verify and validate the proposed methodology through the available weather radar networks. Moreover it allows in same extent analysing the contribution of orography and quality of ancillary data (i.e. landcover). In this work we will discuss the results obtained until now in terms of improved rain cell localization and precipitation quantification.

  18. Systematical estimation of GPM-based global satellite mapping of precipitation products over China

    Science.gov (United States)

    Zhao, Haigen; Yang, Bogang; Yang, Shengtian; Huang, Yingchun; Dong, Guotao; Bai, Juan; Wang, Zhiwei

    2018-03-01

    As the Global Precipitation Measurement (GPM) Core Observatory satellite continues its mission, new version 6 products for Global Satellite Mapping of Precipitation (GSMaP) have been released. However, few studies have systematically evaluated the GSMaP products over mainland China. This study quantitatively evaluated three GPM-based GSMaP version 6 precipitation products for China and eight subregions referring to the Chinese daily Precipitation Analysis Product (CPAP). The GSMaP products included near-real-time (GSMaP_NRT), microwave-infrared reanalyzed (GSMaP_MVK), and gauge-adjusted (GSMaP_Gau) data. Additionally, the gauge-adjusted Integrated Multi-Satellite Retrievals for Global Precipitation Measurement Mission (IMERG_Gau) was also assessed and compared with GSMaP_Gau. The analyses of the selected daily products were carried out at spatiotemporal resolutions of 1/4° for the period of March 2014 to December 2015 in consideration of the resolution of CPAP and the consistency of the coverage periods of the satellite products. The results indicated that GSMaP_MVK and GSMaP_NRT performed comparably and underdetected light rainfall events (Pearson linear correlation coefficient (CC), fractional standard error (FSE), and root-mean-square error (RMSE) metrics during the summer. Compared with GSMaP_NRT and GSMaP_MVK, GSMaP_Gau possessed significantly improved metrics over mainland China and the eight subregions and performed better in terms of CC, RMSE, and FSE but underestimated precipitation to a greater degree than IMERG_Gau. As a quantitative assessment of the GPM-era GSMaP products, these validation results will supply helpful references for both end users and algorithm developers. However, the study findings need to be confirmed over a longer future study period when the longer-period IMERG retrospectively-processed data are available.

  19. Does GPM-based multi-satellite precipitation enhance rainfall estimates over Pakistan and Bolivia arid regions?

    Science.gov (United States)

    Hussain, Y.; Satgé, F.; Bonnet, M. P.; Pillco, R.; Molina, J.; Timouk, F.; Roig, H.; Martinez-Carvajal, H., Sr.; Gulraiz, A.

    2016-12-01

    Arid regions are sensitive to rainfall variations which are expressed in the form of flooding and droughts. Unfortunately, those regions are poorly monitored and high quality rainfall estimates are still needed. The Global Precipitation Measurement (GPM) mission released two new satellite rainfall products named Integrated Multisatellite Retrievals GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) bringing the possibility of accurate rainfall monitoring over these countries. This study assessed both products at monthly scale over Pakistan considering dry and wet season over the 4 main climatic zones from 2014 to 2016. With similar climatic conditions, the Altiplano region of Bolivia is considered to quantify the influence of big lakes (Titicaca and Poopó) in rainfall estimates. For comparison, the widely used TRMM-Multisatellite Precipitation Analysis 3B43 (TMPA-3B43) version 7 is also involved in the analysis to observe the potential enhancement in rainfall estimate brought by GPM products. Rainfall estimates derived from 110 rain-gauges are used as reference to compare IMERG, GSMaP-v6 and TMPA-3B43 at the 0.1° and 0.25° spatial resolution. Over both regions, IMERG and GSMaP-v6 capture the spatial pattern of precipitation as well as TMPA-3B43. All products tend to over estimates rainfall over very arid regions. This feature is even more marked during dry season. However, during this season, both reference and estimated rainfall remain very low and do not impact seasonal water budget computation. On a general way, IMERG slightly outperforms TMPA-3B43 and GSMaP-v6 which provides the less accurate rainfall estimate. The TMPA-3B43 rainfall underestimation previously found over Lake Titicaca is still observed in IMERG estimates. However, GSMaP-v6 considerably decreases the underestimation providing the most accurate rainfall estimate over the lake. MOD11C3 Land Surface Temperature (LST) and ASTER Global Emissivity Dataset reveal strong

  20. ASSESSMENT OF SATELLITE PRECIPITATION PRODUCTS IN THE PHILIPPINE ARCHIPELAGO

    Directory of Open Access Journals (Sweden)

    M. D. Ramos

    2016-06-01

    Full Text Available Precipitation is the most important weather parameter in the Philippines. Made up of more than 7100 islands, the Philippine archipelago is an agricultural country that depends on rain-fed crops. Located in the western rim of the North West Pacific Ocean, this tropical island country is very vulnerable to tropical cyclones that lead to severe flooding events. Recently, satellite-based precipitation estimates have improved significantly and can serve as alternatives to ground-based observations. These data can be used to fill data gaps not only for climatic studies, but can also be utilized for disaster risk reduction and management activities. This study characterized the statistical errors of daily precipitation from four satellite-based rainfall products from (1 the Tropical Rainfall Measuring Mission (TRMM, (2 the CPC Morphing technique (CMORPH of NOAA and (3 the Global Satellite Mapping of Precipitation (GSMAP and (4 Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks (PERSIANN. Precipitation data were compared to 52 synoptic weather stations located all over the Philippines. Results show GSMAP to have over all lower bias and CMORPH with lowest Mean Absolute Error (MAE and Root Mean Square Error (RMSE. In addition, a dichotomous rainfall test reveals GSMAP and CMORPH have low Proportion Correct (PC for convective and stratiform rainclouds, respectively. TRMM consistently showed high PC for almost all raincloud types. Moreover, all four satellite precipitation showed high Correct Negatives (CN values for the north-western part of the country during the North-East monsoon and spring monsoonal transition periods.

  1. New 2012 precipitation frequency estimation analysis for Alaska : musings on data used and the final product.

    Science.gov (United States)

    2013-06-01

    The major product of this study was a precipitation frequency atlas for the entire state of Alaska; this atlas is available at : http://dipper.nws.noaa.gov/hdsc/pfds/. The process of contributing to this study provided an opportunity to (1) evaluate ...

  2. A model for estimating understory vegetation response to fertilization and precipitation in loblolly pine plantations

    Science.gov (United States)

    Curtis L. VanderSchaaf; Ryan W. McKnight; Thomas R. Fox; H. Lee Allen

    2010-01-01

    A model form is presented, where the model contains regressors selected for inclusion based on biological rationale, to predict how fertilization, precipitation amounts, and overstory stand density affect understory vegetation biomass. Due to time, economic, and logistic constraints, datasets of large sample sizes generally do not exist for understory vegetation. Thus...

  3. Estimates of run off, evaporation and precipitation for the Bay of Bengal on seasonal basis

    Digital Repository Service at National Institute of Oceanography (India)

    Varkey, M.J.; Sastry, J.S.

    Mean seasonal river discharge rates (R) of the major rivers along the east coast of India, Bangla Desh and Burma; evaporation rates (E) computed for 5 degrees lat-long. Squares from data on heat loss and mean yearly precipitation (P) values at 5...

  4. GPM SLH: Convective Latent Heating Estimated with GPM Dual-frequency Precipitation Radar Data

    Science.gov (United States)

    Takayabu, Y. N.; Hamada, A.; Yokoyama, C.; Ikuta, Y.; Shige, S.; Yamaji, M.; Kubota, T.

    2017-12-01

    Three dimensional diabatic heating distribution plays essential roles to determine large-scale circulation, as well as to generate mesoscale circulation associated with tropical convection (e.g. Hartmann et al., 1984; Houze et al. 1982). For mid-latitude systems also, diabatic heating contributes to generate PVs resulting in, for example, explosive intensifications of mid-lattitude storms (Boettcher and Wernli, 2011). Previously, with TRMM PR data, we developed a Spectral Latent Heating algorithm (SLH; Shige et al. 2004, etc.) for 36N-36S region. It was based on the spectral LH tables produced from a simulation utilizing the Goddard Cloud Ensemble Model forced with the TOGA-COARE data. With GPM DPR, the observation region is extended to 65N-65S. Here, we introduce a new version of SLH algorithm which is applicable also to the mid-latitude precipitation. A new global GPM SLH ver.5 product is released as one of NASA/JAXA GPM standard products on July 11, 2017. For GPM SLH mid-latitude algorithm, we employ the Japan Meteorological Agency (JMA)'s high resolution (horizontally 2km) Local Forecast Model (LFM) to construct the LUTs. With collaborations of JMA's forecast group, forecast data for 8 extratropical cyclone cases are collected and utilized. For mid-latitude precipitation, we have to deal with large temperature gradients and complex relationship between the freezing level and cloud base levels. LUTs are constructed for LH, Q1-QR, and Q2 (Yanai et al. 1973), for six different precipitation types: Convective and shallow stratiform LUTs are made against precipitation top heights. For deep stratiform and other precipitation, LUTs are made against maximum precipitation to handle the unknown cloud-bases. Finally, three-dimensional convective latent heating is retrieved, utilizing the LUTs and precipitation profile data from GPM 2AKu. We can confirm that retrieved LH looks very similar to simulated LH, for a consistency check. We also confirm a good continuities of

  5. New method to estimate paleoprecipitation using fossil amphibians and reptiles and the middle and late Miocene precipitation gradients in Europe

    Science.gov (United States)

    Böhme, M.; Ilg, A.; Ossig, A.; Küchenhoff, H.

    2006-06-01

    Existing methods for determining paleoprecipitation are subject to large errors (±350 400 mm or more using mammalian proxies), or are restricted to wet climate systems due to their strong facies dependence (paleobotanical proxies). Here we describe a new paleoprecipitation tool based on an indexing of ecophysiological groups within herpetological communities. In recent communities these indices show a highly significant correlation to annual precipitation (r2 = 0.88), and yield paleoprecipitation estimates with average errors of ±250 280 mm. The approach was validated by comparison with published paleoprecipitation estimates from other methods. The method expands the application of paleoprecipitation tools to dry climate systems and in this way contributes to the establishment of a more comprehensive paleoprecipitation database. This method is applied to two high-resolution time intervals from the European Neogene: the early middle Miocene (early Langhian) and the early late Miocene (early Tortonian). The results indicate that both periods show significant meridional precipitation gradients in Europe, these being stronger in the early Langhian (threefold decrease toward the south) than in the early Tortonian (twofold decrease toward the south). This pattern indicates a strengthening of climatic belts during the middle Miocene climatic optimum due to Southern Hemisphere cooling and an increased contribution of Arctic low-pressure cells to the precipitation from the late Miocene onward due to Northern Hemisphere cooling.

  6. Evaluation of satellite-retrieved extreme precipitation using gauge observations

    Science.gov (United States)

    Lockhoff, M.; Zolina, O.; Simmer, C.; Schulz, J.

    2012-04-01

    Precipitation extremes have already been intensively studied employing rain gauge datasets. Their main advantage is that they represent a direct measurement with a relatively high temporal coverage. Their main limitation however is their poor spatial coverage and thus a low representativeness in many parts of the world. In contrast, satellites can provide global coverage and there are meanwhile data sets available that are on one hand long enough to be used for extreme value analysis and that have on the other hand the necessary spatial and temporal resolution to capture extremes. However, satellite observations provide only an indirect mean to determine precipitation and there are many potential observational and methodological weaknesses in particular over land surfaces that may constitute doubts concerning their usability for the analysis of precipitation extremes. By comparing basic climatological metrics of precipitation (totals, intensities, number of wet days) as well as respective characteristics of PDFs, absolute and relative extremes of satellite and observational data this paper aims at assessing to which extent satellite products are suitable for analysing extreme precipitation events. In a first step the assessment focuses on Europe taking into consideration various satellite products available, e.g. data sets provided by the Global Precipitation Climatology Project (GPCP). First results indicate that satellite-based estimates do not only represent the monthly averaged precipitation very similar to rain gauge estimates but they also capture the day-to-day occurrence fairly well. Larger differences can be found though when looking at the corresponding intensities.

  7. Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

    Directory of Open Access Journals (Sweden)

    Hans-Stefan Bauer

    2015-04-01

    Full Text Available Quantitative precipitation estimation and forecasting (QPE and QPF are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale band-like precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data.

  8. Evaluating the hydrological consistency of evaporation products using satellite-based gravity and rainfall data

    Science.gov (United States)

    López, Oliver; Houborg, Rasmus; McCabe, Matthew Francis

    2017-01-01

    Advances in space-based observations have provided the capacity to develop regional- to global-scale estimates of evaporation, offering insights into this key component of the hydrological cycle. However, the evaluation of large-scale evaporation retrievals is not a straightforward task. While a number of studies have intercompared a range of these evaporation products by examining the variance amongst them, or by comparison of pixel-scale retrievals against ground-based observations, there is a need to explore more appropriate techniques to comprehensively evaluate remote-sensing-based estimates. One possible approach is to establish the level of product agreement between related hydrological components: for instance, how well do evaporation patterns and response match with precipitation or water storage changes? To assess the suitability of this consistency-based approach for evaluating evaporation products, we focused our investigation on four globally distributed basins in arid and semi-arid environments, comprising the Colorado River basin, Niger River basin, Aral Sea basin, and Lake Eyre basin. In an effort to assess retrieval quality, three satellite-based global evaporation products based on different methodologies and input data, including CSIRO-PML, the MODIS Global Evapotranspiration product (MOD16), and Global Land Evaporation: the Amsterdam Methodology (GLEAM), were evaluated against rainfall data from the Global Precipitation Climatology Project (GPCP) along with Gravity Recovery and Climate Experiment (GRACE) water storage anomalies. To ensure a fair comparison, we evaluated consistency using a degree correlation approach after transforming both evaporation and precipitation data into spherical harmonics. Overall we found no persistent hydrological consistency in these dryland environments. Indeed, the degree correlation showed oscillating values between periods of low and high water storage changes, with a phase difference of about 2-3 months

  9. Test particle modeling of wave-induced energetic electron precipitation

    International Nuclear Information System (INIS)

    Chang, H.C.; Inan, U.S.

    1985-01-01

    A test particle computer model of the precipitation of radiation belt electrons is extended to compute the dynamic energy spectrum of transient electron fluxes induced by short-duration VLF wave packets traveling along the geomagnetic field lines. The model is adapted to estimate the count rate and associated spectrum of precipitated electrons that would be observed by satellite-based particle detectors with given geometric factor and orientation with respect to the magnetic field. A constant-frequency wave pulse and a lightning-induced whistler wave packet are used as examples of the stimulating wave signals. The effects of asymmetry of particle mirror heights in the two hemispheres and the atmospheric backscatter of loss cone particles on the computed precipitated fluxes are discussed

  10. Merging Radar Quantitative Precipitation Estimates (QPEs) from the High-resolution NEXRAD Reanalysis over CONUS with Rain-gauge Observations

    Science.gov (United States)

    Prat, O. P.; Nelson, B. R.; Stevens, S. E.; Nickl, E.; Seo, D. J.; Kim, B.; Zhang, J.; Qi, Y.

    2015-12-01

    The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (Nexrad) network over the Continental United States (CONUS) is completed for the period covering from 2002 to 2011. While this constitutes a unique opportunity to study precipitation processes at higher resolution than conventionally possible (1-km, 5-min), the long-term radar-only product needs to be merged with in-situ information in order to be suitable for hydrological, meteorological and climatological applications. The radar-gauge merging is performed by using rain gauge information at daily (Global Historical Climatology Network-Daily: GHCN-D), hourly (Hydrometeorological Automated Data System: HADS), and 5-min (Automated Surface Observing Systems: ASOS; Climate Reference Network: CRN) resolution. The challenges related to incorporating differing resolution and quality networks to generate long-term large-scale gridded estimates of precipitation are enormous. In that perspective, we are implementing techniques for merging the rain gauge datasets and the radar-only estimates such as Inverse Distance Weighting (IDW), Simple Kriging (SK), Ordinary Kriging (OK), and Conditional Bias-Penalized Kriging (CBPK). An evaluation of the different radar-gauge merging techniques is presented and we provide an estimate of uncertainty for the gridded estimates. In addition, comparisons with a suite of lower resolution QPEs derived from ground based radar measurements (Stage IV) are provided in order to give a detailed picture of the improvements and remaining challenges.

  11. Rain cell-based identification of the vertical profile of reflectivity as observed by weather radar and its use for precipitation uncertainty estimation

    Science.gov (United States)

    Hazenberg, P.; Torfs, P. J. J. F.; Leijnse, H.; Uijlenhoet, R.

    2012-04-01

    The wide scale implementation of weather radar systems over the last couple of decades has increased our understanding concerning spatio-temporal precipitation dynamics. However, the quantitative estimation of precipitation by these devices is affected by many sources of error. A very dominant source of error results from vertical variations in the hydrometeor size distribution known as the vertical profile of reflectivity (VPR). Since the height of the measurement as well as the beam volume increases with distance from the radar, for stratiform precipitation this results in a serious underestimation (overestimation) of the surface reflectivity while sampling within the snow (bright band) region. This research presents a precipitation cell-based implementation to correct volumetric weather radar measurements for VPR effects. Using the properties of a flipping carpenter square, a contour-based identification technique was developed, which is able to identify and track precipitation cells in real time, distinguishing between convective, stratiform and undefined precipitation. For the latter two types of systems, for each individual cell, a physically plausible vertical profile of reflectivity is estimated using a Monte Carlo optimization method. Since it can be expected that the VPR will vary within a given precipitation cell, a method was developed to take the uncertainty of the VPR estimate into account. As a result, we are able to estimate the amount of precipitation uncertainty as observed by weather radar due to VPR for a given precipitation type and storm cell. We demonstrate the possibilities of this technique for a number of winter precipitation systems observed within the Belgian Ardennes. For these systems, in general, the precipitation uncertainty estimate due to vertical reflectivity profile variations varies between 10-40%.

  12. Satellite-based monitoring of cotton evapotranspiration

    Science.gov (United States)

    Dalezios, Nicolas; Dercas, Nicholas; Tarquis, Ana Maria

    2016-04-01

    Water for agricultural use represents the largest share among all water uses. Vulnerability in agriculture is influenced, among others, by extended periods of water shortage in regions exposed to droughts. Advanced technological approaches and methodologies, including remote sensing, are increasingly incorporated for the assessment of irrigation water requirements. In this paper, remote sensing techniques are integrated for the estimation and monitoring of crop evapotranspiration ETc. The study area is Thessaly central Greece, which is a drought-prone agricultural region. Cotton fields in a small agricultural sub-catchment in Thessaly are used as an experimental site. Daily meteorological data and weekly field data are recorded throughout seven (2004-2010) growing seasons for the computation of reference evapotranspiration ETo, crop coefficient Kc and cotton crop ETc based on conventional data. Satellite data (Landsat TM) for the corresponding period are processed to estimate cotton crop coefficient Kc and cotton crop ETc and delineate its spatiotemporal variability. The methodology is applied for monitoring Kc and ETc during the growing season in the selected sub-catchment. Several error statistics are used showing very good agreement with ground-truth observations.

  13. Spatio-Temporal Analysis of the Accuracy of Tropical Multisatellite Precipitation Analysis 3B42 Precipitation Data in Mid-High Latitudes of China

    Science.gov (United States)

    Cai, Yancong; Jin, Changjie; Wang, Anzhi; Guan, Dexin; Wu, Jiabing; Yuan, Fenghui; Xu, Leilei

    2015-01-01

    Satellite-based precipitation data have contributed greatly to quantitatively forecasting precipitation, and provides a potential alternative source for precipitation data allowing researchers to better understand patterns of precipitation over ungauged basins. However, the absence of calibration satellite data creates considerable uncertainties for The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product over high latitude areas beyond the TRMM satellites latitude band (38°NS). This study attempts to statistically assess TMPA V7 data over the region beyond 40°NS using data obtained from numerous weather stations in 1998–2012. Comparative analysis at three timescales (daily, monthly and annual scale) indicates that adoption of a monthly adjustment significantly improved correlation at a larger timescale increasing from 0.63 to 0.95; TMPA data always exhibits a slight overestimation that is most serious at a daily scale (the absolute bias is 103.54%). Moreover, the performance of TMPA data varies across all seasons. Generally, TMPA data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. Temporal and spatial analysis of accuracy indices suggest that the performance of TMPA data has gradually improved and has benefited from upgrades; the data are more reliable in humid areas than in arid regions. Special attention should be paid to its application in arid areas and in winter with poor scores of accuracy indices. Also, it is clear that the calibration can significantly improve precipitation estimates, the overestimation by TMPA in TRMM-covered area is about a third as much as that in no-TRMM area for monthly and annual precipitation. The systematic evaluation of TMPA over mid-high latitudes provides a broader understanding of satellite-based precipitation estimates, and these data are

  14. Spatio-temporal analysis of the accuracy of tropical multisatellite precipitation analysis 3B42 precipitation data in mid-high latitudes of China.

    Directory of Open Access Journals (Sweden)

    Yancong Cai

    Full Text Available Satellite-based precipitation data have contributed greatly to quantitatively forecasting precipitation, and provides a potential alternative source for precipitation data allowing researchers to better understand patterns of precipitation over ungauged basins. However, the absence of calibration satellite data creates considerable uncertainties for The Tropical Rainfall Measuring Mission (TRMM Multisatellite Precipitation Analysis (TMPA 3B42 product over high latitude areas beyond the TRMM satellites latitude band (38°NS. This study attempts to statistically assess TMPA V7 data over the region beyond 40°NS using data obtained from numerous weather stations in 1998-2012. Comparative analysis at three timescales (daily, monthly and annual scale indicates that adoption of a monthly adjustment significantly improved correlation at a larger timescale increasing from 0.63 to 0.95; TMPA data always exhibits a slight overestimation that is most serious at a daily scale (the absolute bias is 103.54%. Moreover, the performance of TMPA data varies across all seasons. Generally, TMPA data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. Temporal and spatial analysis of accuracy indices suggest that the performance of TMPA data has gradually improved and has benefited from upgrades; the data are more reliable in humid areas than in arid regions. Special attention should be paid to its application in arid areas and in winter with poor scores of accuracy indices. Also, it is clear that the calibration can significantly improve precipitation estimates, the overestimation by TMPA in TRMM-covered area is about a third as much as that in no-TRMM area for monthly and annual precipitation. The systematic evaluation of TMPA over mid-high latitudes provides a broader understanding of satellite-based precipitation estimates, and these

  15. Performance evaluation of latest integrated multi-satellite retrievals for Global Precipitation Measurement (IMERG) over the northern highlands of Pakistan

    Science.gov (United States)

    Anjum, Muhammad Naveed; Ding, Yongjian; Shangguan, Donghui; Ahmad, Ijaz; Ijaz, Muhammad Wajid; Farid, Hafiz Umar; Yagoub, Yousif Elnour; Zaman, Muhammad; Adnan, Muhammad

    2018-06-01

    Recently, the Global Precipitation Measurement (GPM) mission has released the Integrated Multi-satellite Retrievals for GPM (IMERG) at a fine spatial (0.1° × 0.1°) and temporal (half hourly) resolutions. A comprehensive evaluation of this newly launched precipitation product is very important for satellite-based precipitation data users as well as for algorithm developers. The objective of this study was to provide a preliminary and timely performance evaluation of the IMERG product over the northern high lands of Pakistan. For comparison reference, the real-time and post real-time Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products were also evaluated parallel to the IMERG. All of the selected precipitation products were evaluated at annual, monthly, seasonal and daily time scales using reference gauges data from April 2014 to December 2016. The results showed that: (1) the precipitation estimates from IMERG, 3B42V7 and 3B42RT products correlated well with the reference gauges observations at monthly time scale (CC = 0.93, 0.91, 0.88, respectively), whereas moderately at the daily time scale (CC = 0.67, 0.61, and 0.58, respectively); (2) Compared to the 3B42V7 and 3B42RT, the precipitation estimates from IMERG were more reliable in all seasons particularly in the winter season with lowest relative bias (2.61%) and highest CC (0.87); (3) IMERG showed a clear superiority over 3B42V7 and 3B42RT products in order to capture spatial distribution of precipitation over the northern Pakistan; (4) Relative to the 3B42V7 and 3B42RT, daily precipitation estimates from IMEREG showed lowest relative bias (9.20% vs. 21.40% and 26.10%, respectively) and RMSE (2.05 mm/day vs. 2.49 mm/day and 2.88 mm/day, respectively); and (5) Light precipitation events (0-1 mm/day) were usually overestimated by all said satellite-based precipitation products. In contrast moderate (1-20 mm/day) to heavy (>20 mm/day) precipitation events were

  16. Combining C- and X-band Weather Radars for Improving Precipitation Estimates over Urban Areas

    DEFF Research Database (Denmark)

    Nielsen, Jesper Ellerbæk

    of future system state. Accurate and reliable weather radar measurements are, therefore, important for future developments and achievements within urban drainage. This PhD study investigates two types of weather radars. Both systems are in operational use in Denmark today. A network of meteorological C...... individually and owned by local water utility companies. Although the two radar systems use similar working principles, the systems have significant differences regarding technology, temporal resolution, spatial resolution, range and scanning strategy. The focus of the research was to combine the precipitation...

  17. Comparing the impact of time displaced and biased precipitation estimates for online updated urban runoff models.

    Science.gov (United States)

    Borup, Morten; Grum, Morten; Mikkelsen, Peter Steen

    2013-01-01

    When an online runoff model is updated from system measurements, the requirements of the precipitation input change. Using rain gauge data as precipitation input there will be a displacement between the time when the rain hits the gauge and the time where the rain hits the actual catchment, due to the time it takes for the rain cell to travel from the rain gauge to the catchment. Since this time displacement is not present for system measurements the data assimilation scheme might already have updated the model to include the impact from the particular rain cell when the rain data is forced upon the model, which therefore will end up including the same rain twice in the model run. This paper compares forecast accuracy of updated models when using time displaced rain input to that of rain input with constant biases. This is done using a simple time-area model and historic rain series that are either displaced in time or affected with a bias. The results show that for a 10 minute forecast, time displacements of 5 and 10 minutes compare to biases of 60 and 100%, respectively, independent of the catchments time of concentration.

  18. Sequential optimization of a terrestrial biosphere model constrained by multiple satellite based products

    Science.gov (United States)

    Ichii, K.; Kondo, M.; Wang, W.; Hashimoto, H.; Nemani, R. R.

    2012-12-01

    Various satellite-based spatial products such as evapotranspiration (ET) and gross primary productivity (GPP) are now produced by integration of ground and satellite observations. Effective use of these multiple satellite-based products in terrestrial biosphere models is an important step toward better understanding of terrestrial carbon and water cycles. However, due to the complexity of terrestrial biosphere models with large number of model parameters, the application of these spatial data sets in terrestrial biosphere models is difficult. In this study, we established an effective but simple framework to refine a terrestrial biosphere model, Biome-BGC, using multiple satellite-based products as constraints. We tested the framework in the monsoon Asia region covered by AsiaFlux observations. The framework is based on the hierarchical analysis (Wang et al. 2009) with model parameter optimization constrained by satellite-based spatial data. The Biome-BGC model is separated into several tiers to minimize the freedom of model parameter selections and maximize the independency from the whole model. For example, the snow sub-model is first optimized using MODIS snow cover product, followed by soil water sub-model optimized by satellite-based ET (estimated by an empirical upscaling method; Support Vector Regression (SVR) method; Yang et al. 2007), photosynthesis model optimized by satellite-based GPP (based on SVR method), and respiration and residual carbon cycle models optimized by biomass data. As a result of initial assessment, we found that most of default sub-models (e.g. snow, water cycle and carbon cycle) showed large deviations from remote sensing observations. However, these biases were removed by applying the proposed framework. For example, gross primary productivities were initially underestimated in boreal and temperate forest and overestimated in tropical forests. However, the parameter optimization scheme successfully reduced these biases. Our analysis

  19. Satellite based Ocean Forecasting, the SOFT project

    Science.gov (United States)

    Stemmann, L.; Tintoré, J.; Moneris, S.

    2003-04-01

    The knowledge of future oceanic conditions would have enormous impact on human marine related areas. For such reasons, a number of international efforts are being carried out to obtain reliable and manageable ocean forecasting systems. Among the possible techniques that can be used to estimate the near future states of the ocean, an ocean forecasting system based on satellite imagery is developped through the Satelitte based Ocean ForecasTing project (SOFT). SOFT, established by the European Commission, considers the development of a forecasting system of the ocean space-time variability based on satellite data by using Artificial Intelligence techniques. This system will be merged with numerical simulation approaches, via assimilation techniques, to get a hybrid SOFT-numerical forecasting system of improved performance. The results of the project will provide efficient forecasting of sea-surface temperature structures, currents, dynamic height, and biological activity associated to chlorophyll fields. All these quantities could give valuable information on the planning and management of human activities in marine environments such as navigation, fisheries, pollution control, or coastal management. A detailed identification of present or new needs and potential end-users concerned by such an operational tool is being performed. The project would study solutions adapted to these specific needs.

  20. CMORPH 8 Km: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A new technique is presented in which half-hourly global precipitation estimates derived from passive microwave satellite scans are propagated by motion vectors...

  1. Satellite-based assessment of grassland yields

    Science.gov (United States)

    Grant, K.; Siegmund, R.; Wagner, M.; Hartmann, S.

    2015-04-01

    Cutting date and frequency are important parameters determining grassland yields in addition to the effects of weather, soil conditions, plant composition and fertilisation. Because accurate and area-wide data of grassland yields are currently not available, cutting frequency can be used to estimate yields. In this project, a method to detect cutting dates via surface changes in radar images is developed. The combination of this method with a grassland yield model will result in more reliable and regional-wide numbers of grassland yields. For the test-phase of the monitoring project, a study area situated southeast of Munich, Germany, was chosen due to its high density of managed grassland. For determining grassland cutting robust amplitude change detection techniques are used evaluating radar amplitude or backscatter statistics before and after the cutting event. CosmoSkyMed and Sentinel-1A data were analysed. All detected cuts were verified according to in-situ measurements recorded in a GIS database. Although the SAR systems had various acquisition geometries, the amount of detected grassland cut was quite similar. Of 154 tested grassland plots, covering in total 436 ha, 116 and 111 cuts were detected using CosmoSkyMed and Sentinel-1A radar data, respectively. Further improvement of radar data processes as well as additional analyses with higher sample number and wider land surface coverage will follow for optimisation of the method and for validation and generalisation of the results of this feasibility study. The automation of this method will than allow for an area-wide and cost efficient cutting date detection service improving grassland yield models.

  2. Comparison Of Quantitative Precipitation Estimates Derived From Rain Gauge And Radar Derived Algorithms For Operational Flash Flood Support.

    Science.gov (United States)

    Streubel, D. P.; Kodama, K.

    2014-12-01

    To provide continuous flash flood situational awareness and to better differentiate severity of ongoing individual precipitation events, the National Weather Service Research Distributed Hydrologic Model (RDHM) is being implemented over Hawaii and Alaska. In the implementation process of RDHM, three gridded precipitation analyses are used as forcing. The first analysis is a radar only precipitation estimate derived from WSR-88D digital hybrid reflectivity, a Z-R relationship and aggregated into an hourly ¼ HRAP grid. The second analysis is derived from a rain gauge network and interpolated into an hourly ¼ HRAP grid using PRISM climatology. The third analysis is derived from a rain gauge network where rain gauges are assigned static pre-determined weights to derive a uniform mean areal precipitation that is applied over a catchment on a ¼ HRAP grid. To assess the effect of different QPE analyses on the accuracy of RDHM simulations and to potentially identify a preferred analysis for operational use, each QPE was used to force RDHM to simulate stream flow for 20 USGS peak flow events. An evaluation of the RDHM simulations was focused on peak flow magnitude, peak flow timing, and event volume accuracy to be most relevant for operational use. Results showed RDHM simulations based on the observed rain gauge amounts were more accurate in simulating peak flow magnitude and event volume relative to the radar derived analysis. However this result was not consistent for all 20 events nor was it consistent for a few of the rainfall events where an annual peak flow was recorded at more than one USGS gage. Implications of this indicate that a more robust QPE forcing with the inclusion of uncertainty derived from the three analyses may provide a better input for simulating extreme peak flow events.

  3. Towards a Near Real-Time Satellite-Based Flux Monitoring System for the MENA Region

    Science.gov (United States)

    Ershadi, A.; Houborg, R.; McCabe, M. F.; Anderson, M. C.; Hain, C.

    2013-12-01

    Satellite remote sensing has the potential to offer spatially and temporally distributed information on land surface characteristics, which may be used as inputs and constraints for estimating land surface fluxes of carbon, water and energy. Enhanced satellite-based monitoring systems for aiding local water resource assessments and agricultural management activities are particularly needed for the Middle East and North Africa (MENA) region. The MENA region is an area characterized by limited fresh water resources, an often inefficient use of these, and relatively poor in-situ monitoring as a result of sparse meteorological observations. To address these issues, an integrated modeling approach for near real-time monitoring of land surface states and fluxes at fine spatio-temporal scales over the MENA region is presented. This approach is based on synergistic application of multiple sensors and wavebands in the visible to shortwave infrared and thermal infrared (TIR) domain. The multi-scale flux mapping and monitoring system uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI), and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in conjunction with model reanalysis data and multi-sensor remotely sensed data from polar orbiting (e.g. Landsat and MODerate resolution Imaging Spectroradiometer (MODIS)) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate time-continuous (i.e. daily) estimates of field-scale water, energy and carbon fluxes. Within this modeling system, TIR satellite data provide information about the sub-surface moisture status and plant stress, obviating the need for precipitation input and a detailed soil surface characterization (i.e. for prognostic modeling of soil transport processes). The STARFM fusion methodology blends aspects of high frequency (spatially coarse) and spatially fine resolution sensors and is applied directly to flux output

  4. Skill Assessment of An Hybrid Technique To Estimate Quantitative Precipitation Forecast For Galicia (nw Spain)

    Science.gov (United States)

    Lage, A.; Taboada, J. J.

    Precipitation is the most obvious of the weather elements in its effects on normal life. Numerical weather prediction (NWP) is generally used to produce quantitative precip- itation forecast (QPF) beyond the 1-3 h time frame. These models often fail to predict small-scale variations of rain because of spin-up problems and their coarse spatial and temporal resolution (Antolik, 2000). Moreover, there are some uncertainties about the behaviour of the NWP models in extreme situations (de Bruijn and Brandsma, 2000). Hybrid techniques, combining the benefits of NWP and statistical approaches in a flexible way, are very useful to achieve a good QPF. In this work, a new technique of QPF for Galicia (NW of Spain) is presented. This region has a percentage of rainy days per year greater than 50% with quantities that may cause floods, with human and economical damages. The technique is composed of a NWP model (ARPS) and a statistical downscaling process based on an automated classification scheme of at- mospheric circulation patterns for the Iberian Peninsula (J. Ribalaygua and R. Boren, 1995). Results show that QPF for Galicia is improved using this hybrid technique. [1] Antolik, M.S. 2000 "An Overview of the National Weather Service's centralized statistical quantitative precipitation forecasts". Journal of Hydrology, 239, pp:306- 337. [2] de Bruijn, E.I.F and T. Brandsma "Rainfall prediction for a flooding event in Ireland caused by the remnants of Hurricane Charley". Journal of Hydrology, 239, pp:148-161. [3] Ribalaygua, J. and Boren R. "Clasificación de patrones espaciales de precipitación diaria sobre la España Peninsular". Informes N 3 y 4 del Servicio de Análisis e Investigación del Clima. Instituto Nacional de Meteorología. Madrid. 53 pp.

  5. Stochastic characterization of regional circulation patterns for climate model diagnosis and estimation of local precipitation

    International Nuclear Information System (INIS)

    Zorita, E.; Hughes, J.P.

    1993-01-01

    Two statistical approaches for linking large-scale atmospheric circulation patterns and daily local rainfall are described and applied to several GCM (general circulation model) climate simulations. The ultimate objective is to simulate local precipitation associated with alternative climates. The index stations are located near the West and East North American coasts. The first method is based on CART analysis (Classification and Regression trees). It finds the classification of observed daily SLR (sea level pressure) fields in weather types that are most strongly associated with the presence/absence of rainfall in a set of index stations. The best results were obtained for winter rainfall for the West Coast, where a set of physically reasonable weather types could be identified, whereas for the East Coast the rainfall process seemed to be spatially less coherent. The GCM simulations were validated against observations in terms of probability of occurrence and survival time of these weather states. Some discrepancies werefound but there was no systematic bias, indicating that this behavior depends on the particular dynamics of each model. This classification method was then used for the generation of daily rainfall time series from the daily SLP fields from historical observation and from the GCM simulations. Whereas the mean rainfall and probability distributions were rather well replicated, the simulated dry periods were in all cases shorter than in the rainfall observations. The second rainfall generator is based on the analog method and uses information on the evolution of the SLP field in several previous days. It was found to perform reasonably well, although some downward bias in the simulated rainfall persistence was still present. Rainfall changes in a 2xCO 2 climate were investigated by applying both methods to the output of a greenhouse-gas experiment. The simulated precipitation changes were small. (orig.)

  6. Validation of an Innovative Satellite-Based UV Dosimeter

    Science.gov (United States)

    Morelli, Marco; Masini, Andrea; Simeone, Emilio; Khazova, Marina

    2016-08-01

    We present an innovative satellite-based UV (ultraviolet) radiation dosimeter with a mobile app interface that has been validated by exploiting both ground-based measurements and an in-vivo assessment of the erythemal effects on some volunteers having a controlled exposure to solar radiation.Both validations showed that the satellite-based UV dosimeter has a good accuracy and reliability needed for health-related applications.The app with this satellite-based UV dosimeter also includes other related functionalities such as the provision of safe sun exposure time updated in real-time and end exposure visual/sound alert. This app will be launched on the global market by siHealth Ltd in May 2016 under the name of "HappySun" and available both for Android and for iOS devices (more info on http://www.happysun.co.uk).Extensive R&D activities are on-going for further improvement of the satellite-based UV dosimeter's accuracy.

  7. Enhancing Global Land Surface Hydrology Estimates from the NASA MERRA Reanalysis Using Precipitation Observations and Model Parameter Adjustments

    Science.gov (United States)

    Reichle, Rolf; Koster, Randal; DeLannoy, Gabrielle; Forman, Barton; Liu, Qing; Mahanama, Sarith; Toure, Ally

    2011-01-01

    The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides. in addition to atmospheric fields. global estimates of soil moisture, latent heat flux. snow. and runoff for J 979-present. This study introduces a supplemental and improved set of land surface hydrological fields ('MERRA-Land') generated by replaying a revised version of the land component of the MERRA system. Specifically. the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameters in the rainfall interception model, changes that effectively correct for known limitations in the MERRA land surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim reanalysis. MERRA-Land and ERA-Interim root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 15 basins in the western US) of MERRA and MERRA-Land is typically higher than that of ERA-Interim. With a few exceptions. the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using '\\-tERRA output for land surface hydrological studies.

  8. Estimating spatially and temporally varying recharge and runoff from precipitation and urban irrigation in the Los Angeles Basin, California

    Science.gov (United States)

    Hevesi, Joseph A.; Johnson, Tyler D.

    2016-10-17

    A daily precipitation-runoff model, referred to as the Los Angeles Basin watershed model (LABWM), was used to estimate recharge and runoff for a 5,047 square kilometer study area that included the greater Los Angeles area and all surface-water drainages potentially contributing recharge to a 1,450 square kilometer groundwater-study area underlying the greater Los Angeles area, referred to as the Los Angeles groundwater-study area. The recharge estimates for the Los Angeles groundwater-study area included spatially distributed recharge in response to the infiltration of precipitation, runoff, and urban irrigation, as well as mountain-front recharge from surface-water drainages bordering the groundwater-study area. The recharge and runoff estimates incorporated a new method for estimating urban irrigation, consisting of residential and commercial landscape watering, based on land use and the percentage of pervious land area.The LABWM used a 201.17-meter gridded discretization of the study area to represent spatially distributed climate and watershed characteristics affecting the surface and shallow sub-surface hydrology for the Los Angeles groundwater study area. Climate data from a local network of 201 monitoring sites and published maps of 30-year-average monthly precipitation and maximum and minimum air temperature were used to develop the climate inputs for the LABWM. Published maps of land use, land cover, soils, vegetation, and surficial geology were used to represent the physical characteristics of the LABWM area. The LABWM was calibrated to available streamflow records at six streamflow-gaging stations.Model results for a 100-year target-simulation period, from water years 1915 through 2014, were used to quantify and evaluate the spatial and temporal variability of water-budget components, including evapotranspiration (ET), recharge, and runoff. The largest outflow of water from the LABWM was ET; the 100-year average ET rate of 362 millimeters per year (mm

  9. EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experiment

    Directory of Open Access Journals (Sweden)

    J. C. Bergès

    2010-01-01

    Full Text Available This paper presents a new rainfall estimation method, EPSAT-SG which is a frame for method design. The first implementation has been carried out to meet the requirement of the AMMA database on a West African domain. The rainfall estimation relies on two intermediate products: a rainfall probability and a rainfall potential intensity. The first one is computed from MSG/SEVIRI by a feed forward neural network. First evaluation results show better properties than direct precipitation intensity assessment by geostationary satellite infra-red sensors. The second product can be interpreted as a conditional rainfall intensity and, in the described implementation, it is extracted from GPCP-1dd. Various implementation options are discussed and comparison of this embedded product with 3B42 estimates demonstrates the importance of properly managing the temporal discontinuity. The resulting accumulated rainfall field can be presented as a GPCP downscaling. A validation based on ground data supplied by AGRHYMET (Niamey indicates that the estimation error has been reduced in this process. The described method could be easily adapted to other geographical area and operational environment.

  10. Evaluation of two "integrated" polarimetric Quantitative Precipitation Estimation (QPE) algorithms at C-band

    Science.gov (United States)

    Tabary, Pierre; Boumahmoud, Abdel-Amin; Andrieu, Hervé; Thompson, Robert J.; Illingworth, Anthony J.; Le Bouar, Erwan; Testud, Jacques

    2011-08-01

    SummaryTwo so-called "integrated" polarimetric rate estimation techniques, ZPHI ( Testud et al., 2000) and ZZDR ( Illingworth and Thompson, 2005), are evaluated using 12 episodes of the year 2005 observed by the French C-band operational Trappes radar, located near Paris. The term "integrated" means that the concentration parameter of the drop size distribution is assumed to be constant over some area and the algorithms retrieve it using the polarimetric variables in that area. The evaluation is carried out in ideal conditions (no partial beam blocking, no ground-clutter contamination, no bright band contamination, a posteriori calibration of the radar variables ZH and ZDR) using hourly rain gauges located at distances less than 60 km from the radar. Also included in the comparison, for the sake of benchmarking, is a conventional Z = 282 R1.66 estimator, with and without attenuation correction and with and without adjustment by rain gauges as currently done operationally at Météo France. Under those ideal conditions, the two polarimetric algorithms, which rely solely on radar data, appear to perform as well if not better, pending on the measurements conditions (attenuation, rain rates, …), than the conventional algorithms, even when the latter take into account rain gauges through the adjustment scheme. ZZDR with attenuation correction is the best estimator for hourly rain gauge accumulations lower than 5 mm h -1 and ZPHI is the best one above that threshold. A perturbation analysis has been conducted to assess the sensitivity of the various estimators with respect to biases on ZH and ZDR, taking into account the typical accuracy and stability that can be reasonably achieved with modern operational radars these days (1 dB on ZH and 0.2 dB on ZDR). A +1 dB positive bias on ZH (radar too hot) results in a +14% overestimation of the rain rate with the conventional estimator used in this study (Z = 282R1.66), a -19% underestimation with ZPHI and a +23

  11. Low cloud precipitation climatology in the southeastern Pacific marine stratocumulus region using CloudSat

    International Nuclear Information System (INIS)

    Rapp, Anita D; Lebsock, Matthew; L’Ecuyer, Tristan

    2013-01-01

    A climatology of low cloud surface precipitation occurrence and intensity from the new CloudSat 2C-RAIN-PROFILE algorithm is presented from June 2006 through December 2010 for the southeastern Pacific region of marine stratocumulus. Results show that over 70% of low cloud precipitation falls as drizzle. Application of an empirical evaporation model suggests that 50–80% of the precipitation evaporates before it reaches the surface. Segregation of the CloudSat ascending and descending overpasses shows that the majority of precipitation occurs at night. Examination of the seasonal cycle shows that the precipitation is most frequent during the austral winter and spring; however there is considerable regional variability. Conditional rain rates increase from east to west with a maximum occurring in the region influenced by the South Pacific Convergence Zone. Area average rain rates are highest in the region where precipitation rates are moderate, but most frequent. The area average surface rain rate for low cloud precipitation for this region is ∼0.22 mm d −1 , in good agreement with in situ estimates, and is greatly improved over earlier CloudSat precipitation products. These results provide a much-needed quantification of surface precipitation in a region that is currently underestimated in existing satellite-based precipitation climatologies. (letter)

  12. Estimation of microwave source location in precipitating electron fluxes according to Viking satellite data

    International Nuclear Information System (INIS)

    Khrushchinskij, A.A.; Ostapenko, A.A.; Gustafsson, G.; Eliasson, L.; Sandal, I.

    1989-01-01

    According to the Viking satellite data on electron fluxes in the 0.1-300 keV energy range, the microburst source location is estimated. On the basis of experimental delays in detected peaks in different energy channels and theoretical calculations of these delays within the dipole field model (L∼ 4-5.5), it is shown that the most probable source location is the equatorial region with the centre, 5-10 0 shifted towards the ionosphere

  13. Operational Estimation of Accumulated Precipitation using Satellite Observation, by Eumetsat Satellite Application facility in Support to Hydrology (H-SAF Consortium).

    Science.gov (United States)

    di Diodato, A.; de Leonibus, L.; Zauli, F.; Biron, D.; Melfi, D.

    2009-04-01

    Operational Estimation of Accumulated Precipitation using Satellite Observation, by Eumetsat Satellite Application facility in Support to Hydrology (H-SAF Consortium). Cap. Attilio DI DIODATO(*), T.Col. Luigi DE LEONIBUS(*), T.Col Francesco ZAULI(*), Cap. Daniele BIRON(*), Ten. Davide Melfi(*) Satellite Application Facilities (SAFs) are specialised development and processing centres of the EUMETSAT Distributed Ground Segment. SAFs process level 1b data from meteorological satellites (geostationary and polar ones) in conjunction with all other relevant sources of data and appropriate models to generate services and level 2 products. Each SAF is a consortium of EUMETSAT European partners lead by a host institute responsible for the management of the complete SAF project. The Meteorological Service of Italian Air Force is the host Institute for the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF). HSAF has the commitment to develop and to provide, operationally after 2010, products regarding precipitation, soil moisture and snow. HSAF is going to provide information on error structure of its products and validation of the products via their impacts into Hydrological models. To that purpose it has been structured a specific subgroups. Accumulated precipitation is computed by temporal integration of the instantaneous rain rate achieved by the blended LEO/MW and GEO/IR precipitation rate products generated by Rapid Update method available every 15 minutes. The algorithm provides four outputs, consisting in accumulated precipitation in 3, 6, 12 and 24 hours, delivered every 3 hours at the synoptic hours. These outputs are our precipitation background fields. Satellite estimates can cover most of the globe, however, they suffer from errors due to lack of a direct relationship between observation parameters and precipitation, the poor sampling and algorithm imperfections. For this reason the 3 hours accumulated precipitation is

  14. Global trends in satellite-based emergency mapping

    Science.gov (United States)

    Voigt, Stefan; Giulio-Tonolo, Fabio; Lyons, Josh; Kučera, Jan; Jones, Brenda; Schneiderhan, Tobias; Platzeck, Gabriel; Kaku, Kazuya; Hazarika, Manzul Kumar; Czaran, Lorant; Li, Suju; Pedersen, Wendi; James, Godstime Kadiri; Proy, Catherine; Muthike, Denis Macharia; Bequignon, Jerome; Guha-Sapir, Debarati

    2016-01-01

    Over the past 15 years, scientists and disaster responders have increasingly used satellite-based Earth observations for global rapid assessment of disaster situations. We review global trends in satellite rapid response and emergency mapping from 2000 to 2014, analyzing more than 1000 incidents in which satellite monitoring was used for assessing major disaster situations. We provide a synthesis of spatial patterns and temporal trends in global satellite emergency mapping efforts and show that satellite-based emergency mapping is most intensively deployed in Asia and Europe and follows well the geographic, physical, and temporal distributions of global natural disasters. We present an outlook on the future use of Earth observation technology for disaster response and mitigation by putting past and current developments into context and perspective.

  15. Satellite based wind resource assessment over the South China Sea

    DEFF Research Database (Denmark)

    Badger, Merete; Astrup, Poul; Hasager, Charlotte Bay

    2014-01-01

    variations are clearly visible across the domain; for instance sheltering effects caused by the land masses. The satellite based wind resource maps have two shortcomings. One is the lack of information at the higher vertical levels where wind turbines operate. The other is the limited number of overlapping...... years of WRF data – specifically the parameters heat flux, air temperature, and friction velocity – are used to calculate a long-term correction for atmospheric stability effects. The stability correction is applied to the satellite based wind resource maps together with a vertical wind profile...... from satellite synthetic aperture radar (SAR) data are particularly suitable for offshore wind energy applications because they offer a spatial resolution up to 500 m and include coastal seas. In this presentation, satellite wind maps are used in combination with mast observations and numerical...

  16. Applications of TRMM-based Multi-Satellite Precipitation Estimation for Global Runoff Simulation: Prototyping a Global Flood Monitoring System

    Science.gov (United States)

    Hong, Yang; Adler, Robert F.; Huffman, George J.; Pierce, Harold

    2008-01-01

    Advances in flood monitoring/forecasting have been constrained by the difficulty in estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and flood-relevant time scale. With the recent availability of satellite rainfall estimates at fine time and space resolution, this paper describes a prototype research framework for global flood monitoring by combining real-time satellite observations with a database of global terrestrial characteristics through a hydrologically relevant modeling scheme. Four major components included in the framework are (1) real-time precipitation input from NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA); (2) a central geospatial database to preprocess the land surface characteristics: water divides, slopes, soils, land use, flow directions, flow accumulation, drainage network etc.; (3) a modified distributed hydrological model to convert rainfall to runoff and route the flow through the stream network in order to predict the timing and severity of the flood wave, and (4) an open-access web interface to quickly disseminate flood alerts for potential decision-making. Retrospective simulations for 1998-2006 demonstrate that the Global Flood Monitor (GFM) system performs consistently at both station and catchment levels. The GFM website (experimental version) has been running at near real-time in an effort to offer a cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world. The interactive GFM website shows close-up maps of the flood risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating QPF into the GFM, also will be implemented in the future.

  17. Estimates of increased black carbon emissions from electrostatic precipitators during powdered activated carbon injection for mercury emissions control.

    Science.gov (United States)

    Clack, Herek L

    2012-07-03

    The behavior of mercury sorbents within electrostatic precipitators (ESPs) is not well-understood, despite a decade or more of full-scale testing. Recent laboratory results suggest that powdered activated carbon exhibits somewhat different collection behavior than fly ash in an ESP and particulate filters located at the outlet of ESPs have shown evidence of powdered activated carbon penetration during full-scale tests of sorbent injection for mercury emissions control. The present analysis considers a range of assumed differential ESP collection efficiencies for powdered activated carbon as compared to fly ash. Estimated emission rates of submicrometer powdered activated carbon are compared to estimated emission rates of particulate carbon on submicrometer fly ash, each corresponding to its respective collection efficiency. To the extent that any emitted powdered activated carbon exhibits size and optical characteristics similar to black carbon, such emissions could effectively constitute an increase in black carbon emissions from coal-based stationary power generation. The results reveal that even for the low injection rates associated with chemically impregnated carbons, submicrometer particulate carbon emissions can easily double if the submicrometer fraction of the native fly ash has a low carbon content. Increasing sorbent injection rates, larger collection efficiency differentials as compared to fly ash, and decreasing sorbent particle size all lead to increases in the estimated submicrometer particulate carbon emissions.

  18. Intercomparison of PERSIANN-CDR and TRMM-3B42V7 precipitation estimates at monthly and daily time scales

    Science.gov (United States)

    Katiraie-Boroujerdy, Pari-Sima; Akbari Asanjan, Ata; Hsu, Kuo-lin; Sorooshian, Soroosh

    2017-09-01

    In the first part of this paper, monthly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission 3B42 algorithm Version 7 (TRMM-3B42V7) are evaluated over Iran using the Generalized Three-Cornered Hat (GTCH) method which is self-sufficient of reference data as input. Climate Data Unit (CRU) is added to the GTCH evaluations as an independent gauge-based dataset thus, the minimum requirement of three datasets for the model is satisfied. To ensure consistency of all datasets, the two satellite products were aggregated to 0.5° spatial resolution, which is the minimum resolution of CRU. The results show that the PERSIANN-CDR has higher Signal to Noise Ratio (SNR) than TRMM-3B42V7 for the monthly rainfall estimation, especially in the northern half of the country. All datasets showed low SNR in the mountainous area of southwestern Iran, as well as the arid parts in the southeast region of the country. Additionally, in order to evaluate the efficacy of PERSIANN-CDR and TRMM-3B42V7 in capturing extreme daily-precipitation amounts, an in-situ rain-gauge dataset collected by the Islamic Republic of the Iran Meteorological Organization (IRIMO) was employed. Given the sparsity of the rain gauges, only 0.25° pixels containing three or more gauges were used for this evaluation. There were 228 such pixels where daily and extreme rainfall from PERSIANN-CDR and TRMM-3B42V7 could be compared. However, TRMM-3B42V7 overestimates most of the intensity indices (correlation coefficients; R between 0.7648-0.8311, Root Mean Square Error; RMSE between 3.29mm/day-21.2mm/5day); PERSIANN-CDR underestimates these extremes (R between 0.6349-0.7791 and RMSE between 3.59mm/day-30.56mm/5day). Both satellite products show higher correlation coefficients and lower RMSEs for the annual mean of consecutive dry spells than wet spells. The results show that TRMM-3B42V7

  19. Trellis-coded CPM for satellite-based mobile communications

    Science.gov (United States)

    Abrishamkar, Farrokh; Biglieri, Ezio

    1988-01-01

    Digital transmission for satellite-based land mobile communications is discussed. To satisfy the power and bandwidth limitations imposed on such systems, a combination of trellis coding and continuous-phase modulated signals are considered. Some schemes based on this idea are presented, and their performance is analyzed by computer simulation. The results obtained show that a scheme based on directional detection and Viterbi decoding appears promising for practical applications.

  20. Improving the Regional Applicability of Satellite Precipitation Products by Ensemble Algorithm

    Directory of Open Access Journals (Sweden)

    Waseem Muhammad

    2018-04-01

    Full Text Available Satellite-based precipitation products (e.g., Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG and its predecessor, Tropical Rainfall Measuring Mission (TRMM are a critical source of precipitation estimation, particularly for a region with less, or no, hydrometric networking. However, the inconsistency in the performance of these products has been observed in different climatic and topographic diverse regions, timescales, and precipitation intensities and there is still room for improvement. Hence, using a projected ensemble algorithm, the regional precipitation estimate (RP is introduced here. The RP concept is mainly based on the regional performance weights derived from the Mean Square Error (MSE and the precipitation estimate from the TRMM product, that is, TRMM 3B42 (TR, real-time (late (IT and the research (post-real-time (IR products of IMERG. The overall results of the selected contingency table (e.g., Probability of detection (POD and statistical indices (e.g., Correlation Coefficient (CC signposted that the proposed RP product has shown an overall better potential to capture the gauge observations compared with the TR, IR, and IT in five different climatic regions of Pakistan from January 2015 to December 2016, at a diurnal time scale. The current study could be the first research providing preliminary feedback from Pakistan for global precipitation measurement researchers by highlighting the need for refinement in the IMERG.

  1. Comparison of NEXRAD multisensor precipitation estimates to rain gage observations in and near DuPage County, Illinois, 2002–12

    Science.gov (United States)

    Spies, Ryan R.; Over, Thomas M.; Ortel, Terry W.

    2018-05-21

    In this report, precipitation data from 2002 to 2012 from the hourly gridded Next-Generation Radar (NEXRAD)-based Multisensor Precipitation Estimate (MPE) precipitation product are compared to precipitation data from two rain gage networks—an automated tipping bucket network of 25 rain gages operated by the U.S. Geological Survey (USGS) and 51 rain gages from the volunteer-operated Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network—in and near DuPage County, Illinois, at a daily time step to test for long-term differences in space, time, and distribution. The NEXRAD–MPE data that are used are from the fifty 2.5-mile grid cells overlying the rain gages from the other networks. Because of the challenges of measuring of frozen precipitation, the analysis period is separated between days with or without the chance of freezing conditions. The NEXRAD–MPE and tipping-bucket rain gage precipitation data are adjusted to account for undercatch by multiplying by a previously determined factor of 1.14. Under nonfreezing conditions, the three precipitation datasets are broadly similar in cumulative depth and distribution of daily values when the data are combined spatially across the networks. However, the NEXRAD–MPE data indicate a significant trend relative to both rain gage networks as a function of distance from the NEXRAD radar just south of the study area. During freezing conditions, of the USGS network rain gages only the heated gages were considered, and these gages indicate substantial mean undercatch of 50 and 61 percent compared to the NEXRAD–MPE and the CoCoRaHS gages, respectively. The heated USGS rain gages also indicate substantially lower quantile values during freezing conditions, except during the most extreme (highest) events. Because NEXRAD precipitation products are continually evolving, the report concludes with a discussion of recent changes in those products and their potential for improved precipitation estimation. An appendix

  2. Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

    Science.gov (United States)

    Qi, W.; Zhang, C.; Fu, G.; Sweetapple, C.; Zhou, H.

    2016-02-01

    The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash-Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A

  3. Online Tools for Uncovering Data Quality (DQ) Issues in Satellite-Based Global Precipitation Products

    Science.gov (United States)

    Liu, Zhong; Heo, Gil

    2015-01-01

    Data quality (DQ) has many attributes or facets (i.e., errors, biases, systematic differences, uncertainties, benchmark, false trends, false alarm ratio, etc.)Sources can be complicated (measurements, environmental conditions, surface types, algorithms, etc.) and difficult to be identified especially for multi-sensor and multi-satellite products with bias correction (TMPA, IMERG, etc.) How to obtain DQ info fast and easily, especially quantified info in ROI Existing parameters (random error), literature, DIY, etc.How to apply the knowledge in research and applications.Here, we focus on online systems for integration of products and parameters, visualization and analysis as well as investigation and extraction of DQ information.

  4. The impact of reflectivity correction and accounting for raindrop size distribution variability to improve precipitation estimation by weather radar for an extreme low-land mesoscale convective system

    Science.gov (United States)

    Hazenberg, Pieter; Leijnse, Hidde; Uijlenhoet, Remko

    2014-11-01

    Between 25 and 27 August 2010 a long-duration mesoscale convective system was observed above the Netherlands, locally giving rise to rainfall accumulations exceeding 150 mm. Correctly measuring the amount of precipitation during such an extreme event is important, both from a hydrological and meteorological perspective. Unfortunately, the operational weather radar measurements were affected by multiple sources of error and only 30% of the precipitation observed by rain gauges was estimated. Such an underestimation of heavy rainfall, albeit generally less strong than in this extreme case, is typical for operational weather radar in The Netherlands. In general weather radar measurement errors can be subdivided into two groups: (1) errors affecting the volumetric reflectivity measurements (e.g. ground clutter, radar calibration, vertical profile of reflectivity) and (2) errors resulting from variations in the raindrop size distribution that in turn result in incorrect rainfall intensity and attenuation estimates from observed reflectivity measurements. A stepwise procedure to correct for the first group of errors leads to large improvements in the quality of the estimated precipitation, increasing the radar rainfall accumulations to about 65% of those observed by gauges. To correct for the second group of errors, a coherent method is presented linking the parameters of the radar reflectivity-rain rate (Z - R) and radar reflectivity-specific attenuation (Z - k) relationships to the normalized drop size distribution (DSD). Two different procedures were applied. First, normalized DSD parameters for the whole event and for each precipitation type separately (convective, stratiform and undefined) were obtained using local disdrometer observations. Second, 10,000 randomly generated plausible normalized drop size distributions were used for rainfall estimation, to evaluate whether this Monte Carlo method would improve the quality of weather radar rainfall products. Using the

  5. Mesoscale and Local Scale Evaluations of Quantitative Precipitation Estimates by Weather Radar Products during a Heavy Rainfall Event

    Directory of Open Access Journals (Sweden)

    Basile Pauthier

    2016-01-01

    Full Text Available A 24-hour heavy rainfall event occurred in northeastern France from November 3 to 4, 2014. The accuracy of the quantitative precipitation estimation (QPE by PANTHERE and ANTILOPE radar-based gridded products during this particular event, is examined at both mesoscale and local scale, in comparison with two reference rain-gauge networks. Mesoscale accuracy was assessed for the total rainfall accumulated during the 24-hour event, using the Météo France operational rain-gauge network. Local scale accuracy was assessed for both total event rainfall and hourly rainfall accumulations, using the recently developed HydraVitis high-resolution rain gauge network Evaluation shows that (1 PANTHERE radar-based QPE underestimates rainfall fields at mesoscale and local scale; (2 both PANTHERE and ANTILOPE successfully reproduced the spatial variability of rainfall at local scale; (3 PANTHERE underestimates can be significantly improved at local scale by merging these data with rain gauge data interpolation (i.e., ANTILOPE. This study provides a preliminary evaluation of radar-based QPE at local scale, suggesting that merged products are invaluable for applications at very high resolution. The results obtained underline the importance of using high-density rain-gauge networks to obtain information at high spatial and temporal resolution, for better understanding of local rainfall variation, to calibrate remotely sensed rainfall products.

  6. Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002-2012)

    Science.gov (United States)

    Prat, O. P.; Nelson, B. R.

    2015-04-01

    We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over the contiguous United States (CONUS) for the period 2002-2012. This comparison effort includes satellite multi-sensor data sets (bias-adjusted TMPA 3B42, near-real-time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation data sets are compared with surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (±6%). However, differences at the RFC are more important in particular for near-real-time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near-real-time counterpart 3B42RT. However, large biases remained for 3B42 over the western USA for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in. day-1) over the Pacific Northwest. Furthermore, the conditional analysis and a contingency analysis conducted illustrated the challenge in retrieving extreme precipitation from remote sensing estimates.

  7. Empirical model for mean temperature for Indian zone and estimation of precipitable water vapor from ground based GPS measurements

    Directory of Open Access Journals (Sweden)

    C. Suresh Raju

    2007-10-01

    Full Text Available Estimation of precipitable water (PW in the atmosphere from ground-based Global Positioning System (GPS essentially involves modeling the zenith hydrostatic delay (ZHD in terms of surface Pressure (Ps and subtracting it from the corresponding values of zenith tropospheric delay (ZTD to estimate the zenith wet (non-hydrostatic delay (ZWD. This further involves establishing an appropriate model connecting PW and ZWD, which in its simplest case assumed to be similar to that of ZHD. But when the temperature variations are large, for the accurate estimate of PW the variation of the proportionality constant connecting PW and ZWD is to be accounted. For this a water vapor weighted mean temperature (Tm has been defined by many investigations, which has to be modeled on a regional basis. For estimating PW over the Indian region from GPS data, a region specific model for Tm in terms of surface temperature (Ts is developed using the radiosonde measurements from eight India Meteorological Department (IMD stations spread over the sub-continent within a latitude range of 8.5°–32.6° N. Following a similar procedure Tm-based models are also evolved for each of these stations and the features of these site-specific models are compared with those of the region-specific model. Applicability of the region-specific and site-specific Tm-based models in retrieving PW from GPS data recorded at the IGS sites Bangalore and Hyderabad, is tested by comparing the retrieved values of PW with those estimated from the altitude profile of water vapor measured using radiosonde. The values of ZWD estimated at 00:00 UTC and 12:00 UTC are used to test the validity of the models by estimating the PW using the models and comparing it with those obtained from radiosonde data. The region specific Tm-based model is found to be in par with if not better than a

  8. An improved export coefficient model to estimate non-point source phosphorus pollution risks under complex precipitation and terrain conditions.

    Science.gov (United States)

    Cheng, Xian; Chen, Liding; Sun, Ranhao; Jing, Yongcai

    2018-05-15

    To control non-point source (NPS) pollution, it is important to estimate NPS pollution exports and identify sources of pollution. Precipitation and terrain have large impacts on the export and transport of NPS pollutants. We established an improved export coefficient model (IECM) to estimate the amount of agricultural and rural NPS total phosphorus (TP) exported from the Luanhe River Basin (LRB) in northern China. The TP concentrations of rivers from 35 selected catchments in the LRB were used to test the model's explanation capacity and accuracy. The simulation results showed that, in 2013, the average TP export was 57.20 t at the catchment scale. The mean TP export intensity in the LRB was 289.40 kg/km 2 , which was much higher than those of other basins in China. In the LRB topographic regions, the TP export intensity was the highest in the south Yanshan Mountains and was followed by the plain area, the north Yanshan Mountains, and the Bashang Plateau. Among the three pollution categories, the contribution ratios to TP export were, from high to low, the rural population (59.44%), livestock husbandry (22.24%), and land-use types (18.32%). Among all ten pollution sources, the contribution ratios from the rural population (59.44%), pigs (14.40%), and arable land (10.52%) ranked as the top three sources. This study provides information that decision makers and planners can use to develop sustainable measures for the prevention and control of NPS pollution in semi-arid regions.

  9. Merging Satellite Precipitation Products for Improved Streamflow Simulations

    Science.gov (United States)

    Maggioni, V.; Massari, C.; Barbetta, S.; Camici, S.; Brocca, L.

    2017-12-01

    Accurate quantitative precipitation estimation is of great importance for water resources management, agricultural planning and forecasting and monitoring of natural hazards such as flash floods and landslides. In situ observations are limited around the Earth, especially in remote areas (e.g., complex terrain, dense vegetation), but currently available satellite precipitation products are able to provide global precipitation estimates with an accuracy that depends upon many factors (e.g., type of storms, temporal sampling, season, etc.). The recent SM2RAIN approach proposes to estimate rainfall by using satellite soil moisture observations. As opposed to traditional satellite precipitation methods, which sense cloud properties to retrieve instantaneous estimates, this new bottom-up approach makes use of two consecutive soil moisture measurements for obtaining an estimate of the fallen precipitation within the interval between two satellite overpasses. As a result, the nature of the measurement is different and complementary to the one of classical precipitation products and could provide a different valid perspective to substitute or improve current rainfall estimates. Therefore, we propose to merge SM2RAIN and the widely used TMPA 3B42RT product across Italy for a 6-year period (2010-2015) at daily/0.25deg temporal/spatial scale. Two conceptually different merging techniques are compared to each other and evaluated in terms of different statistical metrics, including hit bias, threat score, false alarm rates, and missed rainfall volumes. The first is based on the maximization of the temporal correlation with a reference dataset, while the second is based on a Bayesian approach, which provides a probabilistic satellite precipitation estimate derived from the joint probability distribution of observations and satellite estimates. The merged precipitation products show a better performance with respect to the parental satellite-based products in terms of categorical

  10. [Surveying a zoological facility through satellite-based geodesy].

    Science.gov (United States)

    Böer, M; Thien, W; Tölke, D

    2000-06-01

    In the course of a thesis submitted for a diploma degree within the Fachhochschule Oldenburg the Serengeti Safaripark was surveyed in autumn and winter 1996/97 laying in the planning foundations for the application for licences from the controlling authorities. Taking into consideration the special way of keeping animals in the Serengeti Safaripark (game ranching, spacious walk-through-facilities) the intention was to employ the outstanding satellite based geodesy. This technology relies on special aerials receiving signals from 24 satellites which circle around the globe. These data are being gathered and examined. This examination produces the exact position of this aerial in a system of coordinates which allows depicting this point on a map. This procedure was used stationary (from a strictly defined point) as well as in the movement (in a moving car). Additionally conventional procedures were used when the satellite based geodesy came to its limits. Finally a detailed map of the Serengeti Safaripark was created which shows the position and size of stables and enclosures as well as wood and water areas and the sectors of the leisure park. Furthermore the established areas of the enclosures together with an already existing animal databank have flown into an information system with the help of which the stock of animals can be managed enclosure-orientated.

  11. On-line estimation of the dissolved zinc concentration during ZnS precipitation in a continuous stirred tank reactor (CSTR)

    NARCIS (Netherlands)

    Grootscholten, T.I.M.; Keesman, K.J.; Lens, P.N.L.

    2008-01-01

    In this paper a method is presented to estimate the reaction term of zinc sulphide precipitation and the zinc concentration in a CSTR, using the read-out signal of a sulphide selective electrode. The reaction between zinc and sulphide is described by a non-linear model and therefore classical

  12. A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model

    Science.gov (United States)

    Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

    2017-11-01

    Precipitation plays an important role in determining the climate of a region. Precise estimation of precipitation is required to manage and plan water resources, as well as other related applications such as hydrology, climatology, meteorology and agriculture. Time series of hydrologic variables such as precipitation are composed of deterministic and stochastic parts. Despite this fact, the stochastic part of the precipitation data is not usually considered in modeling of precipitation process. As an innovation, the present study introduces three new hybrid models by integrating soft computing methods including multivariate adaptive regression splines (MARS), Bayesian networks (BN) and gene expression programming (GEP) with a time series model, namely generalized autoregressive conditional heteroscedasticity (GARCH) for modeling of the monthly precipitation. For this purpose, the deterministic (obtained by soft computing methods) and stochastic (obtained by GARCH time series model) parts are combined with each other. To carry out this research, monthly precipitation data of Babolsar, Bandar Anzali, Gorgan, Ramsar, Tehran and Urmia stations with different climates in Iran were used during the period of 1965-2014. Root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE) and determination coefficient (R2) were employed to evaluate the performance of conventional/single MARS, BN and GEP, as well as the proposed MARS-GARCH, BN-GARCH and GEP-GARCH hybrid models. It was found that the proposed novel models are more precise than single MARS, BN and GEP models. Overall, MARS-GARCH and BN-GARCH models yielded better accuracy than GEP-GARCH. The results of the present study confirmed the suitability of proposed methodology for precise modeling of precipitation.

  13. Cross-validation Methodology between Ground and GPM Satellite-based Radar Rainfall Product over Dallas-Fort Worth (DFW) Metroplex

    Science.gov (United States)

    Chen, H.; Chandrasekar, V.; Biswas, S.

    2015-12-01

    Over the past two decades, a large number of rainfall products have been developed based on satellite, radar, and/or rain gauge observations. However, to produce optimal rainfall estimation for a given region is still challenging due to the space time variability of rainfall at many scales and the spatial and temporal sampling difference of different rainfall instruments. In order to produce high-resolution rainfall products for urban flash flood applications and improve the weather sensing capability in urban environment, the center for Collaborative Adaptive Sensing of the Atmosphere (CASA), in collaboration with National Weather Service (NWS) and North Central Texas Council of Governments (NCTCOG), has developed an urban radar remote sensing network in DFW Metroplex. DFW is the largest inland metropolitan area in the U.S., that experiences a wide range of natural weather hazards such as flash flood and hailstorms. The DFW urban remote sensing network, centered by the deployment of eight dual-polarization X-band radars and a NWS WSR-88DP radar, is expected to provide impacts-based warning and forecasts for benefit of the public safety and economy. High-resolution quantitative precipitation estimation (QPE) is one of the major goals of the development of this urban test bed. In addition to ground radar-based rainfall estimation, satellite-based rainfall products for this area are also of interest for this study. Typical example is the rainfall rate product produced by the Dual-frequency Precipitation Radar (DPR) onboard Global Precipitation Measurement (GPM) Core Observatory satellite. Therefore, cross-comparison between ground and space-based rainfall estimation is critical to building an optimal regional rainfall system, which can take advantages of the sampling differences of different sensors. This paper presents the real-time high-resolution QPE system developed for DFW urban radar network, which is based upon the combination of S-band WSR-88DP and X

  14. First estimates of the contribution of CaCO3 precipitation to the release of CO2 to the atmosphere during young sea ice growth

    Science.gov (United States)

    Geilfus, N.-X.; Carnat, G.; Dieckmann, G. S.; Halden, N.; Nehrke, G.; Papakyriakou, T.; Tison, J.-L.; Delille, B.

    2013-01-01

    report measurements of pH, total alkalinity, air-ice CO2 fluxes (chamber method), and CaCO3 content of frost flowers (FF) and thin landfast sea ice. As the temperature decreases, concentration of solutes in the brine skim increases. Along this gradual concentration process, some salts reach their solubility threshold and start precipitating. The precipitation of ikaite (CaCO3.6H2O) was confirmed in the FF and throughout the ice by Raman spectroscopy and X-ray analysis. The amount of ikaite precipitated was estimated to be 25 µmol kg-1 melted FF, in the FF and is shown to decrease from 19 to 15 µmol kg-1 melted ice in the upper part and at the bottom of the ice, respectively. CO2 release due to precipitation of CaCO3 is estimated to be 50 µmol kg-1 melted samples. The dissolved inorganic carbon (DIC) normalized to a salinity of 10 exhibits significant depletion in the upper layer of the ice and in the FF. This DIC loss is estimated to be 2069 µmol kg-1 melted sample and corresponds to a CO2 release from the ice to the atmosphere ranging from 20 to 40 mmol m-2 d-1. This estimate is consistent with flux measurements of air-ice CO2 exchange. Our measurements confirm previous laboratory findings that growing young sea ice acts as a source of CO2 to the atmosphere. CaCO3 precipitation during early ice growth appears to promote the release of CO2 to the atmosphere; however, its contribution to the overall release by newly formed ice is most likely minor.

  15. Comparison between satellite precipitation product and observation rain gauges in the Red-Thai Binh River Basin

    Science.gov (United States)

    Lakshmi, V.; Le, M. H.; Sutton, J. R. P.; Bui, D. D.; Bolten, J. D.

    2017-12-01

    The Red-ThaiBinh River is the second largest river in Vietnam in terms of economic impact and is home to around 29 million people. The river has been facing challenges for water resources allocation, which require reliable and routine hydrological assessments. However, hydrological analysis is difficult due to insufficient spatial coverage by rain gauges. Satellite-based precipitation estimates are a promising alternative with high-resolution in both time and space. This study aims at investigating the uncertainties in satellite-based precipitation product TRMM 3B42 v7.0 by comparing them against in-situ measurements over the Red-ThaiBinh River basin. The TRMM 3B42 v7.0 are assessed in terms of seasonal, monthly and daily variations over a 17-year period (1998 - 2014). Preliminary results indicate that at a daily scale, except for low Mean Bias Error (MBE), satellite based rainfall product has weak relationship with ground observation data, indicating by average performance of 0.326 and -0.485 for correlation coefficient and Nash Sutcliffe Efficiency (NSE), respectively. At monthly scale, we observe that the TRMM 3B42 v7.0 has higher correlation with the correlation increased significantly to 0.863 and NSE of 0.522. By analyzing wet season (May - October) and dry season (November - April) separately we find that the correlation between the TRMM 3B42 v7.0 with ground observations were higher for wet season than the dry season.

  16. Terrestrial precipitation and soil moisture: A case study over southern Arizona and data development

    Science.gov (United States)

    Stillman, Susan

    Quantifying climatological precipitation and soil moisture as well as interannual variability and trends requires extensive observation. This work focuses on the analysis of available precipitation and soil moisture data and the development of new ways to estimate these quantities. Precipitation and soil moisture characteristics are highly dependent on the spatial and temporal scales. We begin at the point scale, examining hourly precipitation and soil moisture at individual gauges. First, we focus on the Walnut Gulch Experimental Watershed (WGEW), a 150 km2 area in southern Arizona. The watershed has been measuring rainfall since 1956 with a very high density network of approximately 0.6 gauges per km2. Additionally, there are 19 soil moisture probes at 5 cm depth with data starting in 2002. In order to extend the measurement period, we have developed a water balance model which estimates monsoon season (Jul-Sep) soil moisture using only precipitation for input, and calibrated so that the modeled soil moisture fits best with the soil moisture measured by each of the 19 probes from 2002-2012. This observationally constrained soil moisture is highly correlated with the collocated probes (R=0.88), and extends the measurement period from 10 to 56 years and the number of gauges from 19 to 88. Then, we focus on the spatiotemporal variability within the watershed and the ability to estimate area averaged quantities. Spatially averaged precipitation and observationally constrained soil moisture from the 88 gauges is then used to evaluate various gridded datasets. We find that gauge-based precipitation products perform best followed by reanalyses and then satellite-based products. Coupled Model Intercomparison Project Phase 5 (CMIP5) models perform the worst and overestimate cold season precipitation while offsetting the monsoon peak precipitation forward or backward by a month. Satellite-based soil moisture is the best followed by land data assimilation systems and

  17. Programmable Ultra-Lightweight System Adaptable Radio Satellite Base Station

    Science.gov (United States)

    Varnavas, Kosta; Sims, Herb

    2015-01-01

    With the explosion of the CubeSat, small sat, and nanosat markets, the need for a robust, highly capable, yet affordable satellite base station, capable of telemetry capture and relay, is significant. The Programmable Ultra-Lightweight System Adaptable Radio (PULSAR) is NASA Marshall Space Flight Center's (MSFC's) software-defined digital radio, developed with previous Technology Investment Programs and Technology Transfer Office resources. The current PULSAR will have achieved a Technology Readiness Level-6 by the end of FY 2014. The extensibility of the PULSAR will allow it to be adapted to perform the tasks of a mobile base station capable of commanding, receiving, and processing satellite, rover, or planetary probe data streams with an appropriate antenna.

  18. Improved Satellite-based Photosysnthetically Active Radiation (PAR) for Air Quality Studies

    Science.gov (United States)

    Pour Biazar, A.; McNider, R. T.; Cohan, D. S.; White, A.; Zhang, R.; Dornblaser, B.; Doty, K.; Wu, Y.; Estes, M. J.

    2015-12-01

    One of the challenges in understanding the air quality over forested regions has been the uncertainties in estimating the biogenic hydrocarbon emissions. Biogenic volatile organic compounds, BVOCs, play a critical role in atmospheric chemistry, particularly in ozone and particulate matter (PM) formation. In southeastern United States, BVOCs (mostly as isoprene) are the dominant summertime source of reactive hydrocarbon. Despite significant efforts in improving BVOC estimates, the errors in emission inventories remain a concern. Since BVOC emissions are particularly sensitive to the available photosynthetically active radiation (PAR), model errors in PAR result in large errors in emission estimates. Thus, utilization of satellite observations to estimate PAR can help in reducing emission uncertainties. Satellite-based PAR estimates rely on the technique used to derive insolation from satellite visible brightness measurements. In this study we evaluate several insolation products against surface pyranometer observations and offer a bias correction to generate a more accurate PAR product. The improved PAR product is then used in biogenic emission estimates. The improved biogenic emission estimates are compared to the emission inventories over Texas and used in air quality simulation over the period of August-September 2013 (NASA's Discover-AQ field campaign). A series of sensitivity simulations will be performed and evaluated against Discover-AQ observations to test the impact of satellite-derived PAR on air quality simulations.

  19. Long-Term Quantitative Precipitation Estimates (QPE) at High Spatial and Temporal Resolution over CONUS: Bias-Adjustment of the Radar-Only National Mosaic and Multi-sensor QPE (NMQ/Q2) Precipitation Reanalysis (2001-2012)

    Science.gov (United States)

    Prat, Olivier; Nelson, Brian; Stevens, Scott; Seo, Dong-Jun; Kim, Beomgeun

    2015-04-01

    The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (NEXRAD) network over Continental United States (CONUS) is completed for the period covering from 2001 to 2012. This important milestone constitutes a unique opportunity to study precipitation processes at a 1-km spatial resolution for a 5-min temporal resolution. However, in order to be suitable for hydrological, meteorological and climatological applications, the radar-only product needs to be bias-adjusted and merged with in-situ rain gauge information. Several in-situ datasets are available to assess the biases of the radar-only product and to adjust for those biases to provide a multi-sensor QPE. The rain gauge networks that are used such as the Global Historical Climatology Network-Daily (GHCN-D), the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), and the Climate Reference Network (CRN), have different spatial density and temporal resolution. The challenges related to incorporating non-homogeneous networks over a vast area and for a long-term record are enormous. Among the challenges we are facing are the difficulties incorporating differing resolution and quality surface measurements to adjust gridded estimates of precipitation. Another challenge is the type of adjustment technique. The objective of this work is threefold. First, we investigate how the different in-situ networks can impact the precipitation estimates as a function of the spatial density, sensor type, and temporal resolution. Second, we assess conditional and un-conditional biases of the radar-only QPE for various time scales (daily, hourly, 5-min) using in-situ precipitation observations. Finally, after assessing the bias and applying reduction or elimination techniques, we are using a unique in-situ dataset merging the different RG networks (CRN, ASOS, HADS, GHCN-D) to

  20. Assessment of Evolving TRMM-Based Real-Time Precipitation Estimation Methods and Their Impacts on Hydrologic Prediction in a High-Latitude Basin

    Science.gov (United States)

    Yong, Bin; Hong, Yang; Ren, Li-Liang; Gourley, Jonathan; Huffman, George J.; Chen, Xi; Wang, Wen; Khan, Sadiq I.

    2013-01-01

    The real-time availability of satellite-derived precipitation estimates provides hydrologists an opportunity to improve current hydrologic prediction capability for medium to large river basins. Due to the availability of new satellite data and upgrades to the precipitation algorithms, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis real-time estimates (TMPA-RT) have been undergoing several important revisions over the past ten years. In this study, the changes of the relative accuracy and hydrologic potential of TMPA-RT estimates over its three major evolving periods were evaluated and inter-compared at daily, monthly and seasonal scales in the high-latitude Laohahe basin in China. Assessment results show that the performance of TMPA-RT in terms of precipitation estimation and streamflow simulation was significantly improved after 3 February 2005. Overestimation during winter months was noteworthy and consistent, which is suggested to be a consequence from interference of snow cover to the passive microwave retrievals. Rainfall estimated by the new version 6 of TMPA-RT starting from 1 October 2008 to present has higher correlations with independent gauge observations and tends to perform better in detecting rain compared to the prior periods, although it suffers larger mean error and relative bias. After a simple bias correction, this latest dataset of TMPA-RT exhibited the best capability in capturing hydrologic response among the three tested periods. In summary, this study demonstrated that there is an increasing potential in the use of TMPA-RT in hydrologic streamflow simulations over its three algorithm upgrade periods, but still with significant challenges during the winter snowing events.

  1. The impact of reflectivity correction and conversion methods to improve precipitation estimation by weather radar for an extreme low-land Mesoscale Convective System

    Science.gov (United States)

    Hazenberg, Pieter; Leijnse, Hidde; Uijlenhoet, Remko

    2014-05-01

    Between 25 and 27 August 2010 a long-duration mesoscale convective system was observed above the Netherlands. For most of the country this led to over 15 hours of near-continuous precipitation, which resulted in total event accumulations exceeding 150 mm in the eastern part of the Netherlands. Such accumulations belong to the largest sums ever recorded in this country and gave rise to local flooding. Measuring precipitation by weather radar within such mesoscale convective systems is known to be a challenge, since measurements are affected by multiple sources of error. For the current event the operational weather radar rainfall product only estimated about 30% of the actual amount of precipitation as measured by rain gauges. In the current presentation we will try to identify what gave rise to such large underestimations. In general weather radar measurement errors can be subdivided into two different groups: 1) errors affecting the volumetric reflectivity measurements taken, and 2) errors related to the conversion of reflectivity values in rainfall intensity and attenuation estimates. To correct for the first group of errors, the quality of the weather radar reflectivity data was improved by successively correcting for 1) clutter and anomalous propagation, 2) radar calibration, 3) wet radome attenuation, 4) signal attenuation and 5) the vertical profile of reflectivity. Such consistent corrections are generally not performed by operational meteorological services. Results show a large improvement in the quality of the precipitation data, however still only ~65% of the actual observed accumulations was estimated. To further improve the quality of the precipitation estimates, the second group of errors are corrected for by making use of disdrometer measurements taken in close vicinity of the radar. Based on these data the parameters of a normalized drop size distribution are estimated for the total event as well as for each precipitation type separately (convective

  2. Assessing Changes in Precipitation and Impacts on Groundwater in Southeastern Brazil using Regional Hydroclimate Reconstruction

    Science.gov (United States)

    Nunes, A.; Fernandes, M.; Silva, G. C., Jr.

    2017-12-01

    Aquifers can be key players in regional water resources. Precipitation infiltration is the most relevant process in recharging the aquifers. In that regard, understanding precipitation changes and impacts on the hydrological cycle helps in the assessment of groundwater availability from the aquifers. Regional modeling systems can provide precipitation, near-surface air temperature, together with soil moisture at different ground levels from coupled land-surface schemes. More accurate those variables are better the evaluation of the precipitation impact on the groundwater. Downscaling of global reanalysis very often employs regional modeling systems, in order to give more detailed information for impact assessment studies at regional scales. In particular, the regional modeling system, Satellite-enhanced Regional Downscaling for Applied Studies (SRDAS), might improve the accuracy of hydrometeorological variables in regions with spatial and temporal scarcity of in-situ observations. SRDAS combines assimilation of precipitation estimates from gauge-corrected satellite-based products with spectral nudging technique. The SRDAS hourly outputs provide monthly means of atmospheric and land-surface variables, including precipitation, used in the calculations of the hydrological budget terms. Results show the impact of changes in precipitation on groundwater in the aquifer located near the southeastern coastline of Brazil, through the assessment of the water-cycle terms, using a hydrological model during dry and rainy periods found in the 15-year numerical integration of SRDAS.

  3. Satellite-based detection of volcanic sulphur dioxide from recent eruptions in Central and South America

    Directory of Open Access Journals (Sweden)

    D. Loyola

    2008-01-01

    Full Text Available Volcanic eruptions can emit large amounts of rock fragments and fine particles (ash into the atmosphere, as well as several gases, including sulphur dioxide (SO2. These ejecta and emissions are a major natural hazard, not only to the local population, but also to the infrastructure in the vicinity of volcanoes and to aviation. Here, we describe a methodology to retrieve quantitative information about volcanic SO2 plumes from satellite-borne measurements in the UV/Visible spectral range. The combination of a satellite-based SO2 detection scheme and a state-of-the-art 3D trajectory model enables us to confirm the volcanic origin of trace gas signals and to estimate the plume height and the effective emission height. This is demonstrated by case-studies for four selected volcanic eruptions in South and Central America, using the GOME, SCIAMACHY and GOME-2 instruments.

  4. Trellis coding with Continuous Phase Modulation (CPM) for satellite-based land-mobile communications

    Science.gov (United States)

    1989-01-01

    This volume of the final report summarizes the results of our studies on the satellite-based mobile communications project. It includes: a detailed analysis, design, and simulations of trellis coded, full/partial response CPM signals with/without interleaving over various Rician fading channels; analysis and simulation of computational cutoff rates for coherent, noncoherent, and differential detection of CPM signals; optimization of the complete transmission system; analysis and simulation of power spectrum of the CPM signals; design and development of a class of Doppler frequency shift estimators; design and development of a symbol timing recovery circuit; and breadboard implementation of the transmission system. Studies prove the suitability of the CPM system for mobile communications.

  5. Estimating long-term statistics for annual precipitation for six regions of the United States from tree-ring data

    International Nuclear Information System (INIS)

    Fritts, H.C.; DeWitt, E.; Gordon, G.A.; Hunt, J.H.; Lofgren, G.R.

    1979-12-01

    Spatial anomalies of seasonal precipitation for the United States and southwestern Canada have been reconstructed from 1602 through 1961 using dendrochronological and multivariate techniques on 65 arid-site tree-ring chronologies from western North America. Seasonal reconstructions are averaged to obtain mean annual precipitation values for six regions of importance to the Nuclear Regulatory Commission (NRC) Nuclear Waste Management Program (NWMP). Statistics calculated from the regionally averaged annual values for 25-year and longer intervals show annual precipitation in the seventeenth through nineteenth centuries to be lower than in the twentieth century for three regions in the American Southwest and higher for one region in the Northwest and two regions in the East. The variability of precipitation generally was higher in the past three centuries than in the present century. Twenty-five-year intervals with noteworthy statistics are identified and important results are summarized and tabulated for use in the hydrologic modeling of the NWMP. Additional research is recommended to incorporate temperature and precipitation into a single hydrologic parameter

  6. Precipitation-induced runoff and leaching from milled peat mining mires by peat types : a comparative method for estimating the loading of water bodies during peat pruduction

    OpenAIRE

    Svahnbäck, Lasse

    2007-01-01

    Precipitation-induced runoff and leaching from milled peat mining mires by peat types: a comparative method for estimating the loading of water bodies during peat production. This research project in environmental geology has arisen out of an observed need to be able to predict more accurately the loading of watercourses with detrimental organic substances and nutrients from already existing and planned peat production areas, since the authorities capacity for insisting on such predicti...

  7. SAMIRA - SAtellite based Monitoring Initiative for Regional Air quality

    Science.gov (United States)

    Schneider, Philipp; Stebel, Kerstin; Ajtai, Nicolae; Diamandi, Andrei; Horalek, Jan; Nicolae, Doina; Stachlewska, Iwona; Zehner, Claus

    2016-04-01

    Here, we present a new ESA-funded project entitled Satellite based Monitoring Initiative for Regional Air quality (SAMIRA), which aims at improving regional and local air quality monitoring through synergetic use of data from present and upcoming satellites, traditionally used in situ air quality monitoring networks and output from chemical transport models. Through collaborative efforts in four countries, namely Romania, Poland, the Czech Republic and Norway, all with existing air quality problems, SAMIRA intends to support the involved institutions and associated users in their national monitoring and reporting mandates as well as to generate novel research in this area. Despite considerable improvements in the past decades, Europe is still far from achieving levels of air quality that do not pose unacceptable hazards to humans and the environment. Main concerns in Europe are exceedances of particulate matter (PM), ground-level ozone, benzo(a)pyrene (BaP) and nitrogen dioxide (NO2). While overall sulfur dioxide (SO2) emissions have decreased in recent years, regional concentrations can still be high in some areas. The objectives of SAMIRA are to improve algorithms for the retrieval of hourly aerosol optical depth (AOD) maps from SEVIRI, and to develop robust methods for deriving column- and near-surface PM maps for the study area by combining satellite AOD with information from regional models. The benefit to existing monitoring networks (in situ, models, satellite) by combining these datasets using data fusion methods will be tested for satellite-based NO2, SO2, and PM/AOD. Furthermore, SAMIRA will test and apply techniques for downscaling air quality-related EO products to a spatial resolution that is more in line with what is generally required for studying urban and regional scale air quality. This will be demonstrated for a set of study sites that include the capitals of the four countries and the highly polluted areas along the border of Poland and the

  8. Improved infrared precipitation estimation approaches based on k-means clustering: Application to north Algeria using MSG-SEVIRI satellite data

    Science.gov (United States)

    Mokdad, Fatiha; Haddad, Boualem

    2017-06-01

    In this paper, two new infrared precipitation estimation approaches based on the concept of k-means clustering are first proposed, named the NAW-Kmeans and the GPI-Kmeans methods. Then, they are adapted to the southern Mediterranean basin, where the subtropical climate prevails. The infrared data (10.8 μm channel) acquired by MSG-SEVIRI sensor in winter and spring 2012 are used. Tests are carried out in eight areas distributed over northern Algeria: Sebra, El Bordj, Chlef, Blida, Bordj Menael, Sidi Aich, Beni Ourthilane, and Beni Aziz. The validation is performed by a comparison of the estimated rainfalls to rain gauges observations collected by the National Office of Meteorology in Dar El Beida (Algeria). Despite the complexity of the subtropical climate, the obtained results indicate that the NAW-Kmeans and the GPI-Kmeans approaches gave satisfactory results for the considered rain rates. Also, the proposed schemes lead to improvement in precipitation estimation performance when compared to the original algorithms NAW (Nagri, Adler, and Wetzel) and GPI (GOES Precipitation Index).

  9. Rainfall frequency analysis for ungauged sites using satellite precipitation products

    Science.gov (United States)

    Gado, Tamer A.; Hsu, Kuolin; Sorooshian, Soroosh

    2017-11-01

    The occurrence of extreme rainfall events and their impacts on hydrologic systems and society are critical considerations in the design and management of a large number of water resources projects. As precipitation records are often limited or unavailable at many sites, it is essential to develop better methods for regional estimation of extreme rainfall at these partially-gauged or ungauged sites. In this study, an innovative method for regional rainfall frequency analysis for ungauged sites is presented. The new method (hereafter, this is called the RRFA-S) is based on corrected annual maximum series obtained from a satellite precipitation product (e.g., PERSIANN-CDR). The probability matching method (PMM) is used here for bias correction to match the CDF of satellite-based precipitation data with the gauged data. The RRFA-S method was assessed through a comparative study with the traditional index flood method using the available annual maximum series of daily rainfall in two different regions in USA (11 sites in Colorado and 18 sites in California). The leave-one-out cross-validation technique was used to represent the ungauged site condition. Results of this numerical application have found that the quantile estimates obtained from the new approach are more accurate and more robust than those given by the traditional index flood method.

  10. Combining weather radar nowcasts and numerical weather prediction models to estimate short-term quantitative precipitation and uncertainty

    DEFF Research Database (Denmark)

    Jensen, David Getreuer

    The topic of this Ph.D. thesis is short term forecasting of precipitation for up to 6 hours called nowcasts. The focus is on improving the precision of deterministic nowcasts, assimilation of radar extrapolation model (REM) data into Danish Meteorological Institutes (DMI) HIRLAM numerical weather...

  11. Satellite-based detection of global urban heat-island temperature influence

    Science.gov (United States)

    Gallo, K.P.; Adegoke, Jimmy O.; Owen, T.W.; Elvidge, C.D.

    2002-01-01

    This study utilizes a satellite-based methodology to assess the urban heat-island influence during warm season months for over 4400 stations included in the Global Historical Climatology Network of climate stations. The methodology includes local and regional satellite retrievals of an indicator of the presence green photosynthetically active vegetation at and around the stations. The difference in local and regional samples of the normalized difference vegetation index (NDVI) is used to estimate differences in mean air temperature. Stations classified as urban averaged 0.90??C (N. Hemisphere) and 0.92??C (S. Hemisphere) warmer than the surrounding environment on the basis of the NDVI-derived temperature estimates. Additionally, stations classified as rural averaged 0.19??C (N. Hemisphere) and 0.16??C (S. Hemisphere) warmer than the surrounding environment. The NDVI-derived temperature estimates were found to be in reasonable agreement with temperature differences observed between climate stations. The results suggest that satellite-derived data sets can be used to estimate the urban heat-island temperature influence on a global basis and that a more detailed analysis of rural stations and their surrounding environment may be necessary to assure that temperature trends derived from assumed rural environments are not influenced by changes in land use/land cover. Copyright 2002 by the American Geophysical Union.

  12. Long-Term Large-Scale Bias-Adjusted Precipitation Estimates at High Spatial and Temporal Resolution Derived from the National Mosaic and Multi-Sensor QPE (NMQ/Q2) Precipitation Reanalysis over CONUS

    Science.gov (United States)

    Prat, O. P.; Nelson, B. R.; Stevens, S. E.; Seo, D. J.; Kim, B.

    2014-12-01

    The processing of radar-only precipitation via the reanalysis from the National Mosaic and Multi-Sensor Quantitative (NMQ/Q2) based on the WSR-88D Next-generation Radar (Nexrad) network over Continental United States (CONUS) is nearly completed for the period covering from 2000 to 2012. This important milestone constitutes a unique opportunity to study precipitation processes at a 1-km spatial resolution for a 5-min temporal resolution. However, in order to be suitable for hydrological, meteorological and climatological applications, the radar-only product needs to be bias-adjusted and merged with in-situ rain gauge information. Rain gauge networks such as the Hydrometeorological Automated Data System (HADS), the Automated Surface Observing Systems (ASOS), the Climate Reference Network (CRN), and the Global Historical Climatology Network - Daily (GHCN-D) are used to adjust for those biases and to merge with the radar only product to provide a multi-sensor estimate. The challenges related to incorporating non-homogeneous networks over a vast area and for a long-term record are enormous. Among the challenges we are facing are the difficulties incorporating differing resolution and quality surface measurements to adjust gridded estimates of precipitation. Another challenge is the type of adjustment technique. After assessing the bias and applying reduction or elimination techniques, we are investigating the kriging method and its variants such as simple kriging (SK), ordinary kriging (OK), and conditional bias-penalized Kriging (CBPK) among others. In addition we hope to generate estimates of uncertainty for the gridded estimate. In this work the methodology is presented as well as a comparison between the radar-only product and the final multi-sensor QPE product. The comparison is performed at various time scales from the sub-hourly, to annual. In addition, comparisons over the same period with a suite of lower resolution QPEs derived from ground based radar

  13. Multitemporal Monitoring of the Air Quality in Bulgaria by Satellite Based Instruments

    Science.gov (United States)

    Nikolov, Hristo; Borisova, Denitsa

    2015-04-01

    Nowadays the effect on climate changes on the population and environment caused by air pollutants at local and regional scale by pollution concentrations higher than allowed is undisputable. Main sources of gas releases are due to anthropogenic emissions caused by the economic and domestic activities of the inhabitants, and to less extent having natural origin. Complementary to pollutants emissions the local weather parameters such as temperature, precipitation, wind speed, clouds, atmospheric water vapor, and wind direction control the chemical reactions in the atmosphere. It should be noted that intrinsic property of the air pollution is its "transboundary-ness" and this is why the air quality (AQ) is not affecting the population of one single country only. This why the exchange of information concerning AQ at EU level is subject to well established legislation and one of EU flagship initiatives for standardization in data exchange, namely INSPIRE, has to cope with. It should be noted that although good reporting mechanism with regard to AQ is already established between EU member states national networks suffer from a serious disadvantage - they don't form a regular grid which is a prerequisite for verification of pollutants transport modeling. Alternative sources of information for AQ are the satellite observations (i.e. OMI, TOMS instruments) providing daily data for ones of the major contributors to air pollution such as O3, NOX and SO2. Those data form regular grids and are processed the same day of the acquisition so they could be used in verification of the outputs generated by numerical modeling of the AQ and pollution transfer. In this research we present results on multitemporal monitoring of several regional "hot spots" responsible for greenhouse gases emissions in Bulgaria with emphasis on satellite-based instruments. Other output from this study is a method for validation of the AQ forecasts and also providing feedback to the service that prepares

  14. Quantitative estimation of orographic precipitation over the Himalayas by using TRMM/PR and a dense network of rain gauges

    Science.gov (United States)

    Yatagai, A.

    2009-04-01

    Precipitation Radar (PR) data acquired by the Tropical Rainfall Measuring Mission (TRMM) over 10 years of observation were used to show the monthly rainfall patterns over the Himalayas. To validate and adjust these patterns, we used a dense network of rain gauges to measure daily precipitation over Nepal, Bangladesh, Bhutan, Pakistan, India, Myanmar, and China. We then compared TRMM/PR and rain gauge data in 0.05-degree grid cells (an approximately 5.5-km mesh). Compared with the rain gauge observations, the PR systematically underestimated precipitation by 28-38% in summer (July-September).Significant correlation between TRMM/PR and RG data was found for all months, but the correlation is relatively low in winter. The relationship is investigated for different elevation zones, and the PR was found to underestimate RG data in most zones, except for certain zones in February (250-1000m), March (0-1000m), and April (0-1500m). Monthly PR climatology was adjusted on the basis of monthly regressions between the two sets of data and depicted.

  15. Mean precipitation estimation, rain gauge network evaluation and quantification of the hydrologic balance in the River Quito basin in Choco, state of Colombia

    International Nuclear Information System (INIS)

    Cordoba, Samir; Zea, Jorge A; Murillo, W

    2006-01-01

    In this work the calculation of the average precipitation in the Quito River basin, state of Choco, Colombia, is presents through diverse techniques, among which are those suggested by Thiessen and those based on the isohyets analysis, in order to select the one appropriate to quantification of rainwater available to the basin. Also included is an estimation of the error with which the average precipitation in the zone studied is fraught when measured, by means of the methodology proposed by Gandin (1970) and Kagan (WMO, 1966), which at the same time allows to evaluate the representativeness of each one of the stations that make up the rain gauge network in the area. The study concludes with a calculation of the hydrologic balance for the Quito river basin based on the pilot procedure suggested in the UNESCO publication on the study of the South America hydrologic balance, from which the great contribution of rainfall to a greatly enhanced run-off may be appreciated

  16. Dissemination of satellite-based river discharge and flood data

    Science.gov (United States)

    Kettner, A. J.; Brakenridge, G. R.; van Praag, E.; de Groeve, T.; Slayback, D. A.; Cohen, S.

    2014-12-01

    In collaboration with NASA Goddard Spaceflight Center and the European Commission Joint Research Centre, the Dartmouth Flood Observatory (DFO) daily measures and distributes: 1) river discharges, and 2) near real-time flood extents with a global coverage. Satellite-based passive microwave sensors and hydrological modeling are utilized to establish 'remote-sensing based discharge stations', and observed time series cover 1998 to the present. The advantages over in-situ gauged discharges are: a) easy access to remote or due to political reasons isolated locations, b) relatively low maintenance costs to maintain a continuous observational record, and c) the capability to obtain measurements during floods, hazardous conditions that often impair or destroy in-situ stations. Two MODIS instruments aboard the NASA Terra and Aqua satellites provide global flood extent coverage at a spatial resolution of 250m. Cloud cover hampers flood extent detection; therefore we ingest 6 images (the Terra and Aqua images of each day, for three days), in combination with a cloud shadow filter, to provide daily global flood extent updates. The Flood Observatory has always made it a high priority to visualize and share its data and products through its website. Recent collaborative efforts with e.g. GeoSUR have enhanced accessibility of DFO data. A web map service has been implemented to automatically disseminate geo-referenced flood extent products into client-side GIS software. For example, for Latin America and the Caribbean region, the GeoSUR portal now displays current flood extent maps, which can be integrated and visualized with other relevant geographical data. Furthermore, the flood state of satellite-observed river discharge sites are displayed through the portal as well. Additional efforts include implementing Open Geospatial Consortium (OGC) standards to incorporate Water Markup Language (WaterML) data exchange mechanisms to further facilitate the distribution of the satellite

  17. Performance of the Falling Snow Retrieval Algorithms for the Global Precipitation Measurement (GPM) Mission

    Science.gov (United States)

    Skofronick-Jackson, Gail; Munchak, Stephen J.; Ringerud, Sarah

    2016-01-01

    Retrievals of falling snow from space represent an important data set for understanding the Earth's atmospheric, hydrological, and energy cycles, especially during climate change. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new and retrievals are still undergoing development with challenges remaining). This work reports on the development and testing of retrieval algorithms for the Global Precipitation Measurement (GPM) mission Core Satellite, launched February 2014.

  18. First Evaluation of the Climatological Calibration Algorithm in the Real-time TMPA Precipitation Estimates over Two Basins at High and Low Latitudes

    Science.gov (United States)

    Yong, Bin; Ren, Liliang; Hong, Yang; Gourley, Jonathan; Tian, Yudong; Huffman, George J.; Chen, Xi; Wang, Weiguang; Wen, Yixin

    2013-01-01

    The TRMM Multi-satellite Precipitation Analysis (TMPA) system underwent a crucial upgrade in early 2009 to include a climatological calibration algorithm (CCA) to its realtime product 3B42RT, and this algorithm will continue to be applied in the future Global Precipitation Measurement era constellation precipitation products. In this study, efforts are focused on the comparison and validation of the Version 6 3B42RT estimates before and after the climatological calibration is applied. The evaluation is accomplished using independent rain gauge networks located within the high-latitude Laohahe basin and the low-latitude Mishui basin, both in China. The analyses indicate the CCA can effectively reduce the systematic errors over the low-latitude Mishui basin but misrepresent the intensity distribution pattern of medium-high rain rates. This behavior could adversely affect TMPA's hydrological applications, especially for extreme events (e.g., floods and landslides). Results also show that the CCA tends to perform slightly worse, in particular, during summer and winter, over the high-latitude Laohahe basin. This is possibly due to the simplified calibration-processing scheme in the CCA that directly applies the climatological calibrators developed within 40 degrees latitude to the latitude belts of 40 degrees N-50 degrees N. Caution should therefore be exercised when using the calibrated 3B42RT for heavy rainfall-related flood forecasting (or landslide warning) over high-latitude regions, as the employment of the smooth-fill scheme in the CCA bias correction could homogenize the varying rainstorm characteristics. Finally, this study highlights that accurate detection and estimation of snow at high latitudes is still a challenging task for the future development of satellite precipitation retrievals.

  19. Estimating the Seasonal Importance of Precipitation to Plant Source Water over Time and Space with Water Isotopes

    Science.gov (United States)

    Nelson, D. B.; Kahmen, A.

    2017-12-01

    The stable isotopic composition of hydrogen and oxygen are physical properties of water molecules that can carry information on their sources or transport histories. This provides a useful tool for assessing the importance of rainfall at different times of the year for plant growth, provided that rainwater values vary over time and that waters do not partially evaporate after deposition. We tested the viability of this approach using data from samples collected at nineteen sites throughout Europe at monthly intervals over two consecutive growing seasons in 2014 and 2015. We compared isotope measurements of plant xylem water with soil water from multiple depths, and measured and modeled precipitation isotope values. Paired analyses of oxygen and hydrogen isotope values were used to screen out a limited number of water samples that were influenced by evaporation, with the majority of all water samples indicating meteoric sources. The isotopic composition of soil and xylem waters varied over the course of an individual growing season, with many trending towards more enriched values, suggesting integration of the plant-relevant water pool at a timescale shorter than the annual mean. We then quantified how soil water residence times varied at each site by calculating the interval between measured xylem water and the most recently preceding match in modeled precipitation isotope values. Results suggest a generally increasing interval between rainfall and plant uptake throughout each year, with source water corresponding to dates in the spring, likely reflecting a combination of spring rain, and mixing with winter and summer precipitation. The seasonally evolving spatial distribution of source water-precipitation lag values was then modeled as a function of location and climatology to develop continental-scale predictions. This spatial portrait of the average date for filling the plant source water pool provides insights on the seasonal importance of rainfall for plant

  20. Comparison of different statistical downscaling methods to estimate changes in hourly extreme precipitation using RCM projections from ENSEMBLES

    DEFF Research Database (Denmark)

    Sunyer Pinya, Maria Antonia; Gregersen, Ida Bülow; Rosbjerg, Dan

    2015-01-01

    change method for extreme events, a weather generator combined with a disaggregation method and a climate analogue method. All three methods rely on different assumptions and use different outputs from the regional climate models (RCMs). The results of the three methods point towards an increase...... in extreme precipitation but the magnitude of the change varies depending on the RCM used and the spatial location. In general, a similar mean change is obtained for the three methods. This adds confidence in the results as each method uses different information from the RCMs. The results of this study...

  1. Evaluation of Bias Correction Method for Satellite-Based Rainfall Data.

    Science.gov (United States)

    Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter

    2016-06-15

    With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003-2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW's) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.

  2. Evaluation of Bias Correction Method for Satellite-Based Rainfall Data

    Directory of Open Access Journals (Sweden)

    Haris Akram Bhatti

    2016-06-01

    Full Text Available With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA Climate Prediction Centre (CPC morphing technique (CMORPH satellite rainfall product (CMORPH in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW sizes and for sequential windows (SW’s of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE. To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r and standard deviation (SD. Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.

  3. Evaluation of Bias Correction Method for Satellite-Based Rainfall Data

    Science.gov (United States)

    Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter

    2016-01-01

    With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW’s) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach. PMID:27314363

  4. Operational Satellite-based Surface Oil Analyses (Invited)

    Science.gov (United States)

    Streett, D.; Warren, C.

    2010-12-01

    During the Deepwater Horizon spill, NOAA imagery analysts in the Satellite Analysis Branch (SAB) issued more than 300 near-real-time satellite-based oil spill analyses. These analyses were used by the oil spill response community for planning, issuing surface oil trajectories and tasking assets (e.g., oil containment booms, skimmers, overflights). SAB analysts used both Synthetic Aperture Radar (SAR) and high resolution visible/near IR multispectral satellite imagery as well as a variety of ancillary datasets. Satellite imagery used included ENVISAT ASAR (ESA), TerraSAR-X (DLR), Cosmo-Skymed (ASI), ALOS (JAXA), Radarsat (MDA), ENVISAT MERIS (ESA), SPOT (SPOT Image Corp.), Aster (NASA), MODIS (NASA), and AVHRR (NOAA). Ancillary datasets included ocean current information, wind information, location of natural oil seeps and a variety of in situ oil observations. The analyses were available as jpegs, pdfs, shapefiles and through Google, KML files and also available on a variety of websites including Geoplatform and ERMA. From the very first analysis issued just 5 hours after the rig sank through the final analysis issued in August, the complete archive is still publicly available on the NOAA/NESDIS website http://www.ssd.noaa.gov/PS/MPS/deepwater.html SAB personnel also served as the Deepwater Horizon International Disaster Charter Project Manager (at the official request of the USGS). The Project Manager’s primary responsibility was to acquire and oversee the processing and dissemination of satellite data generously donated by numerous private companies and nations in support of the oil spill response including some of the imagery described above. SAB has begun to address a number of goals that will improve our routine oil spill response as well as help assure that we are ready for the next spill of national significance. We hope to (1) secure a steady, abundant and timely stream of suitable satellite imagery even in the absence of large-scale emergencies such as

  5. Constraining frequency–magnitude–area relationships for rainfall and flood discharges using radar-derived precipitation estimates: example applications in the Upper and Lower Colorado River basins, USA

    Directory of Open Access Journals (Sweden)

    C. A. Orem

    2016-11-01

    Full Text Available Flood-envelope curves (FECs are useful for constraining the upper limit of possible flood discharges within drainage basins in a particular hydroclimatic region. Their usefulness, however, is limited by their lack of a well-defined recurrence interval. In this study we use radar-derived precipitation estimates to develop an alternative to the FEC method, i.e., the frequency–magnitude–area-curve (FMAC method that incorporates recurrence intervals. The FMAC method is demonstrated in two well-studied US drainage basins, i.e., the Upper and Lower Colorado River basins (UCRB and LCRB, respectively, using Stage III Next-Generation-Radar (NEXRAD gridded products and the diffusion-wave flow-routing algorithm. The FMAC method can be applied worldwide using any radar-derived precipitation estimates. In the FMAC method, idealized basins of similar contributing area are grouped together for frequency–magnitude analysis of precipitation intensity. These data are then routed through the idealized drainage basins of different contributing areas, using contributing-area-specific estimates for channel slope and channel width. Our results show that FMACs of precipitation discharge are power-law functions of contributing area with an average exponent of 0.82 ± 0.06 for recurrence intervals from 10 to 500 years. We compare our FMACs to published FECs and find that for wet antecedent-moisture conditions, the 500-year FMAC of flood discharge in the UCRB is on par with the US FEC for contributing areas of  ∼ 102 to 103 km2. FMACs of flood discharge for the LCRB exceed the published FEC for the LCRB for contributing areas in the range of  ∼ 103 to 104 km2. The FMAC method retains the power of the FEC method for constraining flood hazards in basins that are ungauged or have short flood records, yet it has the added advantage that it includes recurrence-interval information necessary for estimating event probabilities.

  6. THE EVOLUTION OF ANNUAL MEAN TEMPERATURE AND PRECIPITATION QUANTITY VARIABILITY BASED ON ESTIMATED CHANGES BY THE REGIONAL CLIMATIC MODELS

    Directory of Open Access Journals (Sweden)

    Paula Furtună

    2013-03-01

    Full Text Available Climatic changes are representing one of the major challenges of our century, these being forcasted according to climate scenarios and models, which represent plausible and concrete images of future climatic conditions. The results of climate models comparison regarding future water resources and temperature regime trend can become a useful instrument for decision makers in choosing the most effective decisions regarding economic, social and ecologic levels. The aim of this article is the analysis of temperature and pluviometric variability at the closest grid point to Cluj-Napoca, based on data provided by six different regional climate models (RCMs. Analysed on 30 year periods (2001-2030,2031-2060 and 2061-2090, the mean temperature has an ascending general trend, with great varability between periods. The precipitation expressed trough percentage deviation shows a descending general trend, which is more emphazied during 2031-2060 and 2061-2090.

  7. A simulation study of the recession coefficient for antecedent precipitation index. [soil moisture and water runoff estimation

    Science.gov (United States)

    Choudhury, B. J.; Blanchard, B. J.

    1981-01-01

    The antecedent precipitation index (API) is a useful indicator of soil moisture conditions for watershed runoff calculations and recent attempts to correlate this index with spaceborne microwave observations have been fairly successful. It is shown that the prognostic equation for soil moisture used in some of the atmospheric general circulation models together with Thornthwaite-Mather parameterization of actual evapotranspiration leads to API equations. The recession coefficient for API is found to depend on climatic factors through potential evapotranspiration and on soil texture through the field capacity and the permanent wilting point. Climatologial data for Wisconsin together with a recently developed model for global isolation are used to simulate the annual trend of the recession coefficient. Good quantitative agreement is shown with the observed trend at Fennimore and Colby watersheds in Wisconsin. It is suggested that API could be a unifying vocabulary for watershed and atmospheric general circulation modelars.

  8. Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part II: Evaluation of Estimates Using Independent Data

    Science.gov (United States)

    Yang, Song; Olson, William S.; Wang, Jian-Jian; Bell, Thomas L.; Smith, Eric A.; Kummerow, Christian D.

    2006-01-01

    Rainfall rate estimates from spaceborne microwave radiometers are generally accepted as reliable by a majority of the atmospheric science community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain-rate algorithms is based upon passive microwave observations from the TRMM Microwave Imager (TMI). In Part I of this series, improvements of the TMI algorithm that are required to introduce latent heating as an additional algorithm product are described. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, 0.5 deg. -resolution estimates of surface rain rate over ocean from the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over earlier algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly 2.5 -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data is limited, TMI-estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain-rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with (a) additional contextual information brought to the estimation problem and/or (b) physically consistent and representative databases supporting the algorithm. A model of the random error in instantaneous 0.5 deg. -resolution rain-rate estimates appears to be consistent with the levels of error determined from TMI comparisons with collocated

  9. Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part 2; Evaluation of Estimates Using Independent Data

    Science.gov (United States)

    Yang, Song; Olson, William S.; Wang, Jian-Jian; Bell, Thomas L.; Smith, Eric A.; Kummerow, Christian D.

    2004-01-01

    Rainfall rate estimates from space-borne k&ents are generally accepted as reliable by a majority of the atmospheric science commu&y. One-of the Tropical Rainfall Measuring Mission (TRh4M) facility rain rate algorithms is based upon passive microwave observations fiom the TRMM Microwave Imager (TMI). Part I of this study describes improvements in the TMI algorithm that are required to introduce cloud latent heating and drying as additional algorithm products. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, OP5resolution estimates of surface rain rate over ocean fiom the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over forerunning algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm, and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly, 2.5 deg. -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data are limited, TMI estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with: (a) additional contextual information brought to the estimation problem, and/or; (b) physically-consistent and representative databases supporting the algorithm. A model of the random error in instantaneous, 0.5 deg-resolution rain rate estimates appears to be consistent with the levels of error determined from TMI comparisons to collocated radar

  10. Ground- and satellite-based evidence of the biophysical mechanisms behind the greening Sahel.

    Science.gov (United States)

    Brandt, Martin; Mbow, Cheikh; Diouf, Abdoul A; Verger, Aleixandre; Samimi, Cyrus; Fensholt, Rasmus

    2015-04-01

    After a dry period with prolonged droughts in the 1970s and 1980s, recent scientific outcome suggests that the decades of abnormally dry conditions in the Sahel have been reversed by positive anomalies in rainfall. Various remote sensing studies observed a positive trend in vegetation greenness over the last decades which is known as the re-greening of the Sahel. However, little investment has been made in including long-term ground-based data collections to evaluate and better understand the biophysical mechanisms behind these findings. Thus, deductions on a possible increment in biomass remain speculative. Our aim is to bridge these gaps and give specifics on the biophysical background factors of the re-greening Sahel. Therefore, a trend analysis was applied on long time series (1987-2013) of satellite-based vegetation and rainfall data, as well as on ground-observations of leaf biomass of woody species, herb biomass, and woody species abundance in different ecosystems located in the Sahel zone of Senegal. We found that the positive trend observed in satellite vegetation time series (+36%) is caused by an increment of in situ measured biomass (+34%), which is highly controlled by precipitation (+40%). Whereas herb biomass shows large inter-annual fluctuations rather than a clear trend, leaf biomass of woody species has doubled within 27 years (+103%). This increase in woody biomass did not reflect on biodiversity with 11 of 16 woody species declining in abundance over the period. We conclude that the observed greening in the Senegalese Sahel is primarily related to an increasing tree cover that caused satellite-driven vegetation indices to increase with rainfall reversal. © 2014 John Wiley & Sons Ltd.

  11. Simultaneous imaging of aurora on small scale in OI (777.4 nm and N21P to estimate energy and flux of precipitation

    Directory of Open Access Journals (Sweden)

    N. Ivchenko

    2009-07-01

    Full Text Available Simultaneous images of the aurora in three emissions, N21P (673.0 nm, OII (732.0 nm and OI (777.4 nm, have been analysed; the ratio of atomic oxygen to molecular nitrogen has been used to provide estimates of the changes in energy and flux of precipitation within scale sizes of 100 m, and with temporal resolution of 32 frames per second. The choice of filters for the imagers is discussed, with particular emphasis on the choice of the atomic oxygen line at 777.4 nm as one of the three emissions measured. The optical measurements have been combined with radar measurements and compared with the results of an auroral model, hence showing that the ratio of emission rates OI/N2 can be used to estimate the energy within the smallest auroral structures. In the event chosen, measurements were made from mainland Norway, near Tromso, (69.6 N, 19.2 E. The peak energies of precipitation were between 1–15 keV. In a narrow curling arc, it was found that the arc filaments resulted from energies in excess of 10 keV and fluxes of approximately 7 mW/m2. These filaments of the order of 100 m in width were embedded in a region of lower energies (about 5–10 keV and fluxes of about 3 mW/m2. The modelling results show that the method promises to be most powerful for detecting low energy precipitation, more prevalent at the higher latitudes of Svalbard where the multispectral imager, known as ASK, is now installed.

  12. Simultaneous imaging of aurora on small scale in OI (777.4 nm and N21P to estimate energy and flux of precipitation

    Directory of Open Access Journals (Sweden)

    B. S. Lanchester

    2009-07-01

    Full Text Available Simultaneous images of the aurora in three emissions, N21P (673.0 nm, OII (732.0 nm and OI (777.4 nm, have been analysed; the ratio of atomic oxygen to molecular nitrogen has been used to provide estimates of the changes in energy and flux of precipitation within scale sizes of 100 m, and with temporal resolution of 32 frames per second. The choice of filters for the imagers is discussed, with particular emphasis on the choice of the atomic oxygen line at 777.4 nm as one of the three emissions measured. The optical measurements have been combined with radar measurements and compared with the results of an auroral model, hence showing that the ratio of emission rates OI/N2 can be used to estimate the energy within the smallest auroral structures. In the event chosen, measurements were made from mainland Norway, near Troms\\o, (69.6 N, 19.2 E. The peak energies of precipitation were between 1–15 keV. In a narrow curling arc, it was found that the arc filaments resulted from energies in excess of 10 keV and fluxes of approximately 7 mW/m2. These filaments of the order of 100 m in width were embedded in a region of lower energies (about 5–10 keV and fluxes of about 3 mW/m2. The modelling results show that the method promises to be most powerful for detecting low energy precipitation, more prevalent at the higher latitudes of Svalbard where the multispectral imager, known as ASK, is now installed.

  13. An operational procedure for precipitable and cloud liquid water estimate in non-raining conditions over sea Study on the assessment of the nonlinear physical inversion algorithm

    CERN Document Server

    Nativi, S; Mazzetti, P

    2004-01-01

    In a previous work, an operative procedure to estimate precipitable and liquid water in non-raining conditions over sea was developed and assessed. The procedure is based on a fast non-linear physical inversion scheme and a forward model; it is valid for most of satellite microwave radiometers and it also estimates water effective profiles. This paper presents two improvements of the procedure: first, a refinement to provide modularity of the software components and portability across different computation system architectures; second, the adoption of the CERN MINUIT minimisation package, which addresses the problem of global minimisation but is computationally more demanding. Together with the increased computational performance that allowed to impose stricter requirements on the quality of fit, these refinements improved fitting precision and reliability, and allowed to relax the requirements on the initial guesses for the model parameters. The re-analysis of the same data-set considered in the previous pap...

  14. Highlights of satellite-based forest change recognition and tracking using the ForWarn System

    Science.gov (United States)

    Steven P. Norman; William W. Hargrove; Joseph P. Spruce; William M. Christie; Sean W. Schroeder

    2013-01-01

    For a higher resolution version of this file, please use the following link: www.geobabble.orgSatellite-based remote sensing can assist forest managers with their need to recognize disturbances and track recovery. Despite the long...

  15. Goddard Satellite-Based Surface Turbulent Fluxes Climatology, Yearly Grid V3

    Data.gov (United States)

    National Aeronautics and Space Administration — These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-3 Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie...

  16. Goddard Satellite-Based Surface Turbulent Fluxes Climatology, Seasonal Grid V3

    Data.gov (United States)

    National Aeronautics and Space Administration — These data are the Goddard Satellite-based Surface Turbulent Fluxes Version-3 Dataset recently produced through a MEaSUREs funded project led by Dr. Chung-Lin Shie...

  17. Land Data Assimilation of Satellite-Based Soil Moisture Products Using the Land Information System Over the NLDAS Domain

    Science.gov (United States)

    Mocko, David M.; Kumar, S. V.; Peters-Lidard, C. D.; Tian, Y.

    2011-01-01

    This presentation will include results from data assimilation simulations using the NASA-developed Land Information System (LIS). Using the ensemble Kalman filter in LIS, two satellite-based soil moisture products from the AMSR-E instrument were assimilated, one a NASA-based product and the other from the Land Parameter Retrieval Model (LPRM). The domain and land-surface forcing data from these simulations were from the North American Land Data Assimilation System Phase-2, over the period 2002-2008. The Noah land-surface model, version 3.2, was used during the simulations. Changes to estimates of land surface states, such as soil moisture, as well as changes to simulated runoff/streamflow will be presented. Comparisons over the NLDAS domain will also be made to two global reference evapotranspiration (ET) products, one an interpolated product based on FLUXNET tower data and the other a satellite- based algorithm from the MODIS instrument. Results of an improvement metric show that assimilating the LPRM product improved simulated ET estimates while the NASA-based soil moisture product did not.

  18. A review of the PERSIANN family global satellite precipitation data products

    Science.gov (United States)

    Nguyen, P.; Ombadi, M.; Ashouri, H.; Thorstensen, A.; Hsu, K. L.; Braithwaite, D.; Sorooshian, S.; William, L.

    2017-12-01

    Precipitation is an integral part of the hydrologic cycle and plays an important role in the water and energy balance of the Earth. Careful and consistent observation of precipitation is important for several reasons. Over the last two decades, the PERSIANN system of precipitation products have been developed at the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine in collaboration with NASA, NOAA and the UNESCO G-WADI program. The PERSIANN family includes three main satellite-based precipitation estimation products namely PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. They are accessible through several web-based interfaces maintained by CHRS to serve the needs of researchers, professionals and general public. These interfaces are CHRS iRain, Data Portal and RainSphere, which can be accessed at http://irain.eng.uci.edu, http://chrsdata.eng.uci.edu, and http://rainsphere.eng.uci.edu respectively and can be used for visualization, analysis or download of the data. The main objective of this presentation is to provide a concise and clear summary of the similarities and differences between the three products in terms of attributes and algorithm structure. Moreover, the presentation aims to provide an evaluation of the performance of the products over the Contiguous United States (CONUS) using Climate Prediction Center (CPC) precipitation dataset as a baseline of comparison. Also, an assessment of the behavior of PERSIANN family products over the globe (60°S - 60°N) is performed.

  19. Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products

    Directory of Open Access Journals (Sweden)

    Singaiah Chintalapudi

    2014-05-01

    Full Text Available In this study, seven precipitation products (rain gauges, NEXRAD MPE, PERSIANN 0.25 degree, PERSIANN CCS-3hr, PERSIANN CCS-1hr, TRMM 3B42V7, and CMORPH were used to force a physically-based distributed hydrologic model. The model was driven by these products to simulate the hydrologic response of a 1232 km2 watershed in the Guadalupe River basin, Texas. Storm events in 2007 were used to analyze the precipitation products. Comparison with rain gauge observations reveals that there were significant biases in the satellite rainfall products and large variations in the estimated amounts. The radar basin average precipitation compared very well with the rain gauge product while the gauge-adjusted TRMM 3B42V7 precipitation compared best with observed rainfall among all satellite precipitation products. The NEXRAD MPE simulated streamflows matched the observed ones the best yielding the highest Nash-Sutcliffe Efficiency correlation coefficient values for both the July and August 2007 events. Simulations driven by TRMM 3B42V7 matched the observed streamflow better than other satellite products for both events. The PERSIANN coarse resolution product yielded better runoff results than the higher resolution product. The study reveals that satellite rainfall products are viable alternatives when rain gauge or ground radar observations are sparse or non-existent.

  20. Total Discharge Estimation in the Korean Peninsula Using Multi-Satellite Products

    Directory of Open Access Journals (Sweden)

    Jae Young Seo

    2017-07-01

    Full Text Available Estimation of total discharge is necessary to understand the hydrological cycle and to manage water resources efficiently. However, the task is problematic in an area where ground observations are limited. The North Korea region is one example. Here, the total discharge was estimated based on the water balance using multiple satellite products. They are the terrestrial water storage changes (TWSC derived from the Gravity Recovery and Climate Experiment (GRACE, precipitation from the Tropical Rainfall Measuring Mission (TRMM, and evapotranspiration from the Moderate Resolution Imaging Spectroradiometer (MODIS. The satellite-based discharge was compared with land surface model products of the Global Land Data Assimilation System (GLDAS, and a positive relationship between the results was obtained (r = 0.70–0.86; bias = −9.08–16.99 mm/month; RMSE = 36.90–62.56 mm/month; NSE = 0.01–0.62. Among the four land surface models of GLDAS (CLM, Mosaic, Noah, and VIC, CLM corresponded best with the satellite-based discharge, satellite-based discharge has a tendency to slightly overestimate compared to model-based discharge (CLM, Mosaic, Noah, and VIC in the dry season. Also, the total discharge data based on the Precipitation-Runoff Modeling System (PRMS and the in situ discharge for major five river basins in South Korea show comparable seasonality and high correlation with the satellite-based discharge. In spite of the relatively low spatial resolution of GRACE, and loss of information incurred during the process of integrating three different satellite products, the proposed methodology can be a practical tool to estimate the total discharge with reasonable accuracy, especially in a region with scarce hydrologic data.

  1. The Satellite based Monitoring Initiative for Regional Air quality (SAMIRA): Project summary and first results

    Science.gov (United States)

    Schneider, Philipp; Stebel, Kerstin; Ajtai, Nicolae; Diamandi, Andrei; Horalek, Jan; Nemuc, Anca; Stachlewska, Iwona; Zehner, Claus

    2017-04-01

    We present a summary and some first results of a new ESA-funded project entitled Satellite based Monitoring Initiative for Regional Air quality (SAMIRA), which aims at improving regional and local air quality monitoring through synergetic use of data from present and upcoming satellite instruments, traditionally used in situ air quality monitoring networks and output from chemical transport models. Through collaborative efforts in four countries, namely Romania, Poland, the Czech Republic and Norway, all with existing air quality problems, SAMIRA intends to support the involved institutions and associated users in their national monitoring and reporting mandates as well as to generate novel research in this area. The primary goal of SAMIRA is to demonstrate the usefulness of existing and future satellite products of air quality for improving monitoring and mapping of air pollution at the regional scale. A total of six core activities are being carried out in order to achieve this goal: Firstly, the project is developing and optimizing algorithms for the retrieval of hourly aerosol optical depth (AOD) maps from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard of Meteosat Second Generation. As a second activity, SAMIRA aims to derive particulate matter (PM2.5) estimates from AOD data by developing robust algorithms for AOD-to-PM conversion with the support from model- and Lidar data. In a third activity, we evaluate the added value of satellite products of atmospheric composition for operational European-scale air quality mapping using geostatistics and auxiliary datasets. The additional benefit of satellite-based monitoring over existing monitoring techniques (in situ, models) is tested by combining these datasets using geostatistical methods and demonstrated for nitrogen dioxide (NO2), sulphur dioxide (SO2), and aerosol optical depth/particulate matter. As a fourth activity, the project is developing novel algorithms for downscaling coarse

  2. Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations.

    Directory of Open Access Journals (Sweden)

    Tao Wang

    Full Text Available Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique improve the simulation accuracy of mean seasonal (October throughout May snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the

  3. A Satellite-Based Model for Simulating Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

    Directory of Open Access Journals (Sweden)

    Rong Ge

    2018-01-01

    Full Text Available It is important to accurately evaluate ecosystem respiration (RE in the alpine grasslands of the Tibetan Plateau and the temperate grasslands of the Inner Mongolian Plateau, as it serves as a sensitivity indicator of regional and global carbon cycles. Here, we combined flux measurements taken between 2003 and 2013 from 16 grassland sites across northern China and the corresponding MODIS land surface temperature (LST, enhanced vegetation index (EVI, and land surface water index (LSWI to build a satellite-based model to estimate RE at a regional scale. First, the dependencies of both spatial and temporal variations of RE on these biotic and climatic factors were examined explicitly. We found that plant productivity and moisture, but not temperature, can best explain the spatial pattern of RE in northern China’s grasslands; while temperature plays a major role in regulating the temporal variability of RE in the alpine grasslands, and moisture is equally as important as temperature in the temperate grasslands. However, the moisture effect on RE and the explicit representation of spatial variation process are often lacking in most of the existing satellite-based RE models. On this basis, we developed a model by comprehensively considering moisture, temperature, and productivity effects on both temporal and spatial processes of RE, and then, we evaluated the model performance. Our results showed that the model well explained the observed RE in both the alpine (R2 = 0.79, RMSE = 0.77 g C m−2 day−1 and temperate grasslands (R2 = 0.75, RMSE = 0.60 g C m−2 day−1. The inclusion of the LSWI as the water-limiting factor substantially improved the model performance in arid and semi-arid ecosystems, and the spatialized basal respiration rate as an indicator for spatial variation largely determined the regional pattern of RE. Finally, the model accurately reproduced the seasonal and inter-annual variations and spatial variability of RE, and it avoided

  4. Satellite Based Downward Long Wave Radiation by Various Models in Northeast Asia

    Directory of Open Access Journals (Sweden)

    Chanyang Sur

    2014-01-01

    Full Text Available Satellite-based downward long wave radiation measurement under clear sky conditions in Northeast Asia was conducted using five well-known physical models (Brunt 1932, Idso and Jackson 1969, Brutsaert 1975, Satterlund 1979, Prata 1996 with a newly proposed global Rld model (Abramowitz et al. 2012. Data from two flux towers in South Korea were used to validate downward long wave radiation. Moderate resolution imaging spectroradiometer (MODIS atmospheric profile products were used to develop the Rld models. The overall root mean square error (RMSE of MODIS Rld with respect to two ecosystem-type flux towers was determined to be ≈ 20 W m-2. Based on the statistical analyses, MODIS Rld estimates with Brutsaert (1975 and Abramowitz et al. (2012 models were the most applicable for evaluating Rld for clear sky conditions in Northeast Asia. The Abramowitz Rld maps with MODIS Ta and ea showed reasonable seasonal patterns, which were well-aligned with other biophysical variables reported by previous studies. The MODIS Rld map developed in this study will be very useful for identifying spatial patterns that are not detectable from ground-based Rld measurement sites.

  5. Satellite based hydroclimatic understanding of evolution of Dengue and Zika virus

    Science.gov (United States)

    Khan, R.; Jutla, A.; Colwell, R. R.

    2017-12-01

    Vector-borne diseases are prevalent in tropical and subtropical regions especially in Africa, South America, and Asia. Vector eradication is perhaps not possible since pathogens adapt to local environment. In absence of appropriate vaccinations for Dengue and Zika virus, burden of these two infections continue to increase in several geographical locations. Aedes spp. is one of the major vectors for Dengue and Zika viruses. Etiologies on Dengue and Zika viruses are evolving, however the key question remains as to how one species of mosquito can transmit two different infections? We argue that a set of conducive environmental condition, modulated by regional climatic and weather processes, may lead to abundance of a specific virus. Using satellite based rainfall (TRMM/GPM), land surface temperature (MODIS) and dew point temperature (AIRS/MERRA), we have identified appropriate thresholds that can provide estimate on risk of abundance of Dengue or Zika viruses at least few weeks in advance. We will discuss a framework coupling satellite derived hydroclimatic and societal processes to predict environmental niches of favorability of conditions of Dengue or Zika risk in human population on a global scale.

  6. Improved Satellite-based Crop Yield Mapping by Spatially Explicit Parameterization of Crop Phenology

    Science.gov (United States)

    Jin, Z.; Azzari, G.; Lobell, D. B.

    2016-12-01

    Field-scale mapping of crop yields with satellite data often relies on the use of crop simulation models. However, these approaches can be hampered by inaccuracies in the simulation of crop phenology. Here we present and test an approach to use dense time series of Landsat 7 and 8 acquisitions data to calibrate various parameters related to crop phenology simulation, such as leaf number and leaf appearance rates. These parameters are then mapped across the Midwestern United States for maize and soybean, and for two different simulation models. We then implement our recently developed Scalable satellite-based Crop Yield Mapper (SCYM) with simulations reflecting the improved phenology parameterizations, and compare to prior estimates based on default phenology routines. Our preliminary results show that the proposed method can effectively alleviate the underestimation of early-season LAI by the default Agricultural Production Systems sIMulator (APSIM), and that spatially explicit parameterization for the phenology model substantially improves the SCYM performance in capturing the spatiotemporal variation in maize and soybean yield. The scheme presented in our study thus preserves the scalability of SCYM, while significantly reducing its uncertainty.

  7. Evaluating the hydrological consistency of satellite based water cycle components

    KAUST Repository

    Lopez Valencia, Oliver Miguel; Houborg, Rasmus; McCabe, Matthew

    2016-01-01

    observation. Basin-scale studies have shown considerable variability in achieving water budget closure with any degree of accuracy using satellite estimates of the water cycle. In order to assess the suitability of this type of approach for evaluating

  8. Estimation of precipitation rates by measurements of {sup 36}Cl in the GRIP ice core with the PSI/ETH tandem accelerator

    Energy Technology Data Exchange (ETDEWEB)

    Wagner, G.; Baumgartner, S.; Beer, J. [EAWAG, Duebendorf (Switzerland); Synal, H.A. [Paul Scherrer Inst. (PSI), Villigen (Switzerland); Suter, M. [Eidgenoessische Technische Hochschule, Zurich (Switzerland)

    1997-09-01

    Within the European Greenland ice core project (GRIP) {sup 36}Cl AMS measurements have been performed on ice core samples from Summit (Greenland, 73{sup o}N, 37{sup o}W). Most data analysed so far are from the lower part of the ice core. The {sup 36}Cl concentration is well correlated with {delta}{sup 18}O, which is considered as a proxy for paleotemperatures. Assuming that the deposition rate of radionuclides is independent of {delta}{sup 18}O, {sup 36}Cl is used to estimate the relationship between accumulation and {delta}{sup 18}O. The results confirm that the rapid changes of {delta}{sup 18}O, the so-called Dansgaard-Oeschger events, are also reflected in the precipitation rate. (author) 1 fig., 3 refs.

  9. Evaluation of radar-derived precipitation estimates using runoff simulation : report for the NFR Energy Norway funded project 'Utilisation of weather radar data in atmospheric and hydrological models'

    Energy Technology Data Exchange (ETDEWEB)

    Abdella, Yisak; Engeland, Kolbjoern; Lepioufle, Jean-Marie

    2012-11-01

    This report presents the results from the project called 'Utilisation of weather radar data in atmospheric and hydrological models' funded by NFR and Energy Norway. Three precipitation products (radar-derived, interpolated and combination of the two) were generated as input for hydrological models. All the three products were evaluated by comparing the simulated and observed runoff at catchments. In order to expose any bias in the precipitation inputs, no precipitation correction factors were applied. Three criteria were used to measure the performance: Nash, correlation coefficient, and bias. The results shows that the simulations with the combined precipitation input give the best performance. We also see that the radar-derived precipitation estimates give reasonable runoff simulation even without a region specific parameters for the Z-R relationship. All the three products resulted in an underestimation of the estimated runoff, revealing a systematic bias in measurements (e.g. catch deficit, orographic effects, Z-R relationships) that can be improved. There is an important potential of using radar-derived precipitation for simulation of runoff, especially in catchments without precipitation gauges inside.(Author)

  10. NOAA Climate Data Record (CDR) of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Version 1 Revision 1

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — PERSIANN Precipitation Climate Data Record (PERSIANN-CDR) is a daily quasi-global precipitation product for the period of 1982 to 2011. The data covers from 60...

  11. Advances in Understanding the Role of Frozen Precipitation in High Latitude Hydrology

    Science.gov (United States)

    L'Ecuyer, T. S.; Wood, N.; Smalley, M.; McIlhattan, E.; Kulie, M.

    2017-12-01

    Satellite-based millimeter wavelength radar observations provide a unique perspective on the global character of frozen precipitation that has been difficult to detect using conventional spaceborne precipitation sensors. This presentation will describe the methodology underpinning the ten-year CloudSat global snowfall product and discuss the results of a number of complementary approaches that have been adopted to quantify its uncertainties. These datasets are shedding new light on the distribution, character, and impacts of frozen precipitation on high latitude hydrology. Inferred regional snowfall accumulations, for example, provide valuable constraints on projected changes in precipitation and mass balance on the Antarctic ice sheet in climate models. When placed in the broader context of complementary observations from other A-Train sensors, instantaneous snowfall estimates also hint at the large-scale processes that influence snow formation including air-sea interactions associated with cold-air outbreaks, lake-effect snows, and orographic enhancement. Simultaneous CloudSat and CALIPSO observations further emphasize the important role snowfall plays in the lifetime of super-cooled liquid containing clouds in the Arctic and highlight a model deficiency with important implications for surface energy and mass balance on the Greenland ice sheet.

  12. Estimating soil hydrological response by combining precipitation-runoff modeling and hydro-functional soil homogeneous units

    Science.gov (United States)

    Aroca-Jimenez, Estefania; Bodoque, Jose Maria; Diez-Herrero, Andres

    2015-04-01

    Flash floods constitute one of the natural hazards better able to generate risk, particularly with regard to Society. The complexity of this process and its dependence on various factors related to the characteristics of the basin and rainfall make flash floods are difficult to characterize in terms of their hydrological response.To do this, it is essential a proper analysis of the so called 'initial abstractions'. Among all of these processes, infiltration plays a crucial role in explaining the occurrence of floods in mountainous basins.For its characterization the Green-Ampt model , which depends on the characteristics of rainfall and physical properties of soil has been used in this work.This is a method enabling to simulate floods in mountainous basins where hydrological response is sub-daily. However, it has the disadvantage that it is based on physical properties of soil which have a high spatial variability. To address this difficulty soil mapping units have been delineated according to the geomorphological landforms and elements. They represent hydro-functional mapping units that are theoretically homogeneous from the perspective of the pedostructure parameters of the pedon. So the soil texture of each homogeneous group of landform units was studied by granulometric analyses using standarized sieves and Sedigraph devices. In addition, uncertainty associated with the parameterization of the Green-Ampt method has been estimated by implementing a Monte Carlo approach, which required assignment of the proper distribution function to each parameter.The suitability of this method was contrasted by calibrating and validating a hydrological model, in which the generation of runoff hydrograph has been simulated using the SCS unit hydrograph (HEC-GeoHMS software), while flood wave routing has been characterized using the Muskingum-Cunge method. Calibration and validation of the model was from the use of an automatic routine based on the employ of the search algorithm

  13. The ALTA global positioning satellite based timing system

    CERN Document Server

    Brouwer, W; Caron, B; Hewlett, J C; Holm, L; Hamilton, A H; McDonald, W J; Pinfold, J L; Schaapman, J R; Soluk, R A; Wampler, L J

    2002-01-01

    The Alberta Large-area Time-coincidence Array (ALTA) experiment uses a number of scintillation detector systems to form a sparse very large area cosmic air-shower detection array. An important scientific goal of the ALTA collaboration is to search for coincidences in the ALTA array due to large area cosmic ray phenomena. A local cosmic ray event, determined by a coincidence of the triplet of cosmic ray detectors forming a local detector system, is time stamped with a temporal coordinate obtained from a GPS receiver. The readout of the data, the local coincidence and the GPS time stamp are all performed in the local readout crate. This time stamp, along with the local shower direction is used to search for coincidences within the large area array. Using two GPS receivers and duplicate sets of ALTA electronics the timing resolution of the GPS time difference between sites was estimated to be 16 ns.

  14. High-resolution Monthly Satellite Precipitation Product over the Conterminous United States

    Science.gov (United States)

    Hashemi, H.; Fayne, J.; Knight, R. J.; Lakshmi, V.

    2017-12-01

    We present a data set that enhanced the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) monthly product 3B43 in its accuracy and spatial resolution. For this, we developed a correction function to improve the accuracy of TRMM 3B43, spatial resolution of 25 km, by estimating and removing the bias in the satellite data using a ground-based precipitation data set. We observed a strong relationship between the bias and land surface elevation; TRMM 3B43 tends to underestimate the ground-based product at elevations above 1500 m above mean sea level (m.amsl) over the conterminous United States. A relationship was developed between satellite bias and elevation. We then resampled TRMM 3B43 to the Digital Elevation Model (DEM) data set at a spatial resolution of 30 arc second ( 1 km on the ground). The produced high-resolution satellite-based data set was corrected using the developed correction function based on the bias-elevation relationship. Assuming that each rain gauge represents an area of 1 km2, we verified our product against 9,200 rain gauges across the conterminous United States. The new product was compared with the gauges, which have 50, 60, 70, 80, 90, and 100% temporal coverage within the TRMM period of 1998 to 2015. Comparisons between the high-resolution corrected satellite-based data and gauges showed an excellent agreement. The new product captured more detail in the changes in precipitation over the mountainous region than the original TRMM 3B43.

  15. Evaluation of Satellite and Model Precipitation Products Over Turkey

    Science.gov (United States)

    Yilmaz, M. T.; Amjad, M.

    2017-12-01

    Satellite-based remote sensing, gauge stations, and models are the three major platforms to acquire precipitation dataset. Among them satellites and models have the advantage of retrieving spatially and temporally continuous and consistent datasets, while the uncertainty estimates of these retrievals are often required for many hydrological studies to understand the source and the magnitude of the uncertainty in hydrological response parameters. In this study, satellite and model precipitation data products are validated over various temporal scales (daily, 3-daily, 7-daily, 10-daily and monthly) using in-situ measured precipitation observations from a network of 733 gauges from all over the Turkey. Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 version 7 and European Center of Medium-Range Weather Forecast (ECMWF) model estimates (daily, 3-daily, 7-daily and 10-daily accumulated forecast) are used in this study. Retrievals are evaluated for their mean and standard deviation and their accuracies are evaluated via bias, root mean square error, error standard deviation and correlation coefficient statistics. Intensity vs frequency analysis and some contingency table statistics like percent correct, probability of detection, false alarm ratio and critical success index are determined using daily time-series. Both ECMWF forecasts and TRMM observations, on average, overestimate the precipitation compared to gauge estimates; wet biases are 10.26 mm/month and 8.65 mm/month, respectively for ECMWF and TRMM. RMSE values of ECMWF forecasts and TRMM estimates are 39.69 mm/month and 41.55 mm/month, respectively. Monthly correlations between Gauges-ECMWF, Gauges-TRMM and ECMWF-TRMM are 0.76, 0.73 and 0.81, respectively. The model and the satellite error statistics are further compared against the gauges error statistics based on inverse distance weighting (IWD) analysis. Both the model and satellite data have less IWD errors (14

  16. Advances in the Validation of Satellite-Based Maps of Volcanic Sulfur Dioxide Plumes

    Science.gov (United States)

    Realmuto, V. J.; Berk, A.; Acharya, P. K.; Kennett, R.

    2013-12-01

    The monitoring of volcanic gas emissions with gas cameras, spectrometer arrays, tethersondes, and UAVs presents new opportunities for the validation of satellite-based retrievals of gas concentrations. Gas cameras and spectrometer arrays provide instantaneous observations of the gas burden, or concentration along an optical path, over broad sections of a plume, similar to the observations acquired by nadir-viewing satellites. Tethersondes and UAVs provide us with direct measurements of the vertical profiles of gas concentrations within plumes. This presentation will focus on our current efforts to validate ASTER-based maps of sulfur dioxide plumes at Turrialba and Kilauea Volcanoes (located in Costa Rica and Hawaii, respectively). These volcanoes, which are the subjects of comprehensive monitoring programs, are challenging targets for thermal infrared (TIR) remote sensing due the warm and humid atmospheric conditions. The high spatial resolution of ASTER in the TIR (90 meters) allows us to map the plumes back to their source vents, but also requires us to pay close attention to the temperature and emissivity of the surfaces beneath the plumes. Our knowledge of the surface and atmospheric conditions is never perfect, and we employ interactive mapping techniques that allow us to evaluate the impact of these uncertainties on our estimates of plume composition. To accomplish this interactive mapping we have developed the Plume Tracker tool kit, which integrates retrieval procedures, visualization tools, and a customized version of the MODTRAN radiative transfer (RT) model under a single graphics user interface (GUI). We are in the process of porting the RT calculations to graphics processing units (GPUs) with the goal of achieving a 100-fold increase in the speed of computation relative to conventional CPU-based processing. We will report on our progress with this evolution of Plume Tracker. Portions of this research were conducted at the Jet Propulsion Laboratory

  17. Implementing earth observation and advanced satellite based atmospheric sounders for water resource and climate modelling

    DEFF Research Database (Denmark)

    Boegh, E.; Dellwik, Ebba; Hahmann, Andrea N.

    2010-01-01

    This paper discusses preliminary remote sensing (MODIS) based hydrological modelling results for the Danish island Sjælland (7330 km2) in relation to project objectives and methodologies of a new research project “Implementing Earth observation and advanced satellite based atmospheric sounders....... For this purpose, a) internal catchment processes will be studied using a Distributed Temperature Sensing (DTS) system, b) Earth observations will be used to upscale from field to regional scales, and c) at the largest scale, satellite based atmospheric sounders and meso-scale climate modelling will be used...

  18. Improving Fire Emission Estimates in the eastern United States Using Satellite-Based Fuel Loading Factors

    Science.gov (United States)

    Yongqiang Liu; John J. Qu; Xianjun Hao; Wanting Wang

    2005-01-01

    Wildfires can lead to severe environmental consequences by releasing large amounts of particulate matter (PM) and precursors of ozone (Sandberg et al., 1999; Riebau and Fox, 2001). The Southeast has the most burned area among various U.S. regions (Stanturf et al., 2002) and has regionally some of the highest levels of PM and ozone in the nation. Fires have been found...

  19. The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset

    Science.gov (United States)

    Huffman, George J.; Adler, Robert F.; Arkin, Philip; Chang, Alfred; Ferraro, Ralph; Gruber, Arnold; Janowiak, John; McNab, Alan; Rudolf, Bruno; Schneider, Udo

    1997-01-01

    The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit -satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5 deg x 2.5 deg latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino-Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.

  20. Global Crop Monitoring: A Satellite-Based Hierarchical Approach

    Directory of Open Access Journals (Sweden)

    Bingfang Wu

    2015-04-01

    Full Text Available Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, the CropWatch system has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The approach adopts a hierarchical system covering four spatial levels of detail: global, regional, national (thirty-one key countries including China and “sub-countries” (for the nine largest countries. The thirty-one countries encompass more that 80% of both production and exports of maize, rice, soybean and wheat. The methodology resorts to climatic and remote sensing indicators at different scales. The global patterns of crop environmental growing conditions are first analyzed with indicators for rainfall, temperature, photosynthetically active radiation (PAR as well as potential biomass. At the regional scale, the indicators pay more attention to crops and include Vegetation Health Index (VHI, Vegetation Condition Index (VCI, Cropped Arable Land Fraction (CALF as well as Cropping Intensity (CI. Together, they characterize crop situation, farming intensity and stress. CropWatch carries out detailed crop condition analyses at the national scale with a comprehensive array of variables and indicators. The Normalized Difference Vegetation Index (NDVI, cropped areas and crop conditions are integrated to derive food production estimates. For the nine largest countries, CropWatch zooms into the sub-national units to acquire detailed information on crop condition and production by including new indicators (e.g., Crop type proportion. Based on trend analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. The hierarchical approach adopted by CropWatch is the basis of the analyses of climatic and crop conditions assessments published in the quarterly “CropWatch bulletin” which

  1. Katabatic winds diminish precipitation contribution to the Antarctic ice mass balance.

    Science.gov (United States)

    Grazioli, Jacopo; Madeleine, Jean-Baptiste; Gallée, Hubert; Forbes, Richard M; Genthon, Christophe; Krinner, Gerhard; Berne, Alexis

    2017-10-10

    Snowfall in Antarctica is a key term of the ice sheet mass budget that influences the sea level at global scale. Over the continental margins, persistent katabatic winds blow all year long and supply the lower troposphere with unsaturated air. We show that this dry air leads to significant low-level sublimation of snowfall. We found using unprecedented data collected over 1 year on the coast of Adélie Land and simulations from different atmospheric models that low-level sublimation accounts for a 17% reduction of total snowfall over the continent and up to 35% on the margins of East Antarctica, significantly affecting satellite-based estimations close to the ground. Our findings suggest that, as climate warming progresses, this process will be enhanced and will limit expected precipitation increases at the ground level.

  2. Application of Observed Precipitation in NCEP Global and Regional Data Assimilation Systems, Including Reanalysis and Land Data Assimilation

    Science.gov (United States)

    Mitchell, K. E.

    2006-12-01

    The Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) applies several different analyses of observed precipitation in both the data assimilation and validation components of NCEP's global and regional numerical weather and climate prediction/analysis systems (including in NCEP global and regional reanalysis). This invited talk will survey these data assimilation and validation applications and methodologies, as well as the temporal frequency, spatial domains, spatial resolution, data sources, data density and data quality control in the precipitation analyses that are applied. Some of the precipitation analyses applied by EMC are produced by NCEP's Climate Prediction Center (CPC), while others are produced by the River Forecast Centers (RFCs) of the National Weather Service (NWS), or by automated algorithms of the NWS WSR-88D Radar Product Generator (RPG). Depending on the specific type of application in data assimilation or model forecast validation, the temporal resolution of the precipitation analyses may be hourly, daily, or pentad (5-day) and the domain may be global, continental U.S. (CONUS), or Mexico. The data sources for precipitation include ground-based gauge observations, radar-based estimates, and satellite-based estimates. The precipitation analyses over the CONUS are analyses of either hourly, daily or monthly totals of precipitation, and they are of two distinct types: gauge-only or primarily radar-estimated. The gauge-only CONUS analysis of daily precipitation utilizes an orographic-adjustment technique (based on the well-known PRISM precipitation climatology of Oregon State University) developed by the NWS Office of Hydrologic Development (OHD). The primary NCEP global precipitation analysis is the pentad CPC Merged Analysis of Precipitation (CMAP), which blends both gauge observations and satellite estimates. The presentation will include a brief comparison between the CMAP analysis and other global

  3. Precipitation and measurements of precipitation

    NARCIS (Netherlands)

    Schmidt, F.H.; Bruin, H.A.R. de; Attmannspacher, W.; Harrold, T.W.; Kraijenhoff van de Leur, D.A.

    1977-01-01

    In Western Europe, precipitation is normal phenomenon; it is of importance to all aspects of society, particularly to agriculture, in cattle breeding and, of course, it is a subject of hydrological research. Precipitation is an essential part in the hydrological cycle. How disastrous local

  4. Precipitation Analysis at Fine Time Scales using TRMM and Other Satellites: Realtime and Research Products and Applications

    Science.gov (United States)

    Adler, Robert; Huffman, George; Bolvin, David; Nelkin, Eric; Curtis, Scott; Pierce, Harold; Gu, Guojon

    2004-01-01

    Quasi-global precipitation analyses at fine time scales (3-hr) are described. TRMM observations (radar and passive microwave) are used to calibrate polar-orbit microwave observations from SSM/I (and other satellites instruments, including AMSR and AMSU) and geosynchronous IR observations. The individual data sets are then merged using a priority order based on quality to form the TRMM Multi-satellite Precipitation Analysis (MPA). Raingauge information is used to help constrain the satellite-based estimates over land. The TRMM standard research product (Version 6 3B-42 of the TRMM products) will be available for the entire TRMM period (January 1998-present) by the end of 2004. The real-time version of this merged product has been produced over the past two years and is available on the U.S. TRMM web site (trmm.gsfc.nasa.gov) at 0.25 deg latitude-longitude resolution over the latitude range from 50 deg N-50 deg S. Validation of daily totals indicates good results, with limitations noted in mid-latitude winter over land and regions of shallow, orographic precipitation. Various applications of these estimates are described, including: 1) detecting potential floods in near real-time; 2) analyzing Indian Ocean precipitation variations related to the initiation of El Nino; 3) determining characteristics of the African monsoon; and 4) analysis of diurnal variations.

  5. Precipitation Analysis at Fine Time Scales Using Multiple Satellites: Real-time and Research Products and Applications

    Science.gov (United States)

    Adler, Robert; Huffman, George; Bolvin, David; Nelkin, Eric; Curtis, Scott; Pierce, Harold

    2004-01-01

    Quasi-global precipitation analyses at fine time scales (3-hr) are described. TRMM observations (radar and passive microwave) are used to calibrate polar-orbit microwave observations from SSM/I (and other satellites instruments, including AMSR and AMSU) and geosynchronous IR observations. The individual data sets are then merged using a priority order based on quality to form the Multi-satellite Precipitation Analysis (MPA). Raingauge information is used to help constrain the satellite-based estimates over land. The TRMM standard research product (Version 6 3B-42 of the TRMM products) will be available for the entire TRMM period (January 1998-present) in 2004. The real-time version of this merged product has been produced over the past two years and is available on the U.S. TRMM web site (trmm.gsfc.nasa.gov) at 0.25" latitude-longitude resolution over the latitude range from 5O"N-5O0S. Validation of daily totals indicates good results, with limitations noted in mid-latitude winter over land and regions of shallow, orographic precipitation. Various applications of these estimates are described, including: 1) detecting potential floods in near real-time; 2) analyzing Indian Ocean precipitation variations related to the initiation of El Nino; 3) determining characteristics of the African monsoon; and 4) analysis of diurnal variations.

  6. Precipitation Analysis at Fine Time Scales using TRMM and Other Satellites: Real-time and Research Products and Applications

    Science.gov (United States)

    Adler, Robert; Huffman, George; Bolvin, David; Nelkin, Eric; Curtis, Scott; Pierce, Harold; Gu, Guo-Jon

    2004-01-01

    Quasi-global precipitation analyses at fine time scales (3-hr) are described. TRMM observations (radar and passive microwave) are used to calibrate polar-orbit microwave observations from SSM/I (and other satellites instruments, including AMSR and AMSU) and geosynchronous IR observations. The individual data sets are then merged using a priority order based on quality to form the TRMM Multi-satellite Precipitation Analysis (MPA). Raingauge information is used to help constrain the satellite-based estimates over land. The TRMM standard research product (Version 6 3B-42 of the TRMM products) will be available for the entire TRMM period (January 1998-present) by the end of 2004. The real-time version of this merged product has been produced over the past two years and is available on the U.S. TRMM web site (trmm.gsfc.nasa.gov) at 0.25" latitude-longitude resolution over the latitude range from 5O0N-50"S. Validation of daily totals indicates good results, with limitations noted in mid-latitude winter over land and regions of shallow, orographic precipitation. Various applications of these estimates are described, includmg: 1) detecting potential floods in near real-time; 2) analyzing Indian Ocean precipitation variations related to the initiation of El Nino; 3) determining characteristics of the African monsoon; and 4) analysis of diurnal variations.

  7. Satellite-based empirical models linking river plume dynamics with hypoxic area andvolume

    Science.gov (United States)

    Satellite-based empirical models explaining hypoxic area and volume variation were developed for the seasonally hypoxic (O2 < 2 mg L−1) northern Gulf of Mexico adjacent to the Mississippi River. Annual variations in midsummer hypoxic area and ...

  8. Precipitous Birth

    Directory of Open Access Journals (Sweden)

    Jennifer Yee

    2017-09-01

    Full Text Available Audience: This scenario was developed to educate emergency medicine residents on the management of a precipitous birth in the emergency department (ED. The case is also appropriate for teaching of medical students and advanced practice providers, as well as reviewing the principles of crisis resource management, teamwork, and communication. Introduction: Patients with precipitous birth require providers to manage two patients simultaneously with limited time and resources. Crisis resource management skills will be tested once baby is delivered, and the neonate will require assessment for potential neonatal resuscitation. Objectives: At the conclusion of the simulation session, learners will be able to manage women who have precipitous deliveries, as well as perform neonatal assessment and management. Method: This session was conducted using high-fidelity simulation, followed by a debriefing session and lecture on precipitous birth management and neonatal evaluation.

  9. TCA precipitation.

    Science.gov (United States)

    Koontz, Laura

    2014-01-01

    Trichloroacetic acid (TCA) precipitation of proteins is commonly used to concentrate protein samples or remove contaminants, including salts and detergents, prior to downstream applications such as SDS-PAGE or 2D-gels. TCA precipitation denatures the protein, so it should not be used if the protein must remain in its folded state (e.g., if you want to measure a biochemical activity of the protein). © 2014 Elsevier Inc. All rights reserved.

  10. STRONTIUM PRECIPITATION

    Science.gov (United States)

    McKenzie, T.R.

    1960-09-13

    A process is given for improving the precipitation of strontium from an aqueous phosphoric-acid-containing solution with nickel or cobalt ferrocyanide by simultaneously precipitating strontium or calcium phosphate. This is accomplished by adding to the ferrocyanide-containing solution calcium or strontium nitrate in a quantity to yield a concentration of from 0.004 to 0.03 and adjusting the pH of the solution to a value of above 8.

  11. Satellite-Enhanced Regional Downscaling for Applied Studies: Extreme Precipitation Events in Southeastern South America

    Science.gov (United States)

    Nunes, A.; Gomes, G.; Ivanov, V. Y.

    2016-12-01

    Frequently found in southeastern South America during the warm season from October through May, strong and localized precipitation maxima are usually associated with the presence of mesoscale convective complexes (MCCs) travelling across the region. Flashfloods and landslides can be caused by these extremes in precipitation, with damages to the local communities. Heavily populated, southeastern South America hosts many agricultural activities and hydroelectric production. It encompasses one of the most important river basins in South America, the La Plata River Basin. Therefore, insufficient precipitation is equally prejudicial to the region socio-economic activities. MCCs are originated in the warm season of many regions of the world, however South American MCCs are related to the most severe thunderstorms, and have significantly contributed to the precipitation regime. We used the hourly outputs of Satellite-enhanced Regional Downscaling for Applied Studies (SRDAS), developed at the Federal University of Rio de Janeiro in Brazil, in the analysis of the dynamics and physical characteristics of MCCs in South America. SRDAS is the 25-km resolution downscaling of a global reanalysis available from January 1998 through December 2010. The Regional Spectral Model is the SRDAS atmospheric component and assimilates satellite-based precipitation estimates from the NOAA/Climate Prediction Center MORPHing technique global precipitation analyses. In this study, the SRDAS atmospheric and land-surface variables, global reanalysis products, infrared satellite imagery, and the physical retrievals from the Atmospheric Infrared Sounder (AIRS), on board of the NASA's Aqua satellite, were used in the evaluation of the MCCs developed in southeastern South America from 2008 and 2010. Low-level circulations and vertical profiles were analyzed together to establish the relevance of the moisture transport in connection with the upper-troposphere dynamics to the development of those MCCs.

  12. Passive Microwave Precipitation Retrieval Uncertainty Characterized based on Field Campaign Data over Complex Terrain

    Science.gov (United States)

    Derin, Y.; Anagnostou, E. N.; Anagnostou, M.; Kalogiros, J. A.; Casella, D.; Marra, A. C.; Panegrossi, G.; Sanò, P.

    2017-12-01

    Difficulties in representation of high rainfall variability over mountainous areas using ground based sensors make satellite remote sensing techniques attractive for hydrologic studies over these regions. Even though satellite-based rainfall measurements are quasi global and available at high spatial resolution, these products have uncertainties that necessitate use of error characterization and correction procedures based upon more accurate in situ rainfall measurements. Such measurements can be obtained from field campaigns facilitated by research quality sensors such as locally deployed weather radar and in situ weather stations. This study uses such high quality and resolution rainfall estimates derived from dual-polarization X-band radar (XPOL) observations from three field experiments in Mid-Atlantic US East Coast (NASA IPHEX experiment), the Olympic Peninsula of Washington State (NASA OLYMPEX experiment), and the Mediterranean to characterize the error characteristics of multiple passive microwave (PMW) sensor retrievals. The study first conducts an independent error analysis of the XPOL radar reference rainfall fields against in situ rain gauges and disdrometer observations available by the field experiments. Then the study evaluates different PMW precipitation products using the XPOL datasets (GR) over the three aforementioned complex terrain study areas. We extracted matchups of PMW/GR rainfall based on a matching methodology that identifies GR volume scans coincident with PMW field-of-view sampling volumes, and scaled GR parameters to the satellite products' nominal spatial resolution. The following PMW precipitation retrieval algorithms are evaluated: the NASA Goddard PROFiling algorithm (GPROF), standard and climatology-based products (V 3, 4 and 5) from four PMW sensors (SSMIS, MHS, GMI, and AMSR2), and the precipitation products based on the algorithms Cloud Dynamics and Radiation Database (CDRD) for SSMIS and Passive microwave Neural network

  13. Satellite Based Assessment of Hydroclimatic Conditions Related to Cholera in Zimbabwe.

    Directory of Open Access Journals (Sweden)

    Antarpreet Jutla

    Full Text Available Cholera, an infectious diarrheal disease, has been shown to be associated with large scale hydroclimatic processes. The sudden and sporadic occurrence of epidemic cholera is linked with high mortality rates, in part, due to uncertainty in timing and location of outbreaks. Improved understanding of the relationship between pathogenic abundance and climatic processes allows prediction of disease outbreak to be an achievable goal. In this study, we show association of large scale hydroclimatic processes with the cholera epidemic in Zimbabwe reported to have begun in Chitungwiza, a city in Mashonaland East province, in August, 2008.Climatic factors in the region were found to be associated with triggering cholera outbreak and are shown to be related to anomalies of temperature and precipitation, validating the hypothesis that poor conditions of sanitation, coupled with elevated temperatures, and followed by heavy rainfall can initiate outbreaks of cholera. Spatial estimation by satellite of precipitation and global gridded air temperature captured sensitivities in hydroclimatic conditions that permitted identification of the location in the region where the disease outbreak began.Satellite derived hydroclimatic processes can be used to capture environmental conditions related to epidemic cholera, as occurred in Zimbabwe, thereby providing an early warning system. Since cholera cannot be eradicated because the causative agent, Vibrio cholerae, is autochthonous to the aquatic environment, prediction of conditions favorable for its growth and estimation of risks of triggering the disease in a given population can be used to alert responders, potentially decreasing infection and saving lives.

  14. Precipitation from Space: Advancing Earth System Science

    Science.gov (United States)

    Kucera, Paul A.; Ebert, Elizabeth E.; Turk, F. Joseph; Levizzani, Vicenzo; Kirschbaum, Dalia; Tapiador, Francisco J.; Loew, Alexander; Borsche, M.

    2012-01-01

    Of the three primary sources of spatially contiguous precipitation observations (surface networks, ground-based radar, and satellite-based radar/radiometers), only the last is a viable source over ocean and much of the Earth's land. As recently as 15 years ago, users needing quantitative detail of precipitation on anything under a monthly time scale relied upon products derived from geostationary satellite thermal infrared (IR) indices. The Special Sensor Microwave Imager (SSMI) passive microwave (PMW) imagers originated in 1987 and continue today with the SSMI sounder (SSMIS) sensor. The fortunate longevity of the joint National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Tropical Rainfall Measuring Mission (TRMM) is providing the environmental science community a nearly unbroken data record (as of April 2012, over 14 years) of tropical and sub-tropical precipitation processes. TRMM was originally conceived in the mid-1980s as a climate mission with relatively modest goals, including monthly averaged precipitation. TRMM data were quickly exploited for model data assimilation and, beginning in 1999 with the availability of near real time data, for tropical cyclone warnings. To overcome the intermittently spaced revisit from these and other low Earth-orbiting satellites, many methods to merge PMW-based precipitation data and geostationary satellite observations have been developed, such as the TRMM Multisatellite Precipitation Product and the Climate Prediction Center (CPC) morphing method (CMORPH. The purpose of this article is not to provide a survey or assessment of these and other satellite-based precipitation datasets, which are well summarized in several recent articles. Rather, the intent is to demonstrate how the availability and continuity of satellite-based precipitation data records is transforming the ways that scientific and societal issues related to precipitation are addressed, in ways that would not be

  15. Regionalization Study of Satellite based Hydrological Model (SHM) in Hydrologically Homogeneous River Basins of India

    Science.gov (United States)

    Kumari, Babita; Paul, Pranesh Kumar; Singh, Rajendra; Mishra, Ashok; Gupta, Praveen Kumar; Singh, Raghvendra P.

    2017-04-01

    A new semi-distributed conceptual hydrological model, namely Satellite based Hydrological Model (SHM), has been developed under 'PRACRITI-2' program of Space Application Centre (SAC), Ahmedabad for sustainable water resources management of India by using data from Indian Remote Sensing satellites. Entire India is divided into 5km x 5km grid cells and properties at the center of the cells are assumed to represent the property of the cells. SHM contains five modules namely surface water, forest, snow, groundwater and routing. Two empirical equations (SCS-CN and Hargreaves) and water balance method have been used in the surface water module; the forest module is based on the calculations of water balancing & dynamics of subsurface. 2-D Boussinesq equation is used for groundwater modelling which is solved using implicit finite-difference. The routing module follows a distributed routing approach which requires flow path and network with the key point of travel time estimation. The aim of this study is to evaluate the performance of SHM using regionalization technique which also checks the usefulness of a model in data scarce condition or for ungauged basins. However, homogeneity analysis is pre-requisite to regionalization. Similarity index (Φ) and hierarchical agglomerative cluster analysis are adopted to test the homogeneity in terms of physical attributes of three basins namely Brahmani (39,033 km km^2)), Baitarani (10,982 km km^2)) and Kangsabati (9,660 km km^2)) with respect to Subarnarekha (29,196 km km^2)) basin. The results of both homogeneity analysis show that Brahmani basin is the most homogeneous with respect to Subarnarekha river basin in terms of physical characteristics (land use land cover classes, soiltype and elevation). The calibration and validation of model parameters of Brahmani basin is in progress which are to be transferred into the SHM set up of Subarnarekha basin and results are to be compared with the results of calibrated and validated

  16. Radar-based quantitative precipitation estimation for the identification of debris flow occurrence over earthquake-affected regions in Sichuan, China

    Science.gov (United States)

    Shi, Zhao; Wei, Fangqiang; Chandrasekar, Venkatachalam

    2018-03-01

    Both Ms 8.0 Wenchuan earthquake on 12 May 2008 and Ms 7.0 Lushan earthquake on 20 April 2013 occurred in the province of Sichuan, China. In the earthquake-affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall intensity-duration (I-D) threshold of the debris flow in the earthquake-affected area, and to fill up the observational gaps caused by the relatively scarce and low-altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler Weather Radar (CINRAD) were captured for six rainfall events that triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous areas, a series of improvement measures are considered: a hybrid scan mode, a vertical reflectivity profile (VPR) correction, a mosaic of reflectivity, a merged rainfall-reflectivity (R - Z) relationship for convective and stratiform rainfall, and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I-D threshold derived by the frequentist method is I = 10.1D-0.52 and is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I-D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I-D thresholds and likewise underestimate I-D thresholds due to undershooting at the core of convective

  17. Avaliação de estimativas de campos de precipitação para modelagem hidrológica distribuída Assessment of estimated precipitation fields for distributed hydrologic modeling

    Directory of Open Access Journals (Sweden)

    Adriano Rolim da Paz

    2011-03-01

    Full Text Available É crescente a disponibilidade e utilização de campos de chuva estimados por sensoriamento remoto ou calculados por modelos de circulação da atmosfera, os quais são freqüentemente utilizados como entrada para modelos hidrológicos distribuídos. A distribuição espacial dos campos de chuva estimados é altamente relevante e deve ser avaliada frente aos campos de chuva observados. Este artigo propõe um método de comparação espaço-temporal entre campos de chuva observados e estimados baseado na comparação pixel a pixel e na construção de tabelas de contingência. Duas abordagens são utilizadas: (i a análise integrada no espaço gera índices de performance que retratam a qualidade do campo de chuva estimada em reproduzir a ocorrência de chuva observada ao longo do tempo; (ii a análise integrada no tempo produz mapas dos índices de performance que resumem a destreza das estimativas de ocorrência de chuva em cada pixel. Como exemplo de aplicação, é analisada a chuva estimada na climatologia do modelo global de circulação da atmosfera CPTEC/COLA sobre a bacia do Rio Grande. Utilizando-se cinco índices de performance, o método proposto permitiu identificar variações sazonais e padrões espaciais na performance das estimativas de chuva em relação a campos de chuva derivados de observações em pluviômetros.There is an increasing availability and application of precipitation fields estimated by remote sensing or calculated by atmospheric circulation models, which are frequently used as input for distributed hydrological models. The spatial distribution of the estimated precipitation fields is extremely important and must be verified against observed precipitation fields. This paper proposes a method for spatiotemporal comparison between observed and estimated precipitation fields based on a pixel by pixel comparison and on contingency tables. Two distinct approaches are carried out: (i the spatial integrated analysis

  18. Precipitation Matters

    Science.gov (United States)

    McDuffie, Thomas

    2007-01-01

    Although weather, including its role in the water cycle, is included in most elementary science programs, any further examination of raindrops and snowflakes is rare. Together rain and snow make up most of the precipitation that replenishes Earth's life-sustaining fresh water supply. When viewed individually, raindrops and snowflakes are quite…

  19. Bio-precipitation of uranium by two bacterial isolates recovered from extreme environments as estimated by potentiometric titration, TEM and X-ray absorption spectroscopic analyses.

    Science.gov (United States)

    Merroun, Mohamed L; Nedelkova, Marta; Ojeda, Jesus J; Reitz, Thomas; Fernández, Margarita López; Arias, José M; Romero-González, María; Selenska-Pobell, Sonja

    2011-12-15

    This work describes the mechanisms of uranium biomineralization at acidic conditions by Bacillus sphaericus JG-7B and Sphingomonas sp. S15-S1 both recovered from extreme environments. The U-bacterial interaction experiments were performed at low pH values (2.0-4.5) where the uranium aqueous speciation is dominated by highly mobile uranyl ions. X-ray absorption spectroscopy (XAS) showed that the cells of the studied strains precipitated uranium at pH 3.0 and 4.5 as a uranium phosphate mineral phase belonging to the meta-autunite group. Transmission electron microscopic (TEM) analyses showed strain-specific localization of the uranium precipitates. In the case of B. sphaericus JG-7B, the U(VI) precipitate was bound to the cell wall. Whereas for Sphingomonas sp. S15-S1, the U(VI) precipitates were observed both on the cell surface and intracellularly. The observed U(VI) biomineralization was associated with the activity of indigenous acid phosphatase detected at these pH values in the absence of an organic phosphate substrate. The biomineralization of uranium was not observed at pH 2.0, and U(VI) formed complexes with organophosphate ligands from the cells. This study increases the number of bacterial strains that have been demonstrated to precipitate uranium phosphates at acidic conditions via the activity of acid phosphatase. Copyright © 2011 Elsevier B.V. All rights reserved.

  20. Advances In Global Aerosol Modeling Applications Through Assimilation of Satellite-Based Lidar Measurements

    Science.gov (United States)

    Campbell, James; Hyer, Edward; Zhang, Jianglong; Reid, Jeffrey; Westphal, Douglas; Xian, Peng; Vaughan, Mark

    2010-05-01

    Modeling the instantaneous three-dimensional aerosol field and its downwind transport represents an endeavor with many practical benefits foreseeable to air quality, aviation, military and science agencies. The recent proliferation of multi-spectral active and passive satellite-based instruments measuring aerosol physical properties has served as an opportunity to develop and refine the techniques necessary to make such numerical modeling applications possible. Spurred by high-resolution global mapping of aerosol source regions, and combined with novel multivariate data assimilation techniques designed to consider these new data streams, operational forecasts of visibility and aerosol optical depths are now available in near real-time1. Active satellite-based aerosol profiling, accomplished using lidar instruments, represents a critical element for accurate analysis and transport modeling. Aerosol source functions, alone, can be limited in representing the macrophysical structure of injection scenarios within a model. Two-dimensional variational (2D-VAR; x, y) assimilation of aerosol optical depth from passive satellite observations significantly improves the analysis of the initial state. However, this procedure can not fully compensate for any potential vertical redistribution of mass required at the innovation step. The expense of an inaccurate vertical analysis of aerosol structure is corresponding errors downwind, since trajectory paths within successive forecast runs will likely diverge with height. In this paper, the application of a newly-designed system for 3D-VAR (x,y,z) assimilation of vertical aerosol extinction profiles derived from elastic-scattering lidar measurements is described [Campbell et al., 2009]. Performance is evaluated for use with the U. S. Navy Aerosol Analysis and Prediction System (NAAPS) by assimilating NASA/CNES satellite-borne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 0.532 μm measurements [Winker et al., 2009

  1. Bio-precipitation of uranium by two bacterial isolates recovered from extreme environments as estimated by potentiometric titration, TEM and X-ray absorption spectroscopic analyses

    Energy Technology Data Exchange (ETDEWEB)

    Merroun, Mohamed L., E-mail: merroun@ugr.es [Institute of Radiochemistry, Helmholtz Centre Dresden-Rossendorf, Dresden (Germany); Departamento de Microbiologia, Universidad de Granada, Campus Fuentenueva s/n 18071, Granada (Spain); Nedelkova, Marta [Institute of Radiochemistry, Helmholtz Centre Dresden-Rossendorf, Dresden (Germany); Ojeda, Jesus J. [Cell-Mineral Interface Research Programme, Kroto Research Institute, University of Sheffield, Broad Lane, Sheffield S3 7HQ (United Kingdom); Experimental Techniques Centre, Brunel University, Uxbridge, Middlesex UB8 3PH (United Kingdom); Reitz, Thomas [Institute of Radiochemistry, Helmholtz Centre Dresden-Rossendorf, Dresden (Germany); Fernandez, Margarita Lopez; Arias, Jose M. [Departamento de Microbiologia, Universidad de Granada, Campus Fuentenueva s/n 18071, Granada (Spain); Romero-Gonzalez, Maria [Cell-Mineral Interface Research Programme, Kroto Research Institute, University of Sheffield, Broad Lane, Sheffield S3 7HQ (United Kingdom); Selenska-Pobell, Sonja [Institute of Radiochemistry, Helmholtz Centre Dresden-Rossendorf, Dresden (Germany)

    2011-12-15

    Highlights: Black-Right-Pointing-Pointer Precipitation of uranium as U phosphates by natural bacterial isolates. Black-Right-Pointing-Pointer The uranium biomineralization involves the activity of acidic phosphatase. Black-Right-Pointing-Pointer Uranium bioremediation could be achieved via the biomineralization of U(VI) in phosphate minerals. - Abstract: This work describes the mechanisms of uranium biomineralization at acidic conditions by Bacillus sphaericus JG-7B and Sphingomonas sp. S15-S1 both recovered from extreme environments. The U-bacterial interaction experiments were performed at low pH values (2.0-4.5) where the uranium aqueous speciation is dominated by highly mobile uranyl ions. X-ray absorption spectroscopy (XAS) showed that the cells of the studied strains precipitated uranium at pH 3.0 and 4.5 as a uranium phosphate mineral phase belonging to the meta-autunite group. Transmission electron microscopic (TEM) analyses showed strain-specific localization of the uranium precipitates. In the case of B. sphaericus JG-7B, the U(VI) precipitate was bound to the cell wall. Whereas for Sphingomonas sp. S15-S1, the U(VI) precipitates were observed both on the cell surface and intracellularly. The observed U(VI) biomineralization was associated with the activity of indigenous acid phosphatase detected at these pH values in the absence of an organic phosphate substrate. The biomineralization of uranium was not observed at pH 2.0, and U(VI) formed complexes with organophosphate ligands from the cells. This study increases the number of bacterial strains that have been demonstrated to precipitate uranium phosphates at acidic conditions via the activity of acid phosphatase.

  2. Comparison of Different Machine Learning Approaches for Monthly Satellite-Based Soil Moisture Downscaling over Northeast China

    Directory of Open Access Journals (Sweden)

    Yangxiaoyue Liu

    2017-12-01

    Full Text Available Although numerous satellite-based soil moisture (SM products can provide spatiotemporally continuous worldwide datasets, they can hardly be employed in characterizing fine-grained regional land surface processes, owing to their coarse spatial resolution. In this study, we proposed a machine-learning-based method to enhance SM spatial accuracy and improve the availability of SM data. Four machine learning algorithms, including classification and regression trees (CART, K-nearest neighbors (KNN, Bayesian (BAYE, and random forests (RF, were implemented to downscale the monthly European Space Agency Climate Change Initiative (ESA CCI SM product from 25-km to 1-km spatial resolution. During the regression, the land surface temperature (including daytime temperature, nighttime temperature, and diurnal fluctuation temperature, normalized difference vegetation index, surface reflections (red band, blue band, NIR band and MIR band, and digital elevation model were taken as explanatory variables to produce fine spatial resolution SM. We chose Northeast China as the study area and acquired corresponding SM data from 2003 to 2012 in unfrozen seasons. The reconstructed SM datasets were validated against in-situ measurements. The results showed that the RF-downscaled results had superior matching performance to both ESA CCI SM and in-situ measurements, and can positively respond to precipitation variation. Additionally, the RF was less affected by parameters, which revealed its robustness. Both CART and KNN ranked second. Compared to KNN, CART had a relatively close correlation with the validation data, but KNN showed preferable precision. Moreover, BAYE ranked last with significantly abnormal regression values.

  3. Concept for a Satellite-Based Advanced Air Traffic Management System : Volume 4. Operational Description and Qualitative Assessment.

    Science.gov (United States)

    1974-02-01

    The volume presents a description of how the Satellite-Based Advanced Air Traffic Management System (SAATMS) operates and a qualitative assessment of the system. The operational description includes the services, functions, and tasks performed by the...

  4. Using satellite-based measurements to explore spatiotemporal scales and variability of drivers of new particle formation

    Science.gov (United States)

    New particle formation (NPF) can potentially alter regional climate by increasing aerosol particle (hereafter particle) number concentrations and ultimately cloud condensation nuclei. The large scales on which NPF is manifest indicate potential to use satellite-based (inherently ...

  5. The Climate Hazards group InfraRed Precipitation (CHIRP) with Stations (CHIRPS): Development and Validation

    Science.gov (United States)

    Peterson, P.; Funk, C. C.; Husak, G. J.; Pedreros, D. H.; Landsfeld, M.; Verdin, J. P.; Shukla, S.

    2013-12-01

    CHIRP and CHIRPS are new quasi-global precipitation products with daily to seasonal time scales, a 0.05° resolution, and a 1981 to near real-time period of record. Developed by the Climate Hazards Group at UCSB and scientists at the U.S. Geological Survey Earth Resources Observation and Science Center specifically for drought early warning and environmental monitoring, CHIRPS provides moderate latency precipitation estimates that place observed hydrologic extremes in their historic context. Three main types of information are used in the CHIRPS: (1) global 0.05° precipitation climatologies, (2) time-varying grids of satellite-based precipitation estimates, and (3) in situ precipitation observations. CHIRP: The global grids of long-term (1980-2009) average precipitation were estimated for each month based on station data, averaged satellite observations, and physiographic parameters. 1981-present time-varying grids of satellite precipitation were derived from spatially varying regression models based on pentadal cold cloud duration (CCD) values and TRMM V7 training data. The CCD time-series were derived from the CPC and NOAA B1 datasets. Pentadal CCD-percent anomaly values were multiplied by pentadal climatology fields to produce low bias pentadal precipitation estimates. CHIRPS: The CHG station blending procedure uses the satellite-observed spatial covariance structure to assign relative weights to neighboring stations and the CHIRP values. The CHIRPS blending procedure is based on the expected correlation between precipitation at a given target location and precipitation at the locations of the neighboring observation stations. These correlations are estimated using the CHIRP fields. The CHG has developed an extensive archive of in situ daily, pentadal and monthly precipitation totals. The CHG database has over half a billion daily rainfall observations since 1980 and another half billion before 1980. Most of these observations come from four sets of global

  6. Relation between Ocean SST Dipoles and Downwind Continental Croplands Assessed for Early Management Using Satellite-based Photosynthesis Models

    Science.gov (United States)

    Kaneko, Daijiro

    2015-04-01

    Crop-monitoring systems with the unit of carbon-dioxide sequestration for environmental issues related to climate adaptation to global warming have been improved using satellite-based photosynthesis and meteorological conditions. Early management of crop status is desirable for grain production, stockbreeding, and bio-energy providing that the seasonal climate forecasting is sufficiently accurate. Incorrect seasonal forecasting of crop production can damage global social activities if the recognized conditions are unsatisfied. One cause of poor forecasting related to the atmospheric dynamics at the Earth surface, which reflect the energy budget through land surface, especially the oceans and atmosphere. Recognition of the relation between SST anomalies (e.g. ENSO, Atlantic Niño, Indian dipoles, and Ningaloo Niño) and crop production, as expressed precisely by photosynthesis or the sequestrated-carbon rate, is necessary to elucidate the mechanisms related to poor production. Solar radiation, surface air temperature, and water stress all directly affect grain vegetation photosynthesis. All affect stomata opening, which is related to the water balance or definition by the ratio of the Penman potential evaporation and actual transpiration. Regarding stomata, present data and reanalysis data give overestimated values of stomata opening because they are extended from wet models in forests rather than semi-arid regions commonly associated with wheat, maize, and soybean. This study applies a complementary model based on energy conservation for semi-arid zones instead of the conventional Penman-Monteith method. Partitioning of the integrated Net PSN enables precise estimation of crop yields by modifying the semi-closed stomata opening. Partitioning predicts production more accurately using the cropland distribution already classified using satellite data. Seasonal crop forecasting should include near-real-time monitoring using satellite-based process crop models to avoid

  7. Satellite-based evidence of wavelength-dependent aerosol absorption in biomass burning smoke inferred from Ozone Monitoring Instrument

    Directory of Open Access Journals (Sweden)

    H. Jethva

    2011-10-01

    Full Text Available We provide satellite-based evidence of the spectral dependence of absorption in biomass burning aerosols over South America using near-UV measurements made by the Ozone Monitoring Instrument (OMI during 2005–2007. In the current near-UV OMI aerosol algorithm (OMAERUV, it is implicitly assumed that the only absorbing component in carbonaceous aerosols is black carbon whose imaginary component of the refractive index is wavelength independent. With this assumption, OMI-derived aerosol optical depth (AOD is found to be significantly over-estimated compared to that of AERONET at several sites during intense biomass burning events (August-September. Other well-known sources of error affecting the near-UV method of aerosol retrieval do not explain the large observed AOD discrepancies between the satellite and the ground-based observations. A number of studies have revealed strong spectral dependence in carbonaceous aerosol absorption in the near-UV region suggesting the presence of organic carbon in biomass burning generated aerosols. A sensitivity analysis examining the importance of accounting for the presence of wavelength-dependent aerosol absorption in carbonaceous particles in satellite-based remote sensing was carried out in this work. The results convincingly show that the inclusion of spectrally-dependent aerosol absorption in the radiative transfer calculations leads to a more accurate characterization of the atmospheric load of carbonaceous aerosols. The use of a new set of aerosol models assuming wavelength-dependent aerosol absorption in the near-UV region (Absorption Angstrom Exponent λ−2.5 to −3.0 improved the OMAERUV retrieval results by significantly reducing the AOD bias observed when gray aerosols were assumed. In addition, the new retrieval of single-scattering albedo is in better agreement with those of AERONET within the uncertainties (ΔSSA = ±0.03. The new colored carbonaceous aerosol model was also found to

  8. Long-term analysis of aerosol optical depth over Northeast Asia using a satellite-based measurement: MI Yonsei Aerosol Retrieval Algorithm (YAER)

    Science.gov (United States)

    Kim, Mijin; Kim, Jhoon; Yoon, Jongmin; Chung, Chu-Yong; Chung, Sung-Rae

    2017-04-01

    In 2010, the Korean geostationary earth orbit (GEO) satellite, the Communication, Ocean, and Meteorological Satellite (COMS), was launched including the Meteorological Imager (MI). The MI measures atmospheric condition over Northeast Asia (NEA) using a single visible channel centered at 0.675 μm and four IR channels at 3.75, 6.75, 10.8, 12.0 μm. The visible measurement can also be utilized for the retrieval of aerosol optical properties (AOPs). Since the GEO satellite measurement has an advantage for continuous monitoring of AOPs, we can analyze the spatiotemporal variation of the aerosol using the MI observations over NEA. Therefore, we developed an algorithm to retrieve aerosol optical depth (AOD) using the visible observation of MI, and named as MI Yonsei Aerosol Retrieval Algorithm (YAER). In this study, we investigated the accuracy of MI YAER AOD by comparing the values with the long-term products of AERONET sun-photometer. The result showed that the MI AODs were significantly overestimated than the AERONET values over bright surface in low AOD case. Because the MI visible channel centered at red color range, contribution of aerosol signal to the measured reflectance is relatively lower than the surface contribution. Therefore, the AOD error in low AOD case over bright surface can be a fundamental limitation of the algorithm. Meanwhile, an assumption of background aerosol optical depth (BAOD) could result in the retrieval uncertainty, also. To estimate the surface reflectance by considering polluted air condition over the NEA, we estimated the BAOD from the MODIS dark target (DT) aerosol products by pixel. The satellite-based AOD retrieval, however, largely depends on the accuracy of the surface reflectance estimation especially in low AOD case, and thus, the BAOD could include the uncertainty in surface reflectance estimation of the satellite-based retrieval. Therefore, we re-estimated the BAOD using the ground-based sun-photometer measurement, and

  9. Contribution of long-term accounting for raindrop size distribution variations on quantitative precipitation estimation by weather radar: Disdrometers vs parameter optimization

    Science.gov (United States)

    Hazenberg, P.; Uijlenhoet, R.; Leijnse, H.

    2015-12-01

    Volumetric weather radars provide information on the characteristics of precipitation at high spatial and temporal resolution. Unfortunately, rainfall measurements by radar are affected by multiple error sources, which can be subdivided into two main groups: 1) errors affecting the volumetric reflectivity measurements (e.g. ground clutter, vertical profile of reflectivity, attenuation, etc.), and 2) errors related to the conversion of the observed reflectivity (Z) values into rainfall intensity (R) and specific attenuation (k). Until the recent wide-scale implementation of dual-polarimetric radar, this second group of errors received relatively little attention, focusing predominantly on precipitation type-dependent Z-R and Z-k relations. The current work accounts for the impact of variations of the drop size distribution (DSD) on the radar QPE performance. We propose to link the parameters of the Z-R and Z-k relations directly to those of the normalized gamma DSD. The benefit of this procedure is that it reduces the number of unknown parameters. In this work, the DSD parameters are obtained using 1) surface observations from a Parsivel and Thies LPM disdrometer, and 2) a Monte Carlo optimization procedure using surface rain gauge observations. The impact of both approaches for a given precipitation type is assessed for 45 days of summertime precipitation observed within The Netherlands. Accounting for DSD variations using disdrometer observations leads to an improved radar QPE product as compared to applying climatological Z-R and Z-k relations. However, overall precipitation intensities are still underestimated. This underestimation is expected to result from unaccounted errors (e.g. transmitter calibration, erroneous identification of precipitation as clutter, overshooting and small-scale variability). In case the DSD parameters are optimized, the performance of the radar is further improved, resulting in the best performance of the radar QPE product. However

  10. Comparison of region-of-influence methods for estimating high quantiles of precipitation in a dense dataset in the Czech Republic

    Czech Academy of Sciences Publication Activity Database

    Gaál, Ladislav; Kyselý, Jan

    2009-01-01

    Roč. 13, č. 11 (2009), s. 2203-2219 ISSN 1027-5606 R&D Projects: GA AV ČR KJB300420801 Institutional research plan: CEZ:AV0Z30420517 Keywords : heavy precipitation * extreme value analysis * region-of-influence method * central Europe Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.462, year: 2009 http://www.hydrol-earth-syst-sci.net/13/2203/2009/

  11. Probability estimates of heavy precipitation events in a flood-prone central-European region with enhanced influence of Mediterranean cyclones

    Czech Academy of Sciences Publication Activity Database

    Kyselý, Jan; Picek, J.

    2007-01-01

    Roč. 12, - (2007), s. 43-50 ISSN 1680-7340 R&D Projects: GA AV ČR KJB300420601 Institutional research plan: CEZ:AV0Z30420517 Keywords : extreme precipitation event * region al frequency analysis * Generalized Extreme Value distribution * Generalized Logistic distribution * central Europe * Czech Republic Subject RIV: DG - Athmosphere Sciences, Meteorology www.adv-geosci.net/12/43/2007/

  12. Linking precipitation, evapotranspiration and soil moisture content for the improvement of predictability over land

    Science.gov (United States)

    Catalano, Franco; Alessandri, Andrea; De Felice, Matteo

    2013-04-01

    Climate change scenarios are expected to show an intensification of the hydrological cycle together with modifications of evapotranspiration and soil moisture content. Evapotranspiration changes have been already evidenced for the end of the 20th century. The variance of evapotranspiration has been shown to be strongly related to the variance of precipitation over land. Nevertheless, the feedbacks between evapotranspiration, soil moisture and precipitation have not yet been completely understood at present-day. Furthermore, soil moisture reservoirs are associated to a memory and thus their proper initialization may have a strong influence on predictability. In particular, the linkage between precipitation and soil moisture is modulated by the effects on evapotranspiration. Therefore, the investigation of the coupling between these variables appear to be of primary importance for the improvement of predictability over the continents. The coupled manifold (CM) technique (Navarra and Tribbia 2005) is a method designed to separate the effects of the variability of two variables which are connected. This method has proved to be successful for the analysis of different climate fields, like precipitation, vegetation and sea surface temperature. In particular, the coupled variables reveal patterns that may be connected with specific phenomena, thus providing hints regarding potential predictability. In this study we applied the CM to recent observational datasets of precipitation (from CRU), evapotranspiration (from GIMMS and MODIS satellite-based estimates) and soil moisture content (from ESA) spanning a time period of 23 years (1984-2006) with a monthly frequency. Different data stratification (monthly, seasonal, summer JJA) have been employed to analyze the persistence of the patterns and their characteristical time scales and seasonality. The three variables considered show a significant coupling among each other. Interestingly, most of the signal of the

  13. From extended integrity monitoring to the safety evaluation of satellite-based localisation system

    International Nuclear Information System (INIS)

    Legrand, Cyril; Beugin, Julie; Marais, Juliette; Conrard, Blaise; El-Koursi, El-Miloudi; Berbineau, Marion

    2016-01-01

    Global Navigation Satellite Systems (GNSS) such as GPS, already used in aeronautics for safety-related applications, can play a major role in railway safety by allowing a train to locate itself safely. However, in order to implement this positioning solution in any embedded system, its performances must be evaluated according to railway standards. The evaluation of GNSS performances is not based on the same attributes class than RAMS evaluation. Face to these diffculties, we propose to express the integrity attribute, performance of satellite-based localisation. This attribute comes from aeronautical standards and for a hybridised GNSS with inertial system. To achieve this objective, the integrity attribute must be extended to this kind of system and algorithms initially devoted to GNSS integrity monitoring only must be adapted. Thereafter, the formalisation of this integrity attribute permits us to analyse the safety quantitatively through the probabilities of integrity risk and wrong-side failure. In this paper, after an introductory discussion about the use of localisation systems in railway safety context together with integrity issues, a particular integrity monitoring is proposed and described. The detection events of this algorithm permit us to conclude about safety level of satellite-based localisation system.

  14. Precipitation and Carbon-Water Coupling Jointly Control the Interannual Variability of Global Land Gross Primary Production

    Science.gov (United States)

    Zhang, Yao; Xiao, Xiangming; Guanter, Luis; Zhou, Sha; Ciais, Philippe; Joiner, Joanna; Sitch, Stephen; Wu, Xiaocui; Nabel, Julian; Dong, Jinwei; hide

    2016-01-01

    Carbon uptake by terrestrial ecosystems is increasing along with the rising of atmospheric CO2 concentration. Embedded in this trend, recent studies suggested that the interannual variability (IAV) of global carbon fluxes may be dominated by semi-arid ecosystems, but the underlying mechanisms of this high variability in these specific regions are not well known. Here we derive an ensemble of gross primary production (GPP) estimates using the average of three data-driven models and eleven process-based models. These models are weighted by their spatial representativeness of the satellite-based solar-induced chlorophyll fluorescence (SIF). We then use this weighted GPP ensemble to investigate the GPP variability for different aridity regimes. We show that semi-arid regions contribute to 57% of the detrended IAV of global GPP. Moreover, in regions with higher GPP variability, GPP fluctuations are mostly controlled by precipitation and strongly coupled with evapotranspiration (ET). This higher GPP IAV in semi-arid regions is co-limited by supply (precipitation)-induced ET variability and GPP-ET coupling strength. Our results demonstrate the importance of semi-arid regions to the global terrestrial carbon cycle and posit that there will be larger GPP and ET variations in the future with changes in precipitation patterns and dryland expansion.

  15. A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields

    Directory of Open Access Journals (Sweden)

    Martha C. Anderson

    2013-07-01

    Full Text Available Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period.

  16. Climate Prediction Center(CPC)Daily GOES Precipitation Index (GPI)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — GOES Precipitation Index (GPI) is a precipitation estimation algorithm. The GPI technique estimates tropical rainfall using cloud-top temperature as the sole...

  17. Understanding SMAP-L4 soil moisture estimation skill and their dependence with topography, precipitation and vegetation type using Mesonet and Micronet networks.

    Science.gov (United States)

    Moreno, H. A.; Basara, J. B.; Thompson, E.; Bertrand, D.; Johnston, C. S.

    2017-12-01

    Soil moisture measurements using satellite information can benefit from a land data assimilation model Goddard Earth Observing System (GEOS-5) and land data assimilation system (LDAS) to improve the representation of fine-scale dynamics and variability. This work presents some advances to understand the predictive skill of L4-SM product across different land-cover types, topography and precipitation totals, by using a dense network of multi-level soil moisture sensors (i.e. Mesonet and Micronet) in Oklahoma. 130 soil moisture stations are used across different precipitation gradients (i.e. arid vs wet), land cover (e.g. forest, shrubland, grasses, crops), elevation (low, mid and high) and slope to assess the improvements by the L4_SM product relative to the raw SMAP L-band brightness temperatures. The comparisons are conducted between July 2015 and July 2016 at the daily time scale. Results show the highest L4-SM overestimations occur in pastures and cultivated crops, during the rainy season and at higher elevation lands (over 800 meters asl). The smallest errors occur in low elevation lands, low rainfall and developed lands. Forested area's soil moisture biases lie in between pastures (max biases) and low intensity/developed lands (min biases). Fine scale assessment of L4-SM should help GEOS-5 and LDAS teams refine model parameters in light of observed differences and improve assimilation techniques in light of land-cover, topography and precipitation regime. Additionally, regional decision makers could have a framework to weight the utility of this product for water resources applications.

  18. Atmospheric balance of the humidity and estimate of the precipitation recycled in Colombia according to the re-analysis NCEP/NCAR

    International Nuclear Information System (INIS)

    Cuartas, Adriana; Poveda, German

    2002-01-01

    The magnitudes of the entrance humidity flows and exit are considered and the amount of precipitable water at different levels from the atmospheric column on Colombia. The water balance is quantified in the Colombian atmosphere; the regions and the atmospheric levels of entrance and exit of humidity are identified. The hypothesis that in the long term the net atmospheric humidity influence must be equal to the average of long term of the net run-off is verified. In addition, the percentage of recycled precipitation is considered on the Colombian territory. The variability during the two phases of the ENSO is analyzed. The calculations are made with the information of the climatic project Reanalysis developed by the National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR), with the collaboration of the National Oceanic and Atmospheric Administration (NOAA)/National Environmental Satellite of the U.S.A. For this work it was counted on monthly information of 41 years between 1958-1998. The hydrological information was obtained from the project Balances Hidrologicos de Colombia, 1999, made by the Posgrado de Recursos Hidraulicos, de la Universidad Nacional, with the support of COLCIENCIAS and the Unidad de Planeacion Minero Energetica-UPME. The results showed the average value of the net influence of humidity to the atmosphere of Colombia is of 5716 mm/year, with a great variability in both phases of the ENSO. The greater humidity advection towards Colombia occurs in the low levels of pressure (between 1000 and 850 hPa), and originating of all the directions, mainly of trade winds of the east and trade winds of the west. Also one was that the greater humidity transport towards Colombia occurs in trimesters DJF and MAM, with average values 505,1 and 606,6 mm/year, respectively. It was observed that the hypothesis that in the long term, the net atmospheric flux, is equal to the net terrestrial run-off, reasonably is adapted for

  19. Assessing the importance of spatio-temporal RCM resolution when estimating sub-daily extreme precipitation under current and future climate conditions

    DEFF Research Database (Denmark)

    Sunyer Pinya, Maria Antonia; Luchner, J.; Onof, C.

    2017-01-01

    extreme precipitation over Denmark generated by the regional climate model (RCM) HIRHAM-ECEARTH at different spatial resolutions (8, 12, 25 and 50km), three RCM from the RiskChange project at 8km resolution and three RCMs from ENSEMBLES at 25km resolution at temporal aggregations from 1 to 48h...... are more skewed than the observational dataset, which leads to an overestimation by the higher spatial resolution simulations. Nevertheless, in general, under current conditions RCM simulations at high spatial resolution represent extreme events and high-order moments better. The changes projected...

  20. Evaluation of the TMPA-3B42 precipitation product using a high-density rain gauge network over complex terrain in northeastern Iberia

    KAUST Repository

    El Kenawy, Ahmed M.

    2015-08-29

    informative addition to the spatial and temporal coverage of rain gauges in the domain, offering insights into characteristics of average precipitation and their spatial patterns. However, the satellite-based precipitation data should be used cautiously for monitoring extreme precipitation events, particularly over complex terrain. An improvement in precipitation algorithms is still needed to more accurately reproduce high precipitation events in areas of heterogeneous topography over this region.

  1. Evaluation of the TMPA-3B42 precipitation product using a high-density rain gauge network over complex terrain in northeastern Iberia

    KAUST Repository

    El Kenawy, Ahmed M.; Lopez-Moreno, Juan I.; McCabe, Matthew; Vicente-Serrano, Sergio M.

    2015-01-01

    informative addition to the spatial and temporal coverage of rain gauges in the domain, offering insights into characteristics of average precipitation and their spatial patterns. However, the satellite-based precipitation data should be used cautiously for monitoring extreme precipitation events, particularly over complex terrain. An improvement in precipitation algorithms is still needed to more accurately reproduce high precipitation events in areas of heterogeneous topography over this region.

  2. Use of Thermal Data to Estimate Infiltration in Pagany Wash Associated with the winter of 1997-1998 El Nino Precipitation, Yucca Mountain, Nevada

    International Nuclear Information System (INIS)

    LeCain, G.D.; Lu, N.; Kurzmack, M.

    2000-01-01

    Temperature and air-pressure monitoring in a vertical borehole located in Pagany Wash, a normally dry stream-carved channel northeast of Yucca Mountain, Nevada, indicated that the annual temperature wave was measurable to a depth of 11.1 m. Temperature depressions were measured at depths of 3.1, 6.1, 9.2, and 11.1 m below ground surface. The temperature depressions were interpreted to be the result of infiltration associated with the 1997-1998 El Nino precipitation. A pressure differential, of approximately 2 kiloPascals, between stations located 11.1 and 24.5 m below ground surface was interpreted to be the result of compressed air ahead of the wetting front. The pressure differences between stations indicated that the wetting front migrated deeper than 35.2 m and that the Yucca Mountain Tuff retarded the downward movement of the wetting front. An analytical method indicated that the infiltration flux through the Pagany Wash alluvium due to the 1997-1998 El Nino precipitation was approximately 940 mm. A one-dimensional numerical model indicated that the infiltration flux was approximately 1000 mm. Sensitivity analysis indicated that the potential temperature decrease due to conduction was minimal and that cooler surface temperatures could not account for the measured subsurface temperature depressions

  3. Ground-and satellite-based evidence of the biophysical mechanisms behind the greening Sahel

    DEFF Research Database (Denmark)

    Brandt, Martin Stefan; Mbow, Cheikh; Diouf, Abdoul A.

    2015-01-01

    After a dry period with prolonged droughts in the 1970s and 1980s, recent scientific outcome suggests that the decades of abnormally dry conditions in the Sahel have been reversed by positive anomalies in rainfall. Various remote sensing studies observed a positive trend in vegetation greenness...... over the last decades which is known as the re-greening of the Sahel. However, little investment has been made in including long-term ground-based data collections to evaluate and better understand the biophysical mechanisms behind these findings. Thus, deductions on a possible increment in biomass...... remain speculative. Our aim is to bridge these gaps and give specifics on the biophysical background factors of the re-greening Sahel. Therefore, a trend analysis was applied on long time series (1987-2013) of satellite-based vegetation and rainfall data, as well as on ground-observations of leaf biomass...

  4. Engineering satellite-based navigation and timing global navigation satellite systems, signals, and receivers

    CERN Document Server

    Betz, J

    2016-01-01

    This book describes the design and performance analysis of satnav systems, signals, and receivers. It also provides succinct descriptions and comparisons of all the world’s satnav systems. Its comprehensive and logical structure addresses all satnav signals and systems in operation and being developed. Engineering Satellite-Based Navigation and Timing: Global Navigation Satellite Systems, Signals, and Receivers provides the technical foundation for designing and analyzing satnav signals, systems, and receivers. Its contents and structure address all satnav systems and signals: legacy, modernized, and new. It combines qualitative information with detailed techniques and analyses, providing a comprehensive set of insights and engineering tools for this complex multidisciplinary field. Part I describes system and signal engineering including orbital mechanics and constellation design, signal design principles and underlying considerations, link budgets, qua tifying receiver performance in interference, and e...

  5. Network design consideration of a satellite-based mobile communications system

    Science.gov (United States)

    Yan, T.-Y.

    1986-01-01

    Technical considerations for the Mobile Satellite Experiment (MSAT-X), the ground segment testbed for the low-cost spectral efficient satellite-based mobile communications technologies being developed for the 1990's, are discussed. The Network Management Center contains a flexible resource sharing algorithm, the Demand Assigned Multiple Access scheme, which partitions the satellite transponder bandwidth among voice, data, and request channels. Satellite use of multiple UHF beams permits frequency reuse. The backhaul communications and the Telemetry, Tracking and Control traffic are provided through a single full-coverage SHF beam. Mobile Terminals communicate with the satellite using UHF. All communications including SHF-SHF between Base Stations and/or Gateways, are routed through the satellite. Because MSAT-X is an experimental network, higher level network protocols (which are service-specific) will be developed only to test the operation of the lowest three levels, the physical, data link, and network layers.

  6. Advancing land surface model development with satellite-based Earth observations

    Science.gov (United States)

    Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo

    2017-04-01

    The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks. Orth, R., E. Dutra, I. F. Trigo, and G. Balsamo (2016): Advancing land surface model development with satellite-based Earth observations. Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-628

  7. Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals

    Science.gov (United States)

    Meyer, Hanna; Kühnlein, Meike; Appelhans, Tim; Nauss, Thomas

    2016-03-01

    Machine learning (ML) algorithms have successfully been demonstrated to be valuable tools in satellite-based rainfall retrievals which show the practicability of using ML algorithms when faced with high dimensional and complex data. Moreover, recent developments in parallel computing with ML present new possibilities for training and prediction speed and therefore make their usage in real-time systems feasible. This study compares four ML algorithms - random forests (RF), neural networks (NNET), averaged neural networks (AVNNET) and support vector machines (SVM) - for rainfall area detection and rainfall rate assignment using MSG SEVIRI data over Germany. Satellite-based proxies for cloud top height, cloud top temperature, cloud phase and cloud water path serve as predictor variables. The results indicate an overestimation of rainfall area delineation regardless of the ML algorithm (averaged bias = 1.8) but a high probability of detection ranging from 81% (SVM) to 85% (NNET). On a 24-hour basis, the performance of the rainfall rate assignment yielded R2 values between 0.39 (SVM) and 0.44 (AVNNET). Though the differences in the algorithms' performance were rather small, NNET and AVNNET were identified as the most suitable algorithms. On average, they demonstrated the best performance in rainfall area delineation as well as in rainfall rate assignment. NNET's computational speed is an additional advantage in work with large datasets such as in remote sensing based rainfall retrievals. However, since no single algorithm performed considerably better than the others we conclude that further research in providing suitable predictors for rainfall is of greater necessity than an optimization through the choice of the ML algorithm.

  8. Current trends in satellite based emergency mapping - the need for harmonisation

    Science.gov (United States)

    Voigt, Stefan

    2013-04-01

    During the past years, the availability and use of satellite image data to support disaster management and humanitarian relief organisations has largely increased. The automation and data processing techniques are greatly improving as well as the capacity in accessing and processing satellite imagery in getting better globally. More and more global activities via the internet and through global organisations like the United Nations or the International Charter Space and Major Disaster engage in the topic, while at the same time, more and more national or local centres engage rapid mapping operations and activities. In order to make even more effective use of this very positive increase of capacity, for the sake of operational provision of analysis results, for fast validation of satellite derived damage assessments, for better cooperation in the joint inter agency generation of rapid mapping products and for general scientific use, rapid mapping results in general need to be better harmonized, if not even standardized. In this presentation, experiences from various years of rapid mapping gained by the DLR Center for satellite based Crisis Information (ZKI) within the context of the national activities, the International Charter Space and Major Disasters, GMES/Copernicus etc. are reported. Furthermore, an overview on how automation, quality assurance and optimization can be achieved through standard operation procedures within a rapid mapping workflow is given. Building on this long term rapid mapping experience, and building on the DLR initiative to set in pace an "International Working Group on Satellite Based Emergency Mapping" current trends in rapid mapping are discussed and thoughts on how the sharing of rapid mapping information can be optimized by harmonizing analysis results and data structures are presented. Such an harmonization of analysis procedures, nomenclatures and representations of data as well as meta data are the basis to better cooperate within

  9. An approach to estimate the freshwater contribution from glacial melt and precipitation in East Greenland shelf waters using colored dissolved organic matter (CDOM)

    DEFF Research Database (Denmark)

    Stedmon, Colin; Granskog, Mats A.; Dodd, Paul A.

    2015-01-01

    Changes in the supply and storage of freshwater in the Arctic Ocean and its subsequent export to the North Atlantic can potentially influence ocean circulation and climate. In order to understand how the Arctic freshwater budget is changing and the potential impacts, it is important to develop......, and precipitation) and sea ice melt. We develop this approach further and investigate the use of an additional tracer, colored dissolved organic matter (CDOM), which is largely specific to freshwater originating from Arctic rivers. A robust relationship between the freshwater contribution from meteoric water...... processes (riverine input and sea ice formation), while previously, these waters where thought to be derived from open sea processes (cooling and sea ice formation) in the northern Barents and Kara Seas. In Greenlandic coastal waters the meteoric water contribution is influenced by Greenland ice sheet...

  10. Characterization of precipitation features over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

    Science.gov (United States)

    Prat, O. P.; Nelson, B. R.

    2013-12-01

    We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, surface observations, and models to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets of TMPA 3B42, CMORPH, and PERSIANN. The satellite based QPEs are compared over the concurrent period with the NCEP Stage IV product, which is a near real time product providing precipitation data at the hourly temporal scale gridded at a nominal 4-km spatial resolution. In addition, remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model), which provides gridded precipitation estimates that are used as a baseline for multi-sensor QPE products comparison. The comparisons are performed at the annual, seasonal, monthly, and daily scales with focus on selected river basins (Southeastern US, Pacific Northwest, Great Plains). While, unconditional annual rain rates present a satisfying agreement between all products, results suggest that satellite QPE datasets exhibit important biases in particular at higher rain rates (≥4 mm/day). Conversely, on seasonal scales differences between remotely sensed data and ground surface observations can be greater than 50% and up to 90% for low daily accumulation (≤1 mm/day) such as in the Western US (summer) and Central US (winter). The conditional analysis performed using different daily rainfall accumulation thresholds (from low rainfall intensity to intense precipitation) shows that while intense events measured at the ground are infrequent (around 2% for daily accumulation above 2 inches/day), remotely sensed products displayed differences from 20-50% and up to 90-100%. A discussion on the impact of differing spatial and temporal resolutions with respect to the datasets ability to capture extreme

  11. Detecting robust signals of interannual variability of gross primary productivity in Asia from multiple terrestrial carbon cycle models and long-term satellite-based vegetation data

    Science.gov (United States)

    Ichii, K.; Kondo, M.; Ueyama, M.; Kato, T.; Ito, A.; Sasai, T.; Sato, H.; Kobayashi, H.; Saigusa, N.

    2014-12-01

    Long term record of satellite-based terrestrial vegetation are important to evaluate terrestrial carbon cycle models. In this study, we demonstrate how multiple satellite observation can be used for evaluating past changes in gross primary productivity (GPP) and detecting robust anomalies in terrestrial carbon cycle in Asia through our model-data synthesis analysis, Asia-MIP. We focused on the two different temporal coverages: long-term (30 years; 1982-2011) and decadal (10 years; 2001-2011; data intensive period) scales. We used a NOAA/AVHRR NDVI record for long-term analysis and multiple satellite data and products (e.g. Terra-MODIS, SPOT-VEGETATION) as historical satellite data, and multiple terrestrial carbon cycle models (e.g. BEAMS, Biome-BGC, ORCHIDEE, SEIB-DGVM, and VISIT). As a results of long-term (30 years) trend analysis, satellite-based time-series data showed that approximately 40% of the area has experienced a significant increase in the NDVI, while only a few areas have experienced a significant decreasing trend over the last 30 years. The increases in the NDVI were dominant in the sub-continental regions of Siberia, East Asia, and India. Simulations using the terrestrial biosphere models also showed significant increases in GPP, similar to the results for the NDVI, in boreal and temperate regions. A modeled sensitivity analysis showed that the increases in GPP are explained by increased temperature and precipitation in Siberia. Precipitation, solar radiation, CO2fertilization and land cover changes are important factors in the tropical regions. However, the relative contributions of each factor to GPP changes are different among the models. Year-to-year variations of terrestrial GPP were overall consistently captured by the satellite data and terrestrial carbon cycle models if the anomalies are large (e.g. 2003 summer GPP anomalies in East Asia and 2002 spring GPP anomalies in mid to high latitudes). The behind mechanisms can be consistently

  12. A global gridded dataset of daily precipitation going back to 1950, ideal for analysing precipitation extremes

    Science.gov (United States)

    Contractor, S.; Donat, M.; Alexander, L. V.

    2017-12-01

    Reliable observations of precipitation are necessary to determine past changes in precipitation and validate models, allowing for reliable future projections. Existing gauge based gridded datasets of daily precipitation and satellite based observations contain artefacts and have a short length of record, making them unsuitable to analyse precipitation extremes. The largest limiting factor for the gauge based datasets is a dense and reliable station network. Currently, there are two major data archives of global in situ daily rainfall data, first is Global Historical Station Network (GHCN-Daily) hosted by National Oceanic and Atmospheric Administration (NOAA) and the other by Global Precipitation Climatology Centre (GPCC) part of the Deutsche Wetterdienst (DWD). We combine the two data archives and use automated quality control techniques to create a reliable long term network of raw station data, which we then interpolate using block kriging to create a global gridded dataset of daily precipitation going back to 1950. We compare our interpolated dataset with existing global gridded data of daily precipitation: NOAA Climate Prediction Centre (CPC) Global V1.0 and GPCC Full Data Daily Version 1.0, as well as various regional datasets. We find that our raw station density is much higher than other datasets. To avoid artefacts due to station network variability, we provide multiple versions of our dataset based on various completeness criteria, as well as provide the standard deviation, kriging error and number of stations for each grid cell and timestep to encourage responsible use of our dataset. Despite our efforts to increase the raw data density, the in situ station network remains sparse in India after the 1960s and in Africa throughout the timespan of the dataset. Our dataset would allow for more reliable global analyses of rainfall including its extremes and pave the way for better global precipitation observations with lower and more transparent uncertainties.

  13. Hydrological real-time modelling in the Zambezi river basin using satellite-based soil moisture and rainfall data

    Directory of Open Access Journals (Sweden)

    P. Meier

    2011-03-01

    Full Text Available Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. For the management of ecological releases even discharge forecasts with moderate accuracy can be beneficial. Sequential data assimilation using the Ensemble Kalman Filter provides a tool that is both efficient and robust for a real-time modelling framework. One key parameter in a hydrological system is the soil moisture, which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by a radar scatterometer. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modelling framework yields good results in watersheds where soil storage is an important factor. The lead time of the forecast is dependent on the size and the retention capacity of the watershed. For the largest watershed a forecast over 40 days can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In a watershed with little soil storage and a quick response to rainfall events, the performance is relatively poor and the lead time is as short as 10 days only.

  14. Average Estimates of Water-Budget Components Based on Hydrograph Separation and PRISM Precipitation for Gaged Basins in the Appalachian Plateaus Region, 1900-2011

    Data.gov (United States)

    Department of the Interior — As part of the U.S. Geological Survey’s Groundwater Resources Program study of the Appalachian Plateaus aquifers, estimates of annual water-budget components were...

  15. Annual Estimates of Water-Budget Components Based on Hydrograph Separation and PRISM Precipitation for Gaged Basins in the Appalachian Plateaus Region, 1900-2011

    Data.gov (United States)

    Department of the Interior — As part of the U.S. Geological Survey’s Groundwater Resources Program study of the Appalachian Plateaus aquifers, estimates of annual water-budget components were...

  16. Evaluation of Version-7 TRMM Multi-Satellite Precipitation Analysis Product during the Beijing Extreme Heavy Rainfall Event of 21 July 2012

    Directory of Open Access Journals (Sweden)

    Yong Huang

    2013-12-01

    Full Text Available The latest Version-7 (V7 Tropical Rainfall Measuring Mission (TRMM Multi-satellite Precipitation Analysis (TMPA products were released by the National Aeronautics and Space Administration (NASA in December of 2012. Their performance on different climatology, locations, and precipitation types is of great interest to the satellite-based precipitation community. This paper presents a study of TMPA precipitation products (3B42RT and 3B42V7 for an extreme precipitation event in Beijing and its adjacent regions (from 00:00 UTC 21 July 2012 to 00:00 UTC 22 July 2012. Measurements from a dense rain gauge network were used as the ground truth to evaluate the latest TMPA products. Results are summarized as follows. Compared to rain gauge measurements, both 3B42RT and 3B42V7 generally captured the rainfall spatial and temporal pattern, having a moderate spatial correlation coefficient (CC, 0.6 and high CC values (0.88 over the broader Hebei, Beijing and Tianjin (HBT regions, but the rainfall peak is 6 h ahead of gauge observations. Overall, 3B42RT showed higher estimation than 3B42V7 over both HBT and Beijing. At the storm center, both 3B42RT and 3B42V7 presented a relatively large deviation from the temporal variation of rainfall and underestimated the storm by 29.02% and 36.07%, respectively. The current study suggests that the latest TMPA products still have limitations in terms of resolution and accuracy, especially for this type of extreme event within a latitude area on the edge of coverage of TRMM precipitation radar and microwave imager. Therefore, TMPA users should be cautious when 3B42RT and 3B42V7 are used to model, monitor, and forecast both flooding hazards in the Beijing urban area and landslides in the mountainous west and north of Beijing.

  17. Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

    Science.gov (United States)

    Zambrano, Francisco; Wardlow, Brian; Tadesse, Tsegaye; Lillo-Saavedra, Mario; Lagos, Octavio

    2017-04-01

    Precipitation is a key parameter for the study of climate change and variability and the detection and monitoring of natural disaster such as drought. Precipitation datasets that accurately capture the amount and spatial variability of rainfall is critical for drought monitoring and a wide range of other climate applications. This is challenging in many parts of the world, which often have a limited number of weather stations and/or historical data records. Satellite-derived precipitation products offer a viable alternative with several remotely sensed precipitation datasets now available with long historical data records (+30years), which include the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) datasets. This study presents a comparative analysis of three historical satellite-based precipitation datasets that include Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 version 7 (1998-2015), PERSIANN-CDR (1983-2015) and CHIRPS 2.0 (1981-2015) over Chile to assess their performance across the country and for the case of the two long-term products the applicability for agricultural drought were evaluated when used in the calculation of commonly used drought indicator as the Standardized Precipitation Index (SPI). In this analysis, 278 weather stations of in situ rainfall measurements across Chile were initially compared to the satellite data. The study area (Chile) was divided into five latitudinal zones: North, North-Central, Central, South-Central and South to determine if there were a regional difference among these satellite products, and nine statistics were used to evaluate their performance to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to analyze

  18. Efficient all solid-state UV source for satellite-based lidar applications.

    Energy Technology Data Exchange (ETDEWEB)

    Armstrong, Darrell Jewell; Smith, Arlee Virgil

    2003-07-01

    A satellite-based UV-DIAL measurement system would allow continuous global monitoring of ozone concentration in the upper atmosphere. However such systems remain difficult to implement because aerosol-scattering return signals for satellite-based lidars are very weak. A suitable system must produce high-energy UV pulses at multiple wavelengths with very high efficiency. For example, a nanosecond system operating at 10 Hz must generate approximately 1 J per pulse at 308-320 nm. An efficient space-qualified wavelength-agile system based on a single UV source that can meet this requirement is probably not available using current laser technology. As an alternative, we're pursuing a multi-source approach employing all-solid-state modules that individually generate 300-320 nm light with pulse energies in the range of 50-200 mJ, with transform-limited bandwidths and good beam quality. Pulses from the individual sources can be incoherently summed to obtain the required single-pulse energy. These sources use sum-frequency mixing of the 532 nm second harmonic of an Nd:YAG pump laser with 731-803 nm light derived from a recently-developed, state-of-the-art, nanosecond optical parametric oscillator. Two source configurations are under development, one using extra-cavity sum-frequency mixing, and the other intra-cavity sum-frequency mixing. In either configuration, we hope to obtain sum-frequency mixing efficiency approaching 60% by carefully matching the spatial and temporal properties of the laser and OPO pulses. This ideal balance of green and near-IR photons requires an injection-seeded Nd:YAG pump-laser with very high beam quality, and an OPO exhibiting unusually high conversion efficiency and exceptional signal beam quality. The OPO employs a singly-resonant high-Fresnel-number image-rotating self-injection-seeded nonplanar-ring cavity that achieves pump depletion > 65% and produces signal beams with M{sup 2} {approx} 3 at pulse energies exceeding 50 mJ. Pump beam

  19. Using NDVI to estimate carbon fluxes from small rotationally grazed pastures

    Science.gov (United States)

    Satellite-based Normalized Difference Vegetation Index (NDVI) data have been extensively used for estimating gross primary productivity (GPP) and yield of grazing lands throughout the world. However, the usefulness of satellite-based images for monitoring rotationally-grazed pastures in the northea...

  20. Providing satellite-based early warnings of fires to reduce fire flashovers on South Africa’s transmission lines

    CSIR Research Space (South Africa)

    Frost, PE

    2007-07-01

    Full Text Available The Advanced Fire Information System (AFIS) is the first near real time operational satellite-based fire monitoring system of its kind in Africa. The main aim of AFIS is to provide information regarding the prediction, detection and assessment...

  1. Retrospective Analog Year Analyses Using NASA Satellite Precipitation and Soil Moisture Data to Improve USDA's World Agricultural Supply and Demand Estimates

    Science.gov (United States)

    Teng, William; Shannon, Harlan; Mladenova, Iliana; Fang, Fan

    2010-01-01

    A primary goal of the U.S. Department of Agriculture (USDA) is to expand markets for U.S. agricultural products and support global economic development. The USDA World Agricultural Outlook Board (WAOB) supports this goal by coordinating monthly World Agricultural Supply and Demand Estimates (WASDE) for the U.S. and major foreign producing countries. Because weather has a significant impact on crop progress, conditions, and production, WAOB prepares frequent agricultural weather assessments, in a GIS-based, Global Agricultural Decision Support Environment (GLADSE). The main goal of this project, thus, is to improve WAOB's estimates by integrating NASA remote sensing soil moisture observations and research results into GLADSE (See diagram below). Soil moisture is currently a primary data gap at WAOB.

  2. Advancing land surface model development with satellite-based Earth observations

    Science.gov (United States)

    Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo

    2017-05-01

    The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts, we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability, and understanding of climate system feedbacks.

  3. Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration

    CERN Document Server

    Noureldin, Aboelmagd; Georgy, Jacques

    2013-01-01

    Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration is an introduction to the field of Integrated Navigation Systems. It serves as an excellent reference for working engineers as well as textbook for beginners and students new to the area. The book is easy to read and understand with minimum background knowledge. The authors explain the derivations in great detail. The intermediate steps are thoroughly explained so that a beginner can easily follow the material. The book shows a step-by-step implementation of navigation algorithms and provides all the necessary details. It provides detailed illustrations for an easy comprehension. The book also demonstrates real field experiments and in-vehicle road test results with professional discussions and analysis. This work is unique in discussing the different INS/GPS integration schemes in an easy to understand and straightforward way. Those schemes include loosely vs tightly coupled, open loop vs closed loop, and many more.

  4. Satellite-based emission constraint for nitrogen oxides: Capability and uncertainty

    Science.gov (United States)

    Lin, J.; McElroy, M. B.; Boersma, F.; Nielsen, C.; Zhao, Y.; Lei, Y.; Liu, Y.; Zhang, Q.; Liu, Z.; Liu, H.; Mao, J.; Zhuang, G.; Roozendael, M.; Martin, R.; Wang, P.; Spurr, R. J.; Sneep, M.; Stammes, P.; Clemer, K.; Irie, H.

    2013-12-01

    Vertical column densities (VCDs) of tropospheric nitrogen dioxide (NO2) retrieved from satellite remote sensing have been employed widely to constrain emissions of nitrogen oxides (NOx). A major strength of satellite-based emission constraint is analysis of emission trends and variability, while a crucial limitation is errors both in satellite NO2 data and in model simulations relating NOx emissions to NO2 columns. Through a series of studies, we have explored these aspects over China. We separate anthropogenic from natural sources of NOx by exploiting their different seasonality. We infer trends of NOx emissions in recent years and effects of a variety of socioeconomic events at different spatiotemporal scales including the general economic growth, global financial crisis, Chinese New Year, and Beijing Olympics. We further investigate the impact of growing NOx emissions on particulate matter (PM) pollution in China. As part of recent developments, we identify and correct errors in both satellite NO2 retrieval and model simulation that ultimately affect NOx emission constraint. We improve the treatments of aerosol optical effects, clouds and surface reflectance in the NO2 retrieval process, using as reference ground-based MAX-DOAS measurements to evaluate the improved retrieval results. We analyze the sensitivity of simulated NO2 to errors in the model representation of major meteorological and chemical processes with a subsequent correction of model bias. Future studies will implement these improvements to re-constrain NOx emissions.

  5. New perspectives for satellite-based archaeological research in the ancient territory of Hierapolis (Turkey

    Directory of Open Access Journals (Sweden)

    R. Lasaponara

    2008-11-01

    Full Text Available This paper deals with the use of satellite QuickBird images to find traces of past human activity in the ancient territory of Hierapolis (Turkey. This is one of the most important archaeological sites in Turkey, and in 1988 it was inscribed in the UNESCO World Heritage list. Although over the years the archaeological site of Hierapolis has been excavated, restored and well documented, up to now the territory around the ancient urban area is still largely unknown. The current research project, still in progress, aims to search the area neighbouring Hierapolis believed to have been under the control of the city for a long time and, therefore, expected to be very rich in archaeological evidence. In order to investigate a large area around the ancient Hierapolis and discover potential archaeological remains, QuickBird images were adopted.

    Results from satellite-based analysis allowed us to find several unknown rural settlements dating back to early Imperial Roman and the Byzantine age. Two significant test sites were focused on in this paper in order to characterize the different spectral responses observed for different types of archaeological features (shadow and soil marks. Principal Component Analysis and spectral indices were computed to enhance archaeological marks and make identification easier. The capability of the QuickBird data set (panchromatic, multispectral channel, PCA and spectral indices in searching for archaeological marks was assessed in a quantitative way by using a specific indicator.

  6. Does Urban Form Affect Urban NO2? Satellite-Based Evidence for More than 1200 Cities.

    Science.gov (United States)

    Bechle, Matthew J; Millet, Dylan B; Marshall, Julian D

    2017-11-07

    Modifying urban form may be a strategy to mitigate urban air pollution. For example, evidence suggests that urban form can affect motor vehicle usage, a major contributor to urban air pollution. We use satellite-based measurements of urban form and nitrogen dioxide (NO 2 ) to explore relationships between urban form and air pollution for a global data  set of 1274 cities. Three of the urban form metrics studied (contiguity, circularity, and vegetation) have a statistically significant relationship with urban NO 2 ; their combined effect could be substantial. As illustration, if findings presented here are causal, that would suggest that if Christchurch, New Zealand (a city at the 75th percentile for all three urban-form metrics, and with a network of buses, trams, and bicycle facilities) was transformed to match the urban form of Indio - Cathedral City, California, United States (a city at the 25th percentile for those same metrics, and exhibiting sprawl-like suburban development), our models suggest that Christchurch's NO 2 concentrations would be ∼60% higher than its current level. We also find that the combined effect of urban form on NO 2 is larger for small cities (β × IQR = -0.46 for cities urban population and are where much of the future urban growth is expected to occur. This work highlights the need for future study of how changes in urban form and related land use and transportation policies impact urban air pollution, especially for small cities.

  7. An Exploitation of Satellite-based Observation for Health Information: The UFOS Project

    Energy Technology Data Exchange (ETDEWEB)

    Mangin, A.; Morel, M.; Fanton d' Andon, O

    2000-07-01

    Short, medium and long-term trends of UV intensity levels are of crucial importance for either assessing effective biological impacts on human population, or implementing adequate preventive behaviours. Better information on a large spatial scale and increased public awareness of the short-term variations in UV values will help to support health agencies' goals of educating the public on UV risks. The Ultraviolet Forecast Operational Service Project (UFAS), financed in part by the European Commission/DG Information Society (TEN-TELECOM programme), aims to exploit satellite-based observations and to supply a set of UV products directly useful to health care. The short-term objective is to demonstrate the technical and economical feasibility and benefits that could be brought by such a system. UFOS is carried out by ACRI, with the support of an Advisory Group chaired by WHO and involving representation from the sectors of Health (WHO, INTERSUN collaborating centres, ZAMBON), Environment (WMO, IASB), and Telecommunications (EURECOM, IMET). (author)

  8. The attitude inversion method of geostationary satellites based on unscented particle filter

    Science.gov (United States)

    Du, Xiaoping; Wang, Yang; Hu, Heng; Gou, Ruixin; Liu, Hao

    2018-04-01

    The attitude information of geostationary satellites is difficult to be obtained since they are presented in non-resolved images on the ground observation equipment in space object surveillance. In this paper, an attitude inversion method for geostationary satellite based on Unscented Particle Filter (UPF) and ground photometric data is presented. The inversion algorithm based on UPF is proposed aiming at the strong non-linear feature in the photometric data inversion for satellite attitude, which combines the advantage of Unscented Kalman Filter (UKF) and Particle Filter (PF). This update method improves the particle selection based on the idea of UKF to redesign the importance density function. Moreover, it uses the RMS-UKF to partially correct the prediction covariance matrix, which improves the applicability of the attitude inversion method in view of UKF and the particle degradation and dilution of the attitude inversion method based on PF. This paper describes the main principles and steps of algorithm in detail, correctness, accuracy, stability and applicability of the method are verified by simulation experiment and scaling experiment in the end. The results show that the proposed method can effectively solve the problem of particle degradation and depletion in the attitude inversion method on account of PF, and the problem that UKF is not suitable for the strong non-linear attitude inversion. However, the inversion accuracy is obviously superior to UKF and PF, in addition, in the case of the inversion with large attitude error that can inverse the attitude with small particles and high precision.

  9. An Exploitation of Satellite-based Observation for Health Information: The UFOS Project

    International Nuclear Information System (INIS)

    Mangin, A.; Morel, M.; Fanton d'Andon, O.

    2000-01-01

    Short, medium and long-term trends of UV intensity levels are of crucial importance for either assessing effective biological impacts on human population, or implementing adequate preventive behaviours. Better information on a large spatial scale and increased public awareness of the short-term variations in UV values will help to support health agencies' goals of educating the public on UV risks. The Ultraviolet Forecast Operational Service Project (UFAS), financed in part by the European Commission/DG Information Society (TEN-TELECOM programme), aims to exploit satellite-based observations and to supply a set of UV products directly useful to health care. The short-term objective is to demonstrate the technical and economical feasibility and benefits that could be brought by such a system. UFOS is carried out by ACRI, with the support of an Advisory Group chaired by WHO and involving representation from the sectors of Health (WHO, INTERSUN collaborating centres, ZAMBON), Environment (WMO, IASB), and Telecommunications (EURECOM, IMET). (author)

  10. Satellite-based trends of solar radiation and cloud parameters in Europe

    Science.gov (United States)

    Pfeifroth, Uwe; Bojanowski, Jedrzej S.; Clerbaux, Nicolas; Manara, Veronica; Sanchez-Lorenzo, Arturo; Trentmann, Jörg; Walawender, Jakub P.; Hollmann, Rainer

    2018-04-01

    Solar radiation is the main driver of the Earth's climate. Measuring solar radiation and analysing its interaction with clouds are essential for the understanding of the climate system. The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates satellite-based, high-quality climate data records, with a focus on the energy balance and water cycle. Here, multiple of these data records are analyzed in a common framework to assess the consistency in trends and spatio-temporal variability of surface solar radiation, top-of-atmosphere reflected solar radiation and cloud fraction. This multi-parameter analysis focuses on Europe and covers the time period from 1992 to 2015. A high correlation between these three variables has been found over Europe. An overall consistency of the climate data records reveals an increase of surface solar radiation and a decrease in top-of-atmosphere reflected radiation. In addition, those trends are confirmed by negative trends in cloud cover. This consistency documents the high quality and stability of the CM SAF climate data records, which are mostly derived independently from each other. The results of this study indicate that one of the main reasons for the positive trend in surface solar radiation since the 1990's is a decrease in cloud coverage even if an aerosol contribution cannot be completely ruled out.

  11. GPM, DPR Level 2A Ka Precipitation V03

    Data.gov (United States)

    National Aeronautics and Space Administration — The 2AKa algorithm provides precipitation estimates from the Ka radar of the Dual-Frequency Precipitation Radar on the core GPM spacecraft. The product contains two...

  12. GPM, DPR Level 2A Ku Precipitation V03

    Data.gov (United States)

    National Aeronautics and Space Administration — The 2AKu algorithm provides precipitation estimates from the Ku radar of the Dual-Frequency Precipitation Radar on the core GPM spacecraft. The product contains one...

  13. Global Estimates of Average Ground-Level Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth

    Science.gov (United States)

    Van Donkelaar, A.; Martin, R. V.; Brauer, M.; Kahn, R.; Levy, R.; Verduzco, C.; Villeneuve, P.

    2010-01-01

    Exposure to airborne particles can cause acute or chronic respiratory disease and can exacerbate heart disease, some cancers, and other conditions in susceptible populations. Ground stations that monitor fine particulate matter in the air (smaller than 2.5 microns, called PM2.5) are positioned primarily to observe severe pollution events in areas of high population density; coverage is very limited, even in developed countries, and is not well designed to capture long-term, lower-level exposure that is increasingly linked to chronic health effects. In many parts of the developing world, air quality observation is absent entirely. Instruments aboard NASA Earth Observing System satellites, such as the MODerate resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR), monitor aerosols from space, providing once daily and about once-weekly coverage, respectively. However, these data are only rarely used for health applications, in part because the can retrieve the amount of aerosols only summed over the entire atmospheric column, rather than focusing just on the near-surface component, in the airspace humans actually breathe. In addition, air quality monitoring often includes detailed analysis of particle chemical composition, impossible from space. In this paper, near-surface aerosol concentrations are derived globally from the total-column aerosol amounts retrieved by MODIS and MISR. Here a computer aerosol simulation is used to determine how much of the satellite-retrieved total column aerosol amount is near the surface. The five-year average (2001-2006) global near-surface aerosol concentration shows that World Health Organization Air Quality standards are exceeded over parts of central and eastern Asia for nearly half the year.

  14. An intercomparison of satellite-based daily evapotranspiration estimates under different eco-climatic regions in South Africa

    CSIR Research Space (South Africa)

    Majozi, Nobuhle P

    2017-03-01

    Full Text Available ), while windspeed and direction were measured at 2.5 m height using an RM Young wind sentry (Mod l 03001—Campbell Scientific Ltd., Logan, UT, USA), a d solar irradiance was monitored using a pyranometer (Apogee Instru ents, Lo an, UT, USA). R infall... a CS500 probe (Vaisala, Helsinki, Finland), while windspeed and direction were measured at 2.5 m height using an RM Young wind sentry (Model 03001—Campbell Scientific Ltd., Logan, UT, USA), and solar irradiance was monitored using a pyranometer...

  15. A scalable satellite-based crop yield mapper: Integrating satellites and crop models for field-scale estimation in India

    Science.gov (United States)

    Jain, M.; Singh, B.; Srivastava, A.; Lobell, D. B.

    2015-12-01

    Food security will be challenged over the upcoming decades due to increased food demand, natural resource degradation, and climate change. In order to identify potential solutions to increase food security in the face of these changes, tools that can rapidly and accurately assess farm productivity are needed. With this aim, we have developed generalizable methods to map crop yields at the field scale using a combination of satellite imagery and crop models, and implement this approach within Google Earth Engine. We use these methods to examine wheat yield trends in Northern India, which provides over 15% of the global wheat supply and where over 80% of farmers rely on wheat as a staple food source. In addition, we identify the extent to which farmers are shifting sow date in response to heat stress, and how well shifting sow date reduces the negative impacts of heat stress on yield. To identify local-level decision-making, we map wheat sow date and yield at a high spatial resolution (30 m) using Landsat satellite imagery from 1980 to the present. This unique dataset allows us to examine sow date decisions at the field scale over 30 years, and by relating these decisions to weather experienced over the same time period, we can identify how farmers learn and adapt cropping decisions based on weather through time.

  16. Association Between Satellite-based Estimates of Long-term PM2.5 Exposure and Coronary Artery Disease

    Science.gov (United States)

    Background: Epidemiological studies have identified associations between long-term PM2.5 exposure and cardiovascular events, though most have relied on concentrations from central-site air quality monitors. Methods: We utilized a cohort of 5679 patients who had undergone cardiac ...

  17. The use of geostationary satellite based rainfall estimation and rainfall-runoff modelling for regional flash flood assessment

    OpenAIRE

    Suseno, Dwi Prabowo Yuga

    2013-01-01

    The availability of rainfall triggered hazard information such as flash flood is crucial in the flood disaster management and mitigation. However, providing that information is mainly hampered by the shortage of data because of the sparse, uneven or absence the hydrological or meteorological observation. Remote sensing techniques that make frequent observations with continuous spatial coverage provide useful information for detecting the hydrometeorological phenomena such as rainfall and floo...

  18. Air traffic management system design using satellite based geo-positioning and communications assets

    Science.gov (United States)

    Horkin, Phil

    1995-01-01

    The current FAA and ICAO FANS vision of Air Traffic Management will transition the functions of Communications, Navigation, and Surveillance to satellite based assets in the 21st century. Fundamental to widespread acceptance of this vision is a geo-positioning system that can provide worldwide access with best case differential GPS performance, but without the associated problems. A robust communications capability linking-up aircraft and towers to meet the voice and data requirements is also essential. The current GPS constellation does not provide continuous global coverage with a sufficient number of satellites to meet the precision landing requirements as set by the world community. Periodic loss of the minimum number of satellites in view creates an integrity problem, which prevents GPS from becoming the primary system for navigation. Furthermore, there is reluctance on the part of many countries to depend on assets like GPS and GLONASS which are controlled by military communities. This paper addresses these concerns and provides a system solving the key issues associated with navigation, automatic dependent surveillance, and flexible communications. It contains an independent GPS-like navigation system with 27 satellites providing global coverage with a minimum of six in view at all times. Robust communications is provided by a network of TDMA/FDMA communications payloads contained on these satellites. This network can support simultaneous communications for up to 30,000 links, nearly enough to simultaneously support three times the current global fleet of jumbo air passenger aircraft. All of the required hardware is directly traceable to existing designs.

  19. Categorizing natural disaster damage assessment using satellite-based geospatial techniques

    Science.gov (United States)

    Myint, S.W.; Yuan, M.; Cerveny, R.S.; Giri, C.

    2008-01-01

    Remote sensing of a natural disaster's damage offers an exciting backup and/or alternative to traditional means of on-site damage assessment. Although necessary for complete assessment of damage areas, ground-based damage surveys conducted in the aftermath of natural hazard passage can sometimes be potentially complicated due to on-site difficulties (e.g., interaction with various authorities and emergency services) and hazards (e.g., downed power lines, gas lines, etc.), the need for rapid mobilization (particularly for remote locations), and the increasing cost of rapid physical transportation of manpower and equipment. Satellite image analysis, because of its global ubiquity, its ability for repeated independent analysis, and, as we demonstrate here, its ability to verify on-site damage assessment provides an interesting new perspective and investigative aide to researchers. Using one of the strongest tornado events in US history, the 3 May 1999 Oklahoma City Tornado, as a case example, we digitized the tornado damage path and co-registered the damage path using pre- and post-Landsat Thematic Mapper image data to perform a damage assessment. We employed several geospatial approaches, specifically the Getis index, Geary's C, and two lacunarity approaches to categorize damage characteristics according to the original Fujita tornado damage scale (F-scale). Our results indicate strong relationships between spatial indices computed within a local window and tornado F-scale damage categories identified through the ground survey. Consequently, linear regression models, even incorporating just a single band, appear effective in identifying F-scale damage categories using satellite imagery. This study demonstrates that satellite-based geospatial techniques can effectively add spatial perspectives to natural disaster damages, and in particular for this case study, tornado damages.

  20. Regional geology mapping using satellite-based remote sensing approach in Northern Victoria Land, Antarctica

    Science.gov (United States)

    Pour, Amin Beiranvand; Park, Yongcheol; Park, Tae-Yoon S.; Hong, Jong Kuk; Hashim, Mazlan; Woo, Jusun; Ayoobi, Iman

    2018-06-01

    Satellite remote sensing imagery is especially useful for geological investigations in Antarctica because of its remoteness and extreme environmental conditions that constrain direct geological survey. The highest percentage of exposed rocks and soils in Antarctica occurs in Northern Victoria Land (NVL). Exposed Rocks in NVL were part of the paleo-Pacific margin of East Gondwana during the Paleozoic time. This investigation provides a satellite-based remote sensing approach for regional geological mapping in the NVL, Antarctica. Landsat-8 and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) datasets were used to extract lithological-structural and mineralogical information. Several spectral-band ratio indices were developed using Landsat-8 and ASTER bands and proposed for Antarctic environments to map spectral signatures of snow/ice, iron oxide/hydroxide minerals, Al-OH-bearing and Fe, Mg-OH and CO3 mineral zones, and quartz-rich felsic and mafic-to-ultramafic lithological units. The spectral-band ratio indices were tested and implemented to Level 1 terrain-corrected (L1T) products of Landsat-8 and ASTER datasets covering the NVL. The surface distribution of the mineral assemblages was mapped using the spectral-band ratio indices and verified by geological expeditions and laboratory analysis. Resultant image maps derived from spectral-band ratio indices that developed in this study are fairly accurate and correspond well with existing geological maps of the NVL. The spectral-band ratio indices developed in this study are especially useful for geological investigations in inaccessible locations and poorly exposed lithological units in Antarctica environments.

  1. Long-term change analysis of satellite-based evapotranspiration over Indian vegetated surface

    Science.gov (United States)

    Gupta, Shweta; Bhattacharya, Bimal K.; Krishna, Akhouri P.

    2016-05-01

    In the present study, trend of satellite based annual evapotranspiration (ET) and natural forcing factors responsible for this were analyzed. Thirty years (1981-2010) of ET data at 0.08° grid resolution, generated over Indian region from opticalthermal observations from NOAA PAL and MODIS AQUA satellites, were used. Long-term data on gridded (0.5° x 0.5°) annual rainfall (RF), annual mean surface soil moisture (SSM) ERS scatterometer at 25 km resolution and annual mean incoming shortwave radiation from MERRA-2D reanalysis were also analyzed. Mann-Kendall tests were performed with time series data for trend analysis. Mean annual ET loss from Indian ago-ecosystem was found to be almost double (1100 Cubic Km) than Indian forest ecosystem (550 Cubic Km). Rainfed vegetation systems such as forest, rainfed cropland, grassland showed declining ET trend @ - 4.8, -0.6 &-0.4 Cubic Kmyr-1, respectively during 30 years. Irrigated cropland initially showed ET decline upto 1995 @ -0.8 cubic Kmyr-1 which could possibly be due to solar dimming followed by increasing ET @ 0.9 cubic Kmyr-1 after 1995. A cross-over point was detected between forest ET decline and ET increase in irrigated cropland during 2008. During 2001-2010, the four agriculturally important Indian states eastern, central, western and southern showed significantly increasing ET trend with S-score of 15-25 and Z-score of 1.09-2.9. Increasing ET in western and southern states was found to be coupled with increase in annual rainfall and SSM. But in eastern and central states no significant trend in rainfall was observed though significant increase in ET was noticed. The study recommended to investigate the influence of anthropogenic factors such as increase in area under irrigation, increased use of water for irrigation through ground water pumping, change in cropping pattern and cultivars on increasing ET.

  2. Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes

    Science.gov (United States)

    Williams, Jack G.; Rosser, Nick J.; Kincey, Mark E.; Benjamin, Jessica; Oven, Katie J.; Densmore, Alexander L.; Milledge, David G.; Robinson, Tom R.; Jordan, Colm A.; Dijkstra, Tom A.

    2018-01-01

    Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 Mw 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response.

  3. Identification and Quantification of Uncertainties Related to Using Distributed X-band Radar Estimated Precipitation as input in Urban Drainage Models

    DEFF Research Database (Denmark)

    Pedersen, Lisbeth

    The Local Area Weather Radar (LAWR) is a small scale weather radar providing distributed measurements of rainfall primarily for use as input in hydrological applications. As any other weather radar the LAWR measurement of the rainfall is an indirect measurement since it does not measure the rainf......The Local Area Weather Radar (LAWR) is a small scale weather radar providing distributed measurements of rainfall primarily for use as input in hydrological applications. As any other weather radar the LAWR measurement of the rainfall is an indirect measurement since it does not measure...... are quantified using statistical methods. Furthermore, the present calibration method is reviewed and a new extended calibration method has been developed and tested resulting in improved rainfall estimates. As part of the calibration analysis a number of elements affecting the LAWR performance were identified...... in connection with boundary assignment besides general improved understanding of the benefits and pitfalls in using distributed rainfall data as input to models. In connection with the use of LAWR data in urban drainage context, the potential for using LAWR data for extreme rainfall statistics has been studied...

  4. Development of a daily gridded precipitation data set for the Middle East

    Directory of Open Access Journals (Sweden)

    A. Yatagai

    2008-03-01

    Full Text Available We show an algorithm to construct a rain-gauge-based analysis of daily precipitation for the Middle East. One of the key points of our algorithm is to construct an accurate distribution of climatology. One possible advantage of this product is to validate high-resolution climate models and/or to diagnose the impact of climate changes on local hydrological resources. Many users are familiar with a monthly precipitation dataset (New et al., 1999 and a satellite-based daily precipitation dataset (Huffman et al., 2001, yet our data set, unlike theirs, clearly shows the effect of orography on daily precipitation and other extreme events, especially over the Fertile Crescent region. Currently the Middle-East precipitation analysis product is consisting of a 25-year data set for 1979–2003 based on more than 1300 stations.

  5. Estimating basin scale evapotranspiration (ET) by water balance and remote sensing methods

    Science.gov (United States)

    Senay, G.B.; Leake, S.; Nagler, P.L.; Artan, G.; Dickinson, J.; Cordova, J.T.; Glenn, E.P.

    2011-01-01

    Evapotranspiration (ET) is an important hydrological process that can be studied and estimated at multiple spatial scales ranging from a leaf to a river basin. We present a review of methods in estimating basin scale ET and its applications in understanding basin water balance dynamics. The review focuses on two aspects of ET: (i) how the basin scale water balance approach is used to estimate ET; and (ii) how ‘direct’ measurement and modelling approaches are used to estimate basin scale ET. Obviously, the basin water balance-based ET requires the availability of good precipitation and discharge data to calculate ET as a residual on longer time scales (annual) where net storage changes are assumed to be negligible. ET estimated from such a basin water balance principle is generally used for validating the performance of ET models. On the other hand, many of the direct estimation methods involve the use of remotely sensed data to estimate spatially explicit ET and use basin-wide averaging to estimate basin scale ET. The direct methods can be grouped into soil moisture balance modelling, satellite-based vegetation index methods, and methods based on satellite land surface temperature measurements that convert potential ET into actual ET using a proportionality relationship. The review also includes the use of complementary ET estimation principles for large area applications. The review identifies the need to compare and evaluate the different ET approaches using standard data sets in basins covering different hydro-climatic regions of the world.

  6. Modeling rain-fed maize vulnerability to droughts using the standardized precipitation index from satellite estimated rainfall—Southern Malawi case study

    Science.gov (United States)

    Funk, Christopher C.; Verdin, James; Adams Chavula,; Gregory J. Husak,; Harikishan Jayanthi,; Tamuka Magadzire,

    2013-01-01

    During 1990s, disaster risk reduction emerged as a novel, proactive approach to managing risks from natural hazards. The World Bank, USAID, and other international donor agencies began making efforts to mainstream disaster risk reduction in countries whose population and economies were heavily dependent on rain-fed agriculture. This approach has more significance in light of the increasing climatic hazard patterns and the climate scenarios projected for different hazard prone countries in the world. The Famine Early Warning System Network (FEWS NET) has been monitoring the food security issues in the sub-Saharan Africa, Asia and in Haiti. FEWS NET monitors the rainfall and moisture availability conditions with the help of NOAA RFE2 data for deriving food security status in Africa. This paper highlights the efforts in using satellite estimated rainfall inputs to develop drought vulnerability models in the drought prone areas in Malawi. The satellite RFE2 based SPI corresponding to the critical tasseling and silking phases (in the months of January, February, and March) were statistically regressed with drought-induced yield losses at the district level. The analysis has shown that the drought conditions in February and early March lead to most damage to maize yields in this region. The district-wise vulnerabilities to drought were upscaled to obtain a regional maize vulnerability model for southern Malawi. The results would help in establishing an early monitoring mechanism for drought impact assessment, give the decision makers additional time to assess seasonal outcomes, and identify potential food-related hazards in Malawi.

  7. Precipitation-induced runoff and leaching from milled peat mining mires by peat types: A comparative method for estimating the loading of water bodies during peat production

    Energy Technology Data Exchange (ETDEWEB)

    Svahnbaeck, L.

    2007-07-01

    Finland has some 10 million hectares of peatland, accounting for almost a third of its total area. Macroclimatic conditions have varied in the course of the Holocene growth and development of this peatland, and with them the habitats of the peat-forming plants. Temperatures and moisture conditions have played a significant role in determining the dominant species of mire plants growing there at any particular time, the resulting mire types and the accumulation and deposition of plant remains to form the peat. While in a natural state the mires of Finland have functioned as carbon dioxide sinks throughout the post-glacial period, but the ditching of peatland for forestry and agriculture, amounting to some 5,7 million hectares in Finland, has affected their water balance, especially over the last hundred years, and has thereby altered the quantity and species composition of the mire vegetation. The invasion of trees and woody plants to replace the typical mire plants following ditching for forestry purposes has stimulated the decomposition of the already accumulated peat and promoted the humification of the microbiologically active root system layer. The above climatic, environmental and mire development factors, together with ditching, have contributed, and continue to contribute, to the existence of peat horizons that differ in their physical and chemical properties, leading to differences in material transport between peatlands in a natural state and mires that have been ditched or prepared for forestry and peat production. Watercourse loading from the ditching of mires or their use for peat production can have detrimental effects on river and lake environments and their recreational use, especially where oxygen-consuming organic solids and soluble organic substances and nutrients are concerned. It has not previously been possible, however, to estimate in advance the watercourse loading likely to arise from ditching and peat production on the basis of the

  8. Precipitation Indices Low Countries

    Science.gov (United States)

    van Engelen, A. F. V.; Ynsen, F.; Buisman, J.; van der Schrier, G.

    2009-09-01

    has not been below (above) the mean precipitation minus (plus) three standard deviations for the corresponding season, an accumulated precipitation amount which relates to each of the five drought classes in the classification can be estimated. (1) Buisman, J. , Van Engelen, A.F.V. (editor), Duizend jaar weer wind en water in de Lage Landen, Van Wijnen, Franeker (Netherlands), Vol. I763-1300, 1995, Vol. II, 1300-1450, 1996, Vol. III, 1450-1575, 1998, Vol. IV, 1575-1675, 2000, Vol. V, 1675-1750, 2006. (2) Shabalova, M.V., Van Engelen, A.F.V., Evaluation of a reconstruction of winter and summer temperatures in the Low Countries, AD 764-1998, Climatic Change 58: 219-242, 2003

  9. Global precipitations and climate change. Proceedings

    International Nuclear Information System (INIS)

    Desbois, M.; Desalmand, F.

    1994-01-01

    The workshop reviewed the present status of knowledge concerning the past and present evolution of the distribution of precipitations at global scale, related to climate evolution at different time scales. This review was intended to assess the availability and quality of data which could help, through validation and initialization of model studies, to improve our understanding of the processes determining these precipitation changes. On another hand, the modelling specialists presented their actual use of precipitation data. Exchanges of views between the modelling and observing communities were thus made possible, leading to a set of recommendations for future studies. Sessions were then devoted to specific themes: 1) Paleoclimatology, 2) data collection, history and statistics, programmes, 3) methodologies and accuracy of large scale estimation of precipitation from conventional data, 4) estimation of precipitation from satellite data, 5) modelling studies. (orig.)

  10. Modelled Precipitation Over Greenland

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set includes the annual total precipitation from 1985 to 1999 and monthly total precipitation from January 1985 to December 1999. The data is derived from...

  11. Advanced Oil Spill Detection Algorithms For Satellite Based Maritime Environment Monitoring

    Science.gov (United States)

    Radius, Andrea; Azevedo, Rui; Sapage, Tania; Carmo, Paulo

    2013-12-01

    During the last years, the increasing pollution occurrence and the alarming deterioration of the environmental health conditions of the sea, lead to the need of global monitoring capabilities, namely for marine environment management in terms of oil spill detection and indication of the suspected polluter. The sensitivity of Synthetic Aperture Radar (SAR) to the different phenomena on the sea, especially for oil spill and vessel detection, makes it a key instrument for global pollution monitoring. The SAR performances in maritime pollution monitoring are being operationally explored by a set of service providers on behalf of the European Maritime Safety Agency (EMSA), which has launched in 2007 the CleanSeaNet (CSN) project - a pan-European satellite based oil monitoring service. EDISOFT, which is from the beginning a service provider for CSN, is continuously investing in R&D activities that will ultimately lead to better algorithms and better performance on oil spill detection from SAR imagery. This strategy is being pursued through EDISOFT participation in the FP7 EC Sea-U project and in the Automatic Oil Spill Detection (AOSD) ESA project. The Sea-U project has the aim to improve the current state of oil spill detection algorithms, through the informative content maximization obtained with data fusion, the exploitation of different type of data/ sensors and the development of advanced image processing, segmentation and classification techniques. The AOSD project is closely related to the operational segment, because it is focused on the automation of the oil spill detection processing chain, integrating auxiliary data, like wind information, together with image and geometry analysis techniques. The synergy between these different objectives (R&D versus operational) allowed EDISOFT to develop oil spill detection software, that combines the operational automatic aspect, obtained through dedicated integration of the processing chain in the existing open source NEST

  12. Utility and Value of Satellite-Based Frost Forecasting for Kenya's Tea Farming Sector

    Science.gov (United States)

    Morrison, I.

    2016-12-01

    Frost damage regularly inflicts millions of dollars of crop losses in the tea-growing highlands of western Kenya, a problem that the USAID/NASA Regional Visualization and Monitoring System (SERVIR) program is working to mitigate through a frost monitoring and forecasting product that uses satellite-based temperature and soil moisture data to generate up to three days of advanced warning before frost events. This paper presents the findings of a value of information (VOI) study assessing the value of this product based on Kenyan tea farmers' experiences with frost and frost-damage mitigation. Value was calculated based on historic trends of frost frequency, severity, and extent; likelihood of warning receipt and response; and subsequent frost-related crop-loss aversion. Quantification of these factors was derived through inferential analysis of survey data from 400 tea-farming households across the tea-growing regions of Kericho and Nandi, supplemented with key informant interviews with decision-makers at large estate tea plantations, historical frost incident and crop-loss data from estate tea plantations and agricultural insurance companies, and publicly available demographic and economic data. At this time, the product provides a forecasting window of up to three days, and no other frost-prediction methods are used by the large or small-scale farmers of Kenya's tea sector. This represents a significant opportunity for preemptive loss-reduction via Earth observation data. However, the tea-growing community has only two realistic options for frost-damage mitigation: preemptive harvest of available tea leaves to minimize losses, or skiving (light pruning) to facilitate fast recovery from frost damage. Both options are labor-intensive and require a minimum of three days of warning to be viable. As a result, the frost forecasting system has a very narrow margin of usefulness, making its value highly dependent on rapid access to the warning messages and flexible access

  13. A Space-Based Perspective of the 2017 Hurricane Season from the Global Precipitation Measurement (GPM) Mission

    Science.gov (United States)

    Skofronick Jackson, G.; Petersen, W. A.; Huffman, G. J.; Kirschbaum, D.; Wolff, D. B.; Tan, J.; Zavodsky, B.

    2017-12-01

    The Global Precipitation Measurement (GPM) mission collected unique, near real time 3-D satellite-based views of hurricanes in 2017 together with estimated precipitation accumulation using merged satellite data for scientific studies and societal applications. Central to GPM is the NASA-JAXA GPM Core Observatory (CO). The GPM-CO carries an advanced dual-frequency precipitation radar (DPR) and a well-calibrated, multi-frequency passive microwave radiometer that together serve as an on orbit reference for precipitation measurements made by the international GPM satellite constellation. GPM-CO overpasses of major Hurricanes such as Harvey, Irma, Maria, and Ophelia revealed intense convective structures in DPR radar reflectivity together with deep ice-phase microphysics in both the eyewalls and outer rain bands. Of considerable scientific interest, and yet to be determined, will be DPR-diagnosed characteristics of the rain drop size distribution as a function of convective structure, intensity and microphysics. The GPM-CO active/passive suite also provided important decision support information. For example, the National Hurricane Center used GPM-CO observations as a tool to inform track and intensity estimates in their forecast briefings. Near-real-time rainfall accumulation from the Integrated Multi-satellitE Retrievals for GPM (IMERG) was also provided via the NASA SPoRT team to Puerto Rico following Hurricane Maria when ground-based radar systems on the island failed. Comparisons between IMERG, NOAA Multi-Radar Multi-Sensor data, and rain gauge rainfall accumulations near Houston, Texas during Hurricane Harvey revealed spatial biases between ground and IMERG satellite estimates, and a general underestimation of IMERG rain accumulations associated with infrared observations, collectively illustrating the difficulty of measuring rainfall in hurricanes.GPM data continue to advance scientific research on tropical cyclone intensification and structure, and contribute to

  14. Implications of a decrease in the precipitation area for the past and the future

    Science.gov (United States)

    Benestad, Rasmus E.

    2018-04-01

    The total area with 24 hrs precipitation has shrunk by 7% between 50°S–50°N over the period 1998–2016, according to the satellite-based Tropical Rain Measurement Mission data. A decrease in the daily precipitation area is an indication of profound changes in the hydrological cycle, where the global rate of precipitation is balanced by the global rate of evaporation. This decrease was accompanied by increases in total precipitation, evaporation, and wet-day mean precipitation. If these trends are real, then they suggest increased drought frequencies and more intense rainfall. Satellite records, however, may be inhomogeneous because they are synthesised from a number of individual missions with improved technology over time. A linear dependency was also found between the global mean temperature and the 50°S–50°N daily precipitation area with a slope value of ‑17 × 106 km 2/°C. This dependency was used with climate model simulations to make future projections which suggested a continued decrease that will strengthen in the future. The precipitation area evolves differently when the precipitation is accumulated over short and long time scales, however, and there has been a slight increase in the monthly precipitation area while the daily precipitation area decreased. An increase on monthly scale may indicate more pronounced variations in the rainfall patterns due to migrating rain-producing phenomena.

  15. Acid precipitation literature review

    Energy Technology Data Exchange (ETDEWEB)

    Seip, H M; Andersen, B; Andersson, G; Hov, Oe; Kucera, V; Moseholm, L

    1986-01-01

    There is an increasing number of publications on acid deposition and related phenomena. Interest in these topics has also been reflected in a considerable number of meetings and conferences in this field. The largest of these in 1985 was the ''International Symposium on Acidic Precipitation'' (Muskoka, Ontario). Most work so far has been carried out in North America and Europe. There is, however, an increasing interest in obtaining a better picture of sensitive areas and possible acidification in other parts of the world. Anthropogenic SO/sub 2/ emissions have been estimated to be (in TgSyr/sup -1/): 2.4 (Africa), 4.1 (South America), 0.7 (Ocenia), and 18.3 (Asia). The largest increase during the last decade has been in Asia. Based on Studies of precipitation in remote areas it has been suggested that the natural background concentration for sulphate in many areas should be about 6 ..mu..eq 1/sup -1/. A new study of sulphate and nitrate in Greenland snow showed that both ions increased by a factor of about 2 from 1895 to 1978. The concentrations of SO/sub 2/ at Norwegian rural sites show a decreasing trend since late 1970s, while concentrations of sulphate in air show no clear trend. More reliable models for transformation, transport and deposition of chemicals are being developed, including three-dimensional grid models to describe episodes of elevated pollution levels lasting for a few days. Model calculations indicate that control of hydrocarbon (HC) emissions is much more efficient in reducing the ozone level in southern Scandinavia in episodes influenced by long-range transported pollutants than NO/sub x/ control of combined NO/sub x/ and HC control. 36 refs. (EG).

  16. The Added Utility of Hydrological Model and Satellite Based Datasets in Agricultural Drought Analysis over Turkey

    Science.gov (United States)

    Bulut, B.; Hüsami Afşar, M.; Yilmaz, M. T.

    2017-12-01

    Analysis of agricultural drought, which causes substantial socioeconomically costs in Turkey and in the world, is critical in terms of understanding this natural disaster's characteristics (intensity, duration, influence area) and research on possible precautions. Soil moisture is one of the most important parameters which is used to observe agricultural drought, can be obtained using different methods. The most common, consistent and reliable soil moisture datasets used for large scale analysis are obtained from hydrologic models and remote sensing retrievals. On the other hand, Normalized difference vegetation index (NDVI) and gauge based precipitation observations are also commonly used for drought analysis. In this study, soil moisture products obtained from different platforms, NDVI and precipitation datasets over several different agricultural regions under various climate conditions in Turkey are obtained in growth season period. These datasets are later used to investigate agricultural drought by the help of annual crop yield data of selected agricultural lands. The type of vegetation over these regions are obtained using CORINE Land Cover (CLC 2012) data. The crop yield data were taken from the record of related district's statistics which is provided by Turkish Statistical Institute (TÜİK). This project is supported by TÜBİTAK project number 114Y676.

  17. The tritium content of precipitation and groundwater at Yola, Nigeria ...

    African Journals Online (AJOL)

    Tritium is a radioactive isotope of hydrogen which occurs in precipitation. In groundwater studies tritium measurements give information on the time of recharge to the system; the tritium content of precipitation being used to estimate the input of tritium to the groundwater system. At Yola, the tritium ontents in precipitation and ...

  18. Simulation of large-scale soil water systems using groundwater data and satellite based soil moisture

    Science.gov (United States)

    Kreye, Phillip; Meon, Günter

    2016-04-01

    Complex concepts for the physically correct depiction of dominant processes in the hydrosphere are increasingly at the forefront of hydrological modelling. Many scientific issues in hydrological modelling demand for additional system variables besides a simulation of runoff only, such as groundwater recharge or soil moisture conditions. Models that include soil water simulations are either very simplified or require a high number of parameters. Against this backdrop there is a heightened demand of observations to be used to calibrate the model. A reasonable integration of groundwater data or remote sensing data in calibration procedures as well as the identifiability of physically plausible sets of parameters is subject to research in the field of hydrology. Since this data is often combined with conceptual models, the given interfaces are not suitable for such demands. Furthermore, the application of automated optimisation procedures is generally associated with conceptual models, whose (fast) computing times allow many iterations of the optimisation in an acceptable time frame. One of the main aims of this study is to reduce the discrepancy between scientific and practical applications in the field of hydrological modelling. Therefore, the soil model DYVESOM (DYnamic VEgetation SOil Model) was developed as one of the primary components of the hydrological modelling system PANTA RHEI. DYVESOMs structure provides the required interfaces for the calibrations made at runoff, satellite based soil moisture and groundwater level. The model considers spatial and temporal differentiated feedback of the development of the vegetation on the soil system. In addition, small scale heterogeneities of soil properties (subgrid-variability) are parameterized by variation of van Genuchten parameters depending on distribution functions. Different sets of parameters are operated simultaneously while interacting with each other. The developed soil model is innovative regarding concept

  19. Goddard Satellite-Based Surface Turbulent Fluxes, 0.25x0.25 deg, Daily Grid, V3, (GSSTF_F14) V3

    Data.gov (United States)

    National Aeronautics and Space Administration — These data are part of the Goddard Satellite-based Surface Turbulent Fluxes Version 3 (GSSTF3) Dataset recently produced through a MEaSURES funded project led by Dr....

  20. Advancing satellite-based solar power forecasting through integration of infrared channels for automatic detection of coastal marine inversion layer

    Energy Technology Data Exchange (ETDEWEB)

    Kostylev, Vladimir; Kostylev, Andrey; Carter, Chris; Mahoney, Chad; Pavlovski, Alexandre; Daye, Tony [Green Power Labs Inc., Dartmouth, NS (Canada); Cormier, Dallas Eugene; Fotland, Lena [San Diego Gas and Electric Co., San Diego, CA (United States)

    2012-07-01

    The marine atmospheric boundary layer is a layer or cool, moist maritime air with the thickness of a few thousand feet immediately below a temperature inversion. In coastal areas as moist air rises from the ocean surface, it becomes trapped and is often compressed into fog above which a layer of stratus clouds often forms. This phenomenon is common for satellite-based solar radiation monitoring and forecasting. Hour ahead satellite-based solar radiation forecasts are commonly using visible spectrum satellite images, from which it is difficult to automatically differentiate low stratus clouds and fog from high altitude clouds. This provides a challenge for cloud motion tyracking and cloud cover forecasting. San Diego Gas and Electric {sup registered} (SDG and E {sup registered}) Marine Layer Project was undertaken to obtain information for integration with PV forecasts, and to develop a detailed understanding of long-term benefits from forecasting Marine Layer (ML) events and their effects on PV production. In order to establish climatological ML patterns, spatial extent and distribution of marine layer, we analyzed visible and IR spectrum satellite images (GOES WEST) archive for the period of eleven years (2000 - 2010). Historical boundaries of marine layers impact were established based on the cross-classification of visible spectrum (VIS) and infrared (IR) images. This approach is successfully used by us and elsewhere for evaluating cloud albedo in common satellite-based techniques for solar radiation monitoring and forecasting. The approach allows differentiation of cloud cover and helps distinguish low laying fog which is the main consequence of marine layer formation. ML occurrence probability and maximum extent inland was established for each hour and day of the analyzed period and seasonal/patterns were described. SDG and E service area is the most affected region by ML events with highest extent and probability of ML occurrence. Influence of ML was the

  1. Role of physical forcings and nutrient availability on the control of satellite-based chlorophyll a concentration in the coastal upwelling area of the Sicilian Channel

    Directory of Open Access Journals (Sweden)

    Bernardo Patti

    2010-08-01

    Full Text Available The northern sector of the Sicilian Channel is an area of favourable upwelling winds, which ought to support primary production. However, the values for primary production are low when compared with other Mediterranean areas and very low compared with the most biologically productive regions of the world’s oceans: California, the Canary Islands, Humboldt and Benguela. The aim of this study was to identify the main factors that limit phytoplankton biomass in the Sicilian Channel and modulate its monthly changes. We compared satellite-based estimates of chlorophyll a concentration in the Strait of Sicily with those observed in the four Eastern Boundary Upwelling Systems mentioned above and in other Mediterranean wind-induced coastal upwelling systems (the Alboran Sea, the Gulf of Lions and the Aegean Sea. Our results show that this low level of chlorophyll is mainly due to the low nutrient level in surface and sub-surface waters, independently of wind-induced upwelling intensity. Further, monthly changes in chlorophyll are mainly driven by the mixing of water column and wind-induced and/or circulation-related upwelling processes. Finally, primary production limitation due to the enhanced stratification processes resulting from the general warming trend of Mediterranean waters is not active over most of the coastal upwelling area off the southern Sicilian coast.

  2. A change detection strategy for monitoring vegetative and land-use cover types using remotely-sensed, satellite-based data

    International Nuclear Information System (INIS)

    Hallum, C.

    1993-01-01

    Changes to the environment are of critical concern in the world today; consequently, monitoring such changes and assessing their impacts are tasks demanding considerably higher priority. The ecological impacts of the natural global cycles of gases and particulates in the earth's atmosphere are highly influenced by the extent of changes to vegetative canopy characteristics which dictates the need for capability to detect and assess the magnitude of such changes. The primary emphasis of this paper is on the determination of the size and configuration of the sampling unit that maximizes the probability of its intersection with a 'change' area. Assessment of the significance of the 'change' in a given locality is also addressed and relies on a statistical approach that compares the number of elemental units exceeding a reflectance threshold when compared to a previous point in time. Consideration is also given to a technical framework that supports quantifying the magnitude of the 'change' over large areas (i.e., the estimated area changing from forest to agricultural land-use). The latter entails a multistage approach which utilizes satellite-based and other related data sources

  3. Extension of the TAMSAT satellite-based rainfall monitoring over Africa and from 1983 to present

    OpenAIRE

    Tarnavsky, Elena; Grimes, David; Maidment, Ross; Black, Emily; Allan, Richard; Stringer, Marc; Chadwick, Robin; Kayitakire, Francois

    2014-01-01

    Tropical Applications of Meteorology Using Satellite Data and Ground-Based Observations (TAMSAT) rainfall monitoring products have been extended to provide spatially contiguous rainfall estimates across Africa. This has been achieved through a new, climatology-based calibration, which varies in both space and time. As a result, cumulative estimates of rainfall are now issued at the end of each 10-day period (dekad) at 4-km spatial resolution with pan-African coverage. The utility of the produ...

  4. Cerium oxalate precipitation

    International Nuclear Information System (INIS)

    Chang, T.P.

    1987-02-01

    Cerium, a nonradioactive, common stand-in for plutonium in development work, has been used to simulate several plutonium precipitation processes at the Savannah River Laboratory. There are similarities between the plutonium trifluoride and the cerium oxalate precipitations in particle size and extent of plating, but not particle morphology. The equilibrium solubility, precipitation kinetics, particle size, extent of plating, and dissolution characteristics of cerium oxalate have been investigated. Interpretations of particle size and plating based on precipitation kinetics (i.e., nucleation and crystal growth) are presented. 16 refs., 7 figs., 6 tabs

  5. Risk assessment of precipitation extremes in northern Xinjiang, China

    Science.gov (United States)

    Yang, Jun; Pei, Ying; Zhang, Yanwei; Ge, Quansheng

    2018-05-01

    This study was conducted using daily precipitation records gathered at 37 meteorological stations in northern Xinjiang, China, from 1961 to 2010. We used the extreme value theory model, generalized extreme value (GEV) and generalized Pareto distribution (GPD), statistical distribution function to fit outputs of precipitation extremes with different return periods to estimate risks of precipitation extremes and diagnose aridity-humidity environmental variation and corresponding spatial patterns in northern Xinjiang. Spatiotemporal patterns of daily maximum precipitation showed that aridity-humidity conditions of northern Xinjiang could be well represented by the return periods of the precipitation data. Indices of daily maximum precipitation were effective in the prediction of floods in the study area. By analyzing future projections of daily maximum precipitation (2, 5, 10, 30, 50, and 100 years), we conclude that the flood risk will gradually increase in northern Xinjiang. GEV extreme value modeling yielded the best results, proving to be extremely valuable. Through example analysis for extreme precipitation models, the GEV statistical model was superior in terms of favorable analog extreme precipitation. The GPD model calculation results reflect annual precipitation. For most of the estimated sites' 2 and 5-year T for precipitation levels, GPD results were slightly greater than GEV results. The study found that extreme precipitation reaching a certain limit value level will cause a flood disaster. Therefore, predicting future extreme precipitation may aid warnings of flood disaster. A suitable policy concerning effective water resource management is thus urgently required.

  6. Strategies for satellite-based monitoring of CO2 from distributed area and point sources

    Science.gov (United States)

    Schwandner, Florian M.; Miller, Charles E.; Duren, Riley M.; Natraj, Vijay; Eldering, Annmarie; Gunson, Michael R.; Crisp, David

    2014-05-01

    and sensor provides the full range of temporal sampling needed to characterize distributed area and point source emissions. For instance, point source emission patterns will vary with source strength, wind speed and direction. Because wind speed, direction and other environmental factors change rapidly, short term variabilities should be sampled. For detailed target selection and pointing verification, important lessons have already been learned and strategies devised during JAXA's GOSAT mission (Schwandner et al, 2013). The fact that competing spatial and temporal requirements drive satellite remote sensing sampling strategies dictates a systematic, multi-factor consideration of potential solutions. Factors to consider include vista, revisit frequency, integration times, spatial resolution, and spatial coverage. No single satellite-based remote sensing solution can address this problem for all scales. It is therefore of paramount importance for the international community to develop and maintain a constellation of atmospheric CO2 monitoring satellites that complement each other in their temporal and spatial observation capabilities: Polar sun-synchronous orbits (fixed local solar time, no diurnal information) with agile pointing allow global sampling of known distributed area and point sources like megacities, power plants and volcanoes with daily to weekly temporal revisits and moderate to high spatial resolution. Extensive targeting of distributed area and point sources comes at the expense of reduced mapping or spatial coverage, and the important contextual information that comes with large-scale contiguous spatial sampling. Polar sun-synchronous orbits with push-broom swath-mapping but limited pointing agility may allow mapping of individual source plumes and their spatial variability, but will depend on fortuitous environmental conditions during the observing period. These solutions typically have longer times between revisits, limiting their ability to resolve

  7. Understanding the Seasonal Greenness Trends and Controls in South Asia Using Satellite Based Observations

    Science.gov (United States)

    Sarmah, S.; Jia, G.; Zhang, A.; Singha, M.

    2017-12-01

    South Asia (SA) is one of the most remarkable regions in changing vegetation greenness along with its major expansion of agricultural activity, especially irrigated farming. However, SA is predicted to be a vulnerable agricultural regions to future climate changes. The influence of monsoon climate on the seasonal trends and anomalies of vegetation greenness are not well understood in the region which can provide valuable information about climate-ecosystem interaction. This study analyzed the spatio-temporal patterns of seasonal vegetation trends and variability using satellite vegetation indices (VI) including AVHRR Normalized Difference Vegetation Index (NDVI) (1982-2013) and MODIS Enhanced Vegetation Index (EVI) (2000-2013) in summer monsoon (SM) (June-Sept) and winter monsoon (WM) (Dec-Apr) seasons among irrigated cropland (IC), rainfed cropland (RC) and natural vegetation (NV). Seasonal VI variations with climatic factors (precipitation and temperature) and LULC changes have been investigated to identify the forcings behind the vegetation trends and variability. We found that major greening occurred in the last three decades due to the increase in IC productivity noticeably in WM, however, recent (2000-2013) greening trends were lower than the previous decades (1982-1999) in both the IC and RC indicating the stresses on them. The browning trends, mainly concentrated in NV areas were prominent during WM and rigorous since 2000, confirmed from the moderate resolution EVI and LULC datasets. Winter time maximal temperature had been increasing tremendously whereas precipitation trend was not significant over SA. Both the climate variability and LULC changes had integrated effects on the vegetation changes in NV areas specifically in hilly regions. However, LULC impact was intensified since 2000, mostly in north east India. This study also revealed a distinct seasonal variation in spatial distribution of correlation between VI's and climate anomalies over SA

  8. Satellite-based Monotoring of mitiple natural disasters in Mongolian socio-ecological system

    Science.gov (United States)

    Kang, Sinkyu

    2016-04-01

    In this presentation, a conceptual mechanisms how multiple natural hazards (i.e. drought, dust storm, land degradation, and Dzud) in Mongolia are linked with each other and how satellite earth observation (EO) data can be utilized to analyze cause-and results relations and to predict the natural hazards. Massive loss of livestock and wildlife animal during winter seasons (dzud) is an endemic climatic disaster in the Central Asia grasslands but the mechanisms are not well understood yet. Recent national-wide sever Dzud occurred during 2009-2010 winter in Mongolia. Whereas, high stocking rate of livestock may give negative effects on sustainable use of pastureland. Dzud is a natural mechanism reducing grazing pressure when stocking rate is high enough to cause the negative effect. Both Dzud and land degradation were directly linked with drought phenomena, which is associated with dust storm occurrence because those conditions can cause sparse vegetation and increase of sensible heat generating strong vertical wind. At a lower level of administration (i.e., soum), stepwise multiple regression analysis was conducted to find significant factors of inter-annual livestock change. For a period from 2003 to 2010, various datasets were prepared from national census and satellite data (summer and winter temperature and precipitation, and summer dryness and vegetation index, NDVI). As results, linear regression models were successfully produced at 70% of soums studied. Summer and winter variables appeared equally important in controlling livestock dynamics. Single-factor models were predominant. The primary factor of each soum showed certain regional patterns incident well with climate severity and foraging resource availability (e.g. temperature in north, dryness in south, and NDVI in middle). Our results indicate that Mongolian pastoral livelihood is highly vulnerable to extreme variability of endemic regional climate factors and hence, there are still rooms for enhancing

  9. Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi

    Directory of Open Access Journals (Sweden)

    D. Kneis

    2014-07-01

    Full Text Available The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India. The considered data sets known as 3B42 and 3B42-RT (version 7/7A are routinely produced by the tropical rainfall measuring mission (TRMM from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2 the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT. Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate 80 mm day-1. At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6. In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at

  10. GLOBALLY INCREASED CROP GROWTH AND CROPPING INTENSITY FROM THE LONG-TERM SATELLITE-BASED OBSERVATIONS

    Directory of Open Access Journals (Sweden)

    B. Chen

    2018-04-01

    Full Text Available Understanding the spatiotemporal change trend of global crop growth and multiple cropping system under climate change scenarios is a critical requirement for supporting the food security issue that maintains the function of human society. Many studies have predicted the effects of climate changes on crop production using a combination of filed studies and models, but there has been limited evidence relating decadal-scale climate change to global crop growth and the spatiotemporal distribution of multiple cropping system. Using long-term satellite-derived Normalized Difference Vegetation Index (NDVI and observed climate data from 1982 to 2012, we investigated the crop growth trend, spatiotemporal pattern trend of agricultural cropping intensity, and their potential correlations with respect to the climate change drivers at a global scale. Results show that 82.97 % of global cropland maximum NDVI witnesses an increased trend while 17.03 % of that shows a decreased trend over the past three decades. The spatial distribution of multiple cropping system is observed to expand from lower latitude to higher latitude, and the increased cropping intensity is also witnessed globally. In terms of regional major crop zones, results show that all nine selected zones have an obvious upward trend of crop maximum NDVI (p < 0.001, and as for climatic drivers, the gradual temperature and precipitation changes have had a measurable impact on the crop growth trend.

  11. Satellite-based Flood Modeling Using TRMM-based Rainfall Products

    Directory of Open Access Journals (Sweden)

    Greg Easson

    2007-12-01

    Full Text Available Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25o to 0.50o andhence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scaleflood events. This study assesses the question: what are the hydrologic implications ofuncertainty of satellite rainfall data at the coarse scale? We investigated this question onthe 970 km2 Upper Cumberland river basin of Kentucky. The satellite rainfall productassessed was NASA’s Tropical Rainfall Measuring Mission (TRMM Multi-satellitePrecipitation Analysis (TMPA product called 3B41RT that is available in pseudo real timewith a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data canimprove application in flood prediction to some extent with the trade-off of more falsealarms in peak flow. However, a more rational and regime-based adjustment procedureneeds to be identified before the use of satellite data can be institutionalized among floodmodelers.

  12. PRECIPITATION OF PROTACTINIUM

    Science.gov (United States)

    Moore, R.L.

    1958-07-15

    An lmprovement in the separation of protactinium from aqueous nitric acid solutions is described. 1t covers the use of lead dioxide and tin dioxide as carrier precipitates for the protactinium. In carrying out the process, divalent lead or divalent tin is addcd to the solution and oxidized, causing formation of a carrier precipitate of lead dioxide or stannic oxide, respectively.

  13. Global Precipitation Measurement Poster

    Science.gov (United States)

    Azarbarzin, Art

    2010-01-01

    This poster presents an overview of the Global Precipitation Measurement (GPM) constellation of satellites which are designed to measure the Earth's precipitation. It includes the schedule of launches for the various satellites in the constellation, and the coverage of the constellation, It also reviews the mission capabilities, and the mission science objectives.

  14. Benchmarking LSM root-zone soil mositure predictions using satellite-based vegetation indices

    Science.gov (United States)

    The application of modern land surface models (LSMs) to agricultural drought monitoring is based on the premise that anomalies in LSM root-zone soil moisture estimates can accurately anticipate the subsequent impact of drought on vegetation productivity and health. In addition, the water and energy ...

  15. Satellite-based Calibration of Heat Flux at the Ocean Surface

    Science.gov (United States)

    Barron, C. N.; Dastugue, J. M.; May, J. C.; Rowley, C. D.; Smith, S. R.; Spence, P. L.; Gremes-Cordero, S.

    2016-02-01

    Model forecasts of upper ocean heat content and variability on diurnal to daily scales are highly dependent on estimates of heat flux through the air-sea interface. Satellite remote sensing is applied to not only inform the initial ocean state but also to mitigate errors in surface heat flux and model representations affecting the distribution of heat in the upper ocean. Traditional assimilation of sea surface temperature (SST) observations re-centers ocean models at the start of each forecast cycle. Subsequent evolution depends on estimates of surface heat fluxes and upper-ocean processes over the forecast period. The COFFEE project (Calibration of Ocean Forcing with satellite Flux Estimates) endeavors to correct ocean forecast bias through a responsive error partition among surface heat flux and ocean dynamics sources. A suite of experiments in the southern California Current demonstrates a range of COFFEE capabilities, showing the impact on forecast error relative to a baseline three-dimensional variational (3DVAR) assimilation using Navy operational global or regional atmospheric forcing. COFFEE addresses satellite-calibration of surface fluxes to estimate surface error covariances and links these to the ocean interior. Experiment cases combine different levels of flux calibration with different assimilation alternatives. The cases may use the original fluxes, apply full satellite corrections during the forecast period, or extend hindcast corrections into the forecast period. Assimilation is either baseline 3DVAR or standard strong-constraint 4DVAR, with work proceeding to add a 4DVAR expanded to include a weak constraint treatment of the surface flux errors. Covariance of flux errors is estimated from the recent time series of forecast and calibrated flux terms. While the California Current examples are shown, the approach is equally applicable to other regions. These approaches within a 3DVAR application are anticipated to be useful for global and larger

  16. Comparison of ground based indices (API and AQI) with satellite based aerosol products.

    Science.gov (United States)

    Zheng, Sheng; Cao, Chun-Xiang; Singh, Ramesh P

    2014-08-01

    Air quality in mega cities is one of the major concerns due to serious health issues and its indirect impact to the climate. Among mega cities, Beijing city is considered as one of the densely populated cities with extremely poor air quality. The meteorological parameters (wind, surface temperature, air temperature and relative humidity) control the dynamics and dispersion of air pollution. China National Environmental Monitoring Centre (CNEMC) started air pollution index (API) as of 2000 to evaluate air quality, but over the years, it was felt that the air quality is not well represented by API. Recently, the Ministry of Environmental Protection (MEP) of the People's Republic of China (PRC) started using a new index "air quality index (AQI)" from January 2013. We have compared API and AQI with three different MODIS (MODIS - Moderate Resolution Imaging SpectroRadiometer, onboard the Terra/Aqua satellites) AOD (aerosol optical depth) products for ten months, January-October, 2013. The correlation between AQI and Aqua Deep Blue AOD was found to be reasonably good as compared with API, mainly due to inclusion of PM2.5 in the calculation of AQI. In addition, for every month, the correlation coefficient between AQI and Aqua Deep Blue AOD was found to be relatively higher in the month of February to May. According to the monthly average distribution of precipitation, temperature, and PM10, the air quality in the months of June-September was better as compared to those in the months of February-May. AQI and Aqua Deep Blue AOD show highly polluted days associated with dust event, representing true air quality of Beijing. Copyright © 2013 Elsevier B.V. All rights reserved.

  17. A Comparison of Satellite Based, Modeled Derived Daily Solar Radiation Data with Observed Data for the Continental US

    Science.gov (United States)

    White, Jeffrey W.; Hoogenboom, Gerrit; Wilkens, Paul W.; Stackhouse, Paul W., Jr.; Hoell, James M.

    2010-01-01

    Many applications of simulation models and related decision support tools for agriculture and natural resource management require daily meteorological data as inputs. Availability and quality of such data, however, often constrain research and decision support activities that require use of these tools. Daily solar radiation (SRAD) data are especially problematic because the instruments require electronic integrators, accurate sensors are expensive, and calibration standards are seldom available. The Prediction Of Worldwide Energy Resources (NASA/POWER; power.larc.nasa.gov) project at the NASA Langley Research Center estimates daily solar radiation based on data that are derived from satellite observations of outgoing visible radiances and atmospheric parameters based upon satellite observations and assimilation models. The solar data are available for a global 1 degree x 1 degree coordinate grid. SRAD can also be estimated based on attenuation of extraterrestrial radiation (Q0) using daily temperature and rainfall data to estimate the optical thickness of the atmosphere. This study compares daily solar radiation data from NASA/POWER (SRADNP) with instrument readings from 295 stations (SRADOB), as well as with values that were estimated with the WGENR solar generator. WGENR was used both with daily temperature and precipitation records from the stations reporting solar data and records from the NOAA Cooperative Observer Program (COOP), thus providing two additional sources of solar data, SRADWG and SRADCO. Values of SRADNP for different grid cells consistently showed higher correlations (typically 0.85 to 0.95) with SRADOB data than did SRADWG or SRADCO for sites within the corresponding cells. Mean values of SRADOB, SRADWG and SRADNP for sites within a grid cell usually were within 1 MJm-2d-1 of each other, but NASA/POWER values averaged 1.1 MJm-2d-1 lower than SRADOB. The magnitude of this bias was greater at lower latitudes and during summer months and may be at

  18. Studying Vegetation Salinity: From the Field View to a Satellite-Based Perspective

    Directory of Open Access Journals (Sweden)

    Rachel Lugassi

    2017-02-01

    Full Text Available Salinization of irrigated lands in the semi-arid Jezreel Valley, Northern Israel results in soil-structure deterioration and crop damage. We formulated a generic rule for estimating salinity of different vegetation types by studying the relationship between Cl/Na and different spectral slopes in the visible–near infrared–shortwave infrared (VIS–NIR–SWIR spectral range using both field measurements and satellite imagery (Sentinel-2. For the field study, the slope-based model was integrated with conventional partial least squares (PLS analyses. Differences in 14 spectral ranges, indicating changes in salinity levels, were identified across the VIS–NIR–SWIR region (350–2500 nm. Next, two different models were run using PLS regression: (i using spectral slope data across these ranges; and (ii using preprocessed spectral reflectance. The best model for predicting Cl content was based on continuum removal reflectance (R2 = 0.84. Satisfactory correlations were obtained using the slope-based PLS model (R2 = 0.77 for Cl and R2 = 0.63 for Na. Thus, salinity contents in fresh plants could be estimated, despite masking of some spectral regions by water absorbance. Finally, we estimated the most sensitive spectral channels for monitoring vegetation salinity from a satellite perspective. We evaluated the recently available Sentinel-2 imagery’s ability to distinguish variability in vegetation salinity levels. The best estimate of a Sentinel-2-based vegetation salinity index was generated based on a ratio between calculated slopes: the 490–665 nm and 705–1610 nm. This index was denoted as the Sentinel-2-based vegetation salinity index (SVSI (band 4 − band 2/(band 5 + band 11.

  19. Flood forecasting and uncertainty of precipitation forecasts

    International Nuclear Information System (INIS)

    Kobold, Mira; Suselj, Kay

    2004-01-01

    The timely and accurate flood forecasting is essential for the reliable flood warning. The effectiveness of flood warning is dependent on the forecast accuracy of certain physical parameters, such as the peak magnitude of the flood, its timing, location and duration. The conceptual rainfall - runoff models enable the estimation of these parameters and lead to useful operational forecasts. The accurate rainfall is the most important input into hydrological models. The input for the rainfall can be real time rain-gauges data, or weather radar data, or meteorological forecasted precipitation. The torrential nature of streams and fast runoff are characteristic for the most of the Slovenian rivers. Extensive damage is caused almost every year- by rainstorms affecting different regions of Slovenia' The lag time between rainfall and runoff is very short for Slovenian territory and on-line data are used only for now casting. Forecasted precipitations are necessary for hydrological forecast for some days ahead. ECMWF (European Centre for Medium-Range Weather Forecasts) gives general forecast for several days ahead while more detailed precipitation data with limited area ALADIN/Sl model are available for two days ahead. There is a certain degree of uncertainty using such precipitation forecasts based on meteorological models. The variability of precipitation is very high in Slovenia and the uncertainty of ECMWF predicted precipitation is very large for Slovenian territory. ECMWF model can predict precipitation events correctly, but underestimates amount of precipitation in general The average underestimation is about 60% for Slovenian region. The predictions of limited area ALADIN/Si model up to; 48 hours ahead show greater applicability in hydrological forecasting. The hydrological models are sensitive to precipitation input. The deviation of runoff is much bigger than the rainfall deviation. Runoff to rainfall error fraction is about 1.6. If spatial and time distribution

  20. Code Tracking Algorithms for Mitigating Multipath Effects in Fading Channels for Satellite-Based Positioning

    Directory of Open Access Journals (Sweden)

    Markku Renfors

    2007-12-01

    Full Text Available The ever-increasing public interest in location and positioning services has originated a demand for higher performance global navigation satellite systems (GNSSs. In order to achieve this incremental performance, the estimation of line-of-sight (LOS delay with high accuracy is a prerequisite for all GNSSs. The delay lock loops (DLLs and their enhanced variants (i.e., feedback code tracking loops are the structures of choice for the commercial GNSS receivers, but their performance in severe multipath scenarios is still rather limited. In addition, the new satellite positioning system proposals specify the use of a new modulation, the binary offset carrier (BOC modulation, which triggers a new challenge in the code tracking stage. Therefore, in order to meet this emerging challenge and to improve the accuracy of the delay estimation in severe multipath scenarios, this paper analyzes feedback as well as feedforward code tracking algorithms and proposes the peak tracking (PT methods, which are combinations of both feedback and feedforward structures and utilize the inherent advantages of both structures. We propose and analyze here two variants of PT algorithm: PT with second-order differentiation (Diff2, and PT with Teager Kaiser (TK operator, which will be denoted herein as PT(Diff2 and PT(TK, respectively. In addition to the proposal of the PT methods, the authors propose also an improved early-late-slope (IELS multipath elimination technique which is shown to provide very good mean-time-to-lose-lock (MTLL performance. An implementation of a noncoherent multipath estimating delay locked loop (MEDLL structure is also presented. We also incorporate here an extensive review of the existing feedback and feedforward delay estimation algorithms for direct sequence code division multiple access (DS-CDMA signals in satellite fading channels, by taking into account the impact of binary phase shift keying (BPSK as well as the newly proposed BOC modulation

  1. Characteristics and Diurnal Cycle of GPM Rainfall Estimates over the Central Amazon Region

    Directory of Open Access Journals (Sweden)

    Rômulo Oliveira

    2016-06-01

    Full Text Available Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement and GoAmazon (Observations and Modeling of the Green Ocean Amazon over the Brazilian Amazon during 2014/2015. This study aims to assess the characteristics of Global Precipitation Measurement (GPM satellite-based precipitation estimates in representing the diurnal cycle over the Brazilian Amazon. The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG and the Goddard Profiling Algorithm—Version 2014 (GPROF2014 algorithms are evaluated against ground-based radar observations. Specifically, the S-band weather radar from the Amazon Protection National System (SIPAM, is first validated against the X-band CHUVA radar and then used as a reference to evaluate GPM precipitation. Results showed satisfactory agreement between S-band SIPAM radar and both IMERG and GPROF2014 algorithms. However, during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI sensor, significantly overestimates the frequency of heavy rainfall volumes around 00:00–04:00 UTC and 15:00–18:00 UTC. This overestimation is particularly evident over the Negro, Solimões and Amazon rivers due to the poorly-calibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM

  2. Estimating Evapotranspiration Using an Observation Based Terrestrial Water Budget

    Science.gov (United States)

    Rodell, Matthew; McWilliams, Eric B.; Famiglietti, James S.; Beaudoing, Hiroko K.; Nigro, Joseph

    2011-01-01

    Evapotranspiration (ET) is difficult to measure at the scales of climate models and climate variability. While satellite retrieval algorithms do exist, their accuracy is limited by the sparseness of in situ observations available for calibration and validation, which themselves may be unrepresentative of 500m and larger scale satellite footprints and grid pixels. Here, we use a combination of satellite and ground-based observations to close the water budgets of seven continental scale river basins (Mackenzie, Fraser, Nelson, Mississippi, Tocantins, Danube, and Ubangi), estimating mean ET as a residual. For any river basin, ET must equal total precipitation minus net runoff minus the change in total terrestrial water storage (TWS), in order for mass to be conserved. We make use of precipitation from two global observation-based products, archived runoff data, and TWS changes from the Gravity Recovery and Climate Experiment satellite mission. We demonstrate that while uncertainty in the water budget-based estimates of monthly ET is often too large for those estimates to be useful, the uncertainty in the mean annual cycle is small enough that it is practical for evaluating other ET products. Here, we evaluate five land surface model simulations, two operational atmospheric analyses, and a recent global reanalysis product based on our results. An important outcome is that the water budget-based ET time series in two tropical river basins, one in Brazil and the other in central Africa, exhibit a weak annual cycle, which may help to resolve debate about the strength of the annual cycle of ET in such regions and how ET is constrained throughout the year. The methods described will be useful for water and energy budget studies, weather and climate model assessments, and satellite-based ET retrieval optimization.

  3. Using GRACE to constrain precipitation amount over cold mountainous basins

    Science.gov (United States)

    Behrangi, Ali; Gardner, Alex S.; Reager, John T.; Fisher, Joshua B.

    2017-01-01

    Despite the importance for hydrology and climate-change studies, current quantitative knowledge on the amount and distribution of precipitation in mountainous and high-elevation regions is limited due to instrumental and retrieval shortcomings. Here by focusing on two large endorheic basins in High Mountain Asia, we show that satellite gravimetry (Gravity Recovery and Climate Experiment (GRACE)) can be used to provide an independent estimate of monthly accumulated precipitation using mass balance equation. Results showed that the GRACE-based precipitation estimate has the highest agreement with most of the commonly used precipitation products in summer, but it deviates from them in cold months, when the other products are expected to have larger errors. It was found that most of the products capture about or less than 50% of the total precipitation estimated using GRACE in winter. Overall, Global Precipitation Climatology Project (GPCP) showed better agreement with GRACE estimate than other products. Yet on average GRACE showed 30% more annual precipitation than GPCP in the study basins. In basins of appropriate size with an absence of dense ground measurements, as is a typical case in cold mountainous regions, we find GRACE can be a viable alternative to constrain monthly and seasonal precipitation estimates from other remotely sensed precipitation products that show large bias.

  4. The Impact of Urbanization on the Precipitation Component of the Water Cycle: A New Perspective

    Science.gov (United States)

    Shephard, J. Marshal

    2002-01-01

    presentation discusses the feasibility of using the TRMM or GPM satellite to identify precipitation anomalies likely caused by urbanization (Shepherd et al. 2002). Recent results from analyses of TRMM data around several major U.S. cities (e.g. Dallas, Atlanta, Houston) will be discussed. The presentation also summarizes a NASA-funded research effort to investigate the phenomenon of urban-induced precipitation anomalies using TRMM (future GPM) satellite-based remote sensing, an intensive ground observation/validation effort near Atlanta, and coupled atmosphere-land numerical modeling techniques.

  5. Precipitates in irradiated Zircaloy

    International Nuclear Information System (INIS)

    Chung, H.M.

    1985-10-01

    Precipitates in high-burnup (>20 MWd/kg U) Zircaloy spent-fuel cladding discharged from commercial boiling- and pressurized-water reactors have been characterized by TEM-HVEM. Three classes of primary precipitates were observed in the irradiated Zircaloys: Zr 3 O (2 to 6 nm), cubic-ZrO 2 (greater than or equal to 10 nm), and delta-hydride (35 to 100 nm). The former two precipitations appears to be irradiation induced in nature. Zr(Fe/sub x/Cr/sub 1-x/) 2 and Zr 2 (Fe/sub x/Ni/sub 1-x/) intermetallics, which are the primary precipitates in unirradiated Zircaloys, were largely dissolved after the high burnup. It seems, therefore, that the influence of the size and distribution of the intermetallics on the corrosion behavior may be quite different for the irradiated Zircaloys

  6. WPA Precipitation Tabulations

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Hourly precipitation data tabulated under the Work Projects Administration (WPA), a New Deal program created to reduce unemployment during the Great Depression....

  7. A novel cross-satellite based assessment of the spatio-temporal development of a cyanobacterial harmful algal bloom

    Science.gov (United States)

    Page, Benjamin P.; Kumar, Abhishek; Mishra, Deepak R.

    2018-04-01

    As the frequency of cyanobacterial harmful algal blooms (CyanoHABs) become more common in recreational lakes and water supply reservoirs, demand for rapid detection and temporal monitoring will be imminent for effective management. The goal of this study was to demonstrate a novel and potentially operational cross-satellite based protocol for synoptic monitoring of rapidly evolving and increasingly common CyanoHABs in inland waters. The analysis involved a novel way to cross-calibrate a chlorophyll-a (Chl-a) detection model for the Landsat-8 OLI sensor from the relationship between the normalized difference chlorophyll index and the floating algal index derived from Sentinel-2A on a coinciding overpass date during the summer CyanoHAB bloom in Utah Lake. This aided in the construction of a time-series phenology of the Utah Lake CyanoHAB event. Spatio-temporal cyanobacterial density maps from both Sentinel-2A and Landsat-8 sensors revealed that the bloom started in the first week of July 2016 (July 3rd, mean cell count: 9163 cells/mL), reached peak in mid-July (July 15th, mean cell count: 108176 cells/mL), and reduced in August (August 24th, mean cell count: 9145 cells/mL). Analysis of physical and meteorological factors suggested a complex interaction between landscape processes (high surface runoff), climatic conditions (high temperature, high rainfall followed by negligible rainfall, stable wind), and water quality (low water level, high Chl-a) which created a supportive environment for triggering these blooms in Utah Lake. This cross satellite-based monitoring methods can be a great tool for regular monitoring and will reduce the budget cost for monitoring and predicting CyanoHABs in large lakes.

  8. Spatial and temporal interpolation of satellite-based aerosol optical depth measurements over North America using B-splines

    Science.gov (United States)

    Pfister, Nicolas; O'Neill, Norman T.; Aube, Martin; Nguyen, Minh-Nghia; Bechamp-Laganiere, Xavier; Besnier, Albert; Corriveau, Louis; Gasse, Geremie; Levert, Etienne; Plante, Danick

    2005-08-01

    Satellite-based measurements of aerosol optical depth (AOD) over land are obtained from an inversion procedure applied to dense dark vegetation pixels of remotely sensed images. The limited number of pixels over which the inversion procedure can be applied leaves many areas with little or no AOD data. Moreover, satellite coverage by sensors such as MODIS yields only daily images of a given region with four sequential overpasses required to straddle mid-latitude North America. Ground based AOD data from AERONET sun photometers are available on a more continuous basis but only at approximately fifty locations throughout North America. The object of this work is to produce a complete and coherent mapping of AOD over North America with a spatial resolution of 0.1 degree and a frequency of three hours by interpolating MODIS satellite-based data together with available AERONET ground based measurements. Before being interpolated, the MODIS AOD data extracted from different passes are synchronized to the mapping time using analyzed wind fields from the Global Multiscale Model (Meteorological Service of Canada). This approach amounts to a trajectory type of simplified atmospheric dynamics correction method. The spatial interpolation is performed using a weighted least squares method applied to bicubic B-spline functions defined on a rectangular grid. The least squares method enables one to weight the data accordingly to the measurement errors while the B-splines properties of local support and C2 continuity offer a good approximation of AOD behaviour viewed as a function of time and space.

  9. Global Monitoring RSEM System for Crop Production by Incorporating Satellite-based Photosynthesis Rates and Anomaly Data of Sea Surface Temperature

    Science.gov (United States)

    Kaneko, D.; Sakuma, H.

    2014-12-01

    The first author has been developing RSEM crop-monitoring system using satellite-based assessment of photosynthesis, incorporating meteorological conditions. Crop production comprises of several stages and plural mechanisms based on leaf photosynthesis, surface energy balance, and the maturing of grains after fixation of CO2, along with water exchange through soil vegetation-atmosphere transfer. Grain production in prime countries appears to be randomly perturbed regionally and globally. Weather for crop plants reflects turbulent phenomena of convective and advection flows in atmosphere and surface boundary layer. It has been difficult for scientists to simulate and forecast weather correctly for sufficiently long terms to crop harvesting. However, severely poor harvests related to continental events must originate from a consistent mechanism of abnormal energetic flow in the atmosphere through both land and oceans. It should be remembered that oceans have more than 100 times of energy storage compared to atmosphere and ocean currents represent gigantic energy flows, strongly affecting climate. Anomalies of Sea Surface Temperature (SST), globally known as El Niño, Indian Ocean dipole, and Atlantic Niño etc., affect the seasonal climate on a continental scale. The authors aim to combine monitoring and seasonal forecasting, considering such mechanisms through land-ocean biosphere transfer. The present system produces assessments for all continents, specifically monitoring agricultural fields of main crops. Historical regions of poor and good harvests are compared with distributions of SST anomalies, which are provided by NASA GSFC. Those comparisons fairly suggest that the Worst harvest in 1993 and the Best in 1994 relate to the offshore distribution of low temperature anomalies and high gaps in ocean surface temperatures. However, high-temperature anomalies supported good harvests because of sufficient solar radiation for photosynthesis, and poor harvests because

  10. Validation and in vivo assessment of an innovative satellite-based solar UV dosimeter for a mobile app dedicated to skin health.

    Science.gov (United States)

    Morelli, M; Masini, A; Simeone, E; Khazova, M

    2016-08-31

    We present an innovative satellite-based solar UV (ultraviolet) radiation dosimeter with a mobile app interface that has been validated by exploiting both ground-based measurements and an in vivo assessment of the erythemal effects on some volunteers having controlled exposure to solar radiation. The app with this satellite-based UV dosimeter also includes other related functionalities such as the provision of safe sun exposure time updated in real-time and end exposure visual/sound alert. Both validations showed that the system has a good accuracy and reliability needed for health-related applications. This app will be launched on the market by siHealth Ltd in May 2016 under the name of "HappySun" and is available for both Android and iOS devices (more info on ). Extensive R&D activities are on-going for the further improvement of the satellite-based UV dosimeter's accuracy.

  11. Satellite-based retrieval of particulate matter concentrations over the United Arab Emirates (UAE)

    Science.gov (United States)

    Zhao, Jun; Temimi, Marouane; Hareb, Fahad; Eibedingil, Iyasu

    2016-04-01

    In this study, an empirical algorithm was established to retrieve particulate matter (PM) concentrations (PM2.5 and PM10) using satellite-derived aerosol optical depth (AOD) over the United Arab Emirates (UAE). Validation of the proposed algorithm using ground truth data demonstrates its good accuracy. Time series of in situ measured PM concentrations between 2014 and 2015 showed high values in summer and low values in winter. Estimated and in situ measured PM concentrations were higher in 2015 than 2014. Remote sensing is an essential tool to reveal and back track the seasonality and inter-annual variations of PM concentrations and provide valuable information on the protection of human health and the response of air quality to anthropogenic activities and climate change.

  12. Towards a satellite based system for monitoring agricultural water use: A case study for Saudi Arabia

    KAUST Repository

    McCabe, Matthew; Houborg, Rasmus; Rosas, Jorge; Ershadi, Ali; Anderson, Martha; Hain, Christopher

    2015-01-01

    Over the last few decades, the Kingdom of Saudi Arabia (KSA) has witnessed a dramatic expansion of its agricultural sector. In common with many other developing countries, this has been driven by a combination of population increases and the related effects on consumption as well as a demand for increased food security. Inevitably, the sector growth has come at the expense of a parallel increase in water consumption. Indeed, it is estimated that more than 80% of all of the water used in the Kingdom relates to agricultural production. More concerning is that the vast majority of this water is derived from non-renewable fossil groundwater extraction. To exacerbate the problem, groundwater extraction is largely unmonitored, meaning that there is very little accounting of water use on a routine basis. In the absence of techniques to directly quantify abstractions related to agriculture at large spatial scales, a mechanism for inferring crop water use as an indirect surrogate is required.

  13. Towards a satellite based system for monitoring agricultural water use: A case study for Saudi Arabia

    KAUST Repository

    McCabe, Matthew

    2015-11-12

    Over the last few decades, the Kingdom of Saudi Arabia (KSA) has witnessed a dramatic expansion of its agricultural sector. In common with many other developing countries, this has been driven by a combination of population increases and the related effects on consumption as well as a demand for increased food security. Inevitably, the sector growth has come at the expense of a parallel increase in water consumption. Indeed, it is estimated that more than 80% of all of the water used in the Kingdom relates to agricultural production. More concerning is that the vast majority of this water is derived from non-renewable fossil groundwater extraction. To exacerbate the problem, groundwater extraction is largely unmonitored, meaning that there is very little accounting of water use on a routine basis. In the absence of techniques to directly quantify abstractions related to agriculture at large spatial scales, a mechanism for inferring crop water use as an indirect surrogate is required.

  14. Costs and benefits of satellite-based tools for irrigation management

    Directory of Open Access Journals (Sweden)

    Francesco eVuolo

    2015-07-01

    Full Text Available This paper presents the results of a collaborative work with farmers and a cost-benefit analysis of geospatial technologies applied to irrigation water management in the semi-arid agricultural area in Lower Austria. We use Earth observation (EO data to estimate crop evapotranspiration (ET and webGIS technologies to deliver maps and irrigation advice to farmers. The study reports the technical and qualitative evaluation performed during a demonstration phase in 2013 and provides an outlook to future developments. The calculation of the benefits is based on a comparison of the irrigation volumes estimated from satellite vs. the irrigation supplied by the farmers. In most cases, the amount of water supplied was equal to the maximum amount of water required by crops. At the same time high variability was observed for the different irrigation units and crop types. Our data clearly indicates that economic benefits could be achieved by reducing irrigation volumes, especially for water-intensive crops. Regarding the qualitative evaluation, most of the farmers expressed a very positive interest in the provided information. In particular, information related to crop ET was appreciated as this helps to make better informed decisions on irrigation. The majority of farmers (54% also expressed a general willingness to pay, either directly or via cost sharing, for such a service. Based on different cost scenarios, we calculated the cost of the service. Considering 20,000 ha regularly irrigated land, the advisory service would cost between 2.5 and 4.3 €/ha per year depending on the type of satellite data used. For comparison, irrigation costs range between 400 and 1000 €/ha per year for a typical irrigation volume of 2,000 cubic meters per ha. With a correct irrigation application, more than 10% of the water and energy could be saved in water-intensive crops, which is equivalent to an economic benefit of 40-100 €/ha per year.

  15. On the ability of RegCM4 to simulate surface solar radiation patterns over Europe: An assessment using satellite-based observations

    Science.gov (United States)

    Alexandri, Georgia; Georgoulias, Aristeidis K.; Zanis, Prodromos; Tsikerdekis, Athanasios; Katragkou, Eleni; Kourtidis, Konstantinos; Meleti, Charikleia

    2015-04-01

    We assess here the ability of RegCM4 to simulate the surface solar radiation (SSR) patterns over the European domain. For the needs of this work, a decadal (1999-2009) simulation was implemented at a horizontal resolution of 50km using the first year as a spin-up. The model is driven by emissions from CMIP5 while ERA-interim data were used as lateral boundary conditions. The RegCM4 SSR fields were validated against satellite-based SSR observations from Meteosat First Generation (MFG) and Meteosat Second Generation (MSG) sensors (CM SAF SIS product). The RegCM4 simulations slightly overestimate SSR compared to CM SAF over Europe with the bias being +1.54% in case of MFG (2000-2005) and +3.34% in case of MSG (2006-2009). SSR from RegCM4 is much closer to SSR from CM SAF over land (bias of -1.59% for MFG and +0.66% for MSG) than over ocean (bias of +7.20% for MFG and 8.07% for MSG). In order to understand the reasons of this bias, we proceeded to a detailed assessment of various parameters that define the SSR levels (cloud fractional cover - CFC, cloud optical thickness - COT, cloud droplet effective radius - Re, aerosol optical thickness - AOD, asymmetry factor - ASY, single scattering albedo - SSA, water vapor - WV and surface albedo - ALB). We validated the simulated CFC, COT and Re from RegCM4 against satellite-based observations from MSG and we found that RegCM4 significantly underestimates CFC and Re, and overestimates COT over Europe. The aerosol-related parameters from RegCM4 were compared with values from the aerosol climatology taken into account within CM SAF SSR estimates. AOD is significantly underestimated in our simulations which leads to a positive SSR bias. The RegCM4 WV and ALB were compared with WV values from ERA-interim and ALB climatological observations from CERES which are also taken into account within CM SAF SSR estimates. Finally, with the use of a radiative transfer model (SBDART) we manage to quantify the relative contribution of each of

  16. Probability of occurrence of monthly and seasonal winter precipitation over Northwest India based on antecedent-monthly precipitation

    Science.gov (United States)

    Nageswararao, M. M.; Mohanty, U. C.; Dimri, A. P.; Osuri, Krishna K.

    2018-05-01

    Winter (December, January, and February (DJF)) precipitation over northwest India (NWI) is mainly associated with the eastward moving mid-latitude synoptic systems, western disturbances (WDs), embedded within the subtropical westerly jet (SWJ), and is crucial for Rabi (DJF) crops. In this study, the role of winter precipitation at seasonal and monthly scale over NWI and its nine meteorological subdivisions has been analyzed. High-resolution (0.25° × 0.25°) gridded precipitation data set of India Meteorological Department (IMD) for the period of 1901-2013 is used. Results indicated that the seasonal precipitation over NWI is below (above) the long-term mean in most of the years, when precipitation in any of the month (December/January/February) is in deficit (excess). The contribution of December precipitation (15-20%) to the seasonal (DJF) precipitation is lesser than January (35-40%) and February (35-50%) over all the subdivisions. December (0.60), January (0.57), and February (0.69) precipitation is in-phase (correlation) with the corresponding winter season precipitation. However, January precipitation is not in-phase with the corresponding December (0.083) and February (-0.03) precipitation, while December is in-phase with the February (0.21). When monthly precipitation (December or January or December-January or February) at subdivision level over NWI is excess (deficit); then, the probability of occurrence of seasonal excess (deficit) precipitation is high (almost nil). When antecedent-monthly precipitation is a deficit or excess, the probability of monthly (January or February or January + February) precipitation to be a normal category is >60% over all the subdivisions. This study concludes that the December precipitation is a good indicator to estimate the performance of January, February, January-February, and the seasonal (DJF) precipitation.

  17. An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme.

    Science.gov (United States)

    Valderrama-Landeros, L; Flores-de-Santiago, F; Kovacs, J M; Flores-Verdugo, F

    2017-12-14

    Optimizing the classification accuracy of a mangrove forest is of utmost importance for conservation practitioners. Mangrove forest mapping using satellite-based remote sensing techniques is by far the most common method of classification currently used given the logistical difficulties of field endeavors in these forested wetlands. However, there is now an abundance of options from which to choose in regards to satellite sensors, which has led to substantially different estimations of mangrove forest location and extent with particular concern for degraded systems. The objective of this study was to assess the accuracy of mangrove forest classification using different remotely sensed data sources (i.e., Landsat-8, SPOT-5, Sentinel-2, and WorldView-2) for a system located along the Pacific coast of Mexico. Specifically, we examined a stressed semiarid mangrove forest which offers a variety of conditions such as dead areas, degraded stands, healthy mangroves, and very dense mangrove island formations. The results indicated that Landsat-8 (30 m per pixel) had  the lowest overall accuracy at 64% and that WorldView-2 (1.6 m per pixel) had the highest at 93%. Moreover, the SPOT-5 and the Sentinel-2 classifications (10 m per pixel) were very similar having accuracies of 75 and 78%, respectively. In comparison to WorldView-2, the other sensors overestimated the extent of Laguncularia racemosa and underestimated the extent of Rhizophora mangle. When considering such type of sensors, the higher spatial resolution can be particularly important in mapping small mangrove islands that often occur in degraded mangrove systems.

  18. Satellite Based Probabilistic Snow Cover Extent Mapping (SCE) at Hydro-Québec

    Science.gov (United States)

    Teasdale, Mylène; De Sève, Danielle; Angers, Jean-François; Perreault, Luc

    2016-04-01

    Over 40% of Canada's water resources are in Quebec and Hydro-Quebec has developed potential to become one of the largest producers of hydroelectricity in the world, with a total installed capacity of 36,643 MW. The Hydro-Québec fleet park includes 27 large reservoirs with a combined storage capacity of 176 TWh, and 668 dams and 98 controls. Thus, over 98% of all electricity used to supply the domestic market comes from water resources and the excess output is sold on the wholesale markets. In this perspective the efficient management of water resources is needed and it is based primarily on a good river flow estimation including appropriate hydrological data. Snow on ground is one of the significant variables representing 30% to 40% of its annual energy reserve. More specifically, information on snow cover extent (SCE) and snow water equivalent (SWE) is crucial for hydrological forecasting, particularly in northern regions since the snowmelt provides the water that fills the reservoirs and is subsequently used for hydropower generation. For several years Hydro Quebec's research institute ( IREQ) developed several algorithms to map SCE and SWE. So far all the methods were deterministic. However, given the need to maximize the efficient use of all resources while ensuring reliability, the electrical systems must now be managed taking into account all risks. Since snow cover estimation is based on limited spatial information, it is important to quantify and handle its uncertainty in the hydrological forecasting system. This paper presents the first results of a probabilistic algorithm for mapping SCE by combining Bayesian mixture of probability distributions and multiple logistic regression models applied to passive microwave data. This approach allows assigning for each grid point, probabilities to the set of the mutually exclusive discrete outcomes: "snow" and "no snow". Its performance was evaluated using the Brier score since it is particularly appropriate to

  19. Hierarchical Satellite-based Approach to Global Monitoring of Crop Condition and Food Production

    Science.gov (United States)

    Zheng, Y.; Wu, B.; Gommes, R.; Zhang, M.; Zhang, N.; Zeng, H.; Zou, W.; Yan, N.

    2014-12-01

    The assessment of global food security goes beyond the mere estimate of crop production: It needs to take into account the spatial and temporal patterns of food availability, as well as physical and economic access. Accurate and timely information is essential to both food producers and consumers. Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, such as FY-2/3A, HJ-1 CCD, CropWatch has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The new monitoring approach adopts a hierarchical system covering four spatial levels of detail: global (sixty-five Monitoring and Reporting Units, MRU), seven major production zones (MPZ), thirty-one key countries (including China) and "sub- countries." The thirty-one countries encompass more that 80% of both global exports and production of four major crops (maize, rice, soybean and wheat). The methodology resorts to climatic and remote sensing indicators at different scales, using the integrated information to assess global, regional, and national (as well as sub-national) crop environmental condition, crop condition, drought, production, and agricultural trends. The climatic indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass are first analysed at global scale to describe overall crop growing conditions. At MPZ scale, the key indicators pay more attention to crops and include Vegetation health index (VHI), Vegetation condition index (VCI), Cropped arable land fraction (CALF) as well as Cropping intensity (CI). Together, they characterise agricultural patterns, farming intensity and stress. CropWatch carries out detailed crop condition analyses for thirty one individual countries at the national scale with a comprehensive array of variables and indicators. The Normalized difference vegetation index (NDVI), cropped areas and crop condition are

  20. National satellite-based humid tropical forest change assessment in Peru in support of REDD+ implementation

    Science.gov (United States)

    Potapov, P. V.; Dempewolf, J.; Talero, Y.; Hansen, M. C.; Stehman, S. V.; Vargas, C.; Rojas, E. J.; Castillo, D.; Mendoza, E.; Calderón, A.; Giudice, R.; Malaga, N.; Zutta, B. R.

    2014-12-01

    Transparent, consistent, and accurate national forest monitoring is required for successful implementation of reducing emissions from deforestation and forest degradation (REDD+) programs. Collecting baseline information on forest extent and rates of forest loss is a first step for national forest monitoring in support of REDD+. Peru, with the second largest extent of Amazon basin rainforest, has made significant progress in advancing its forest monitoring capabilities. We present a national-scale humid tropical forest cover loss map derived by the Ministry of Environment REDD+ team in Peru. The map quantifies forest loss from 2000 to 2011 within the Peruvian portion of the Amazon basin using a rapid, semi-automated approach. The available archive of Landsat imagery (11 654 scenes) was processed and employed for change detection to obtain annual gross forest cover loss maps. A stratified sampling design and a combination of Landsat (30 m) and RapidEye (5 m) imagery as reference data were used to estimate the primary forest cover area, total gross forest cover loss area, proportion of primary forest clearing, and to validate the Landsat-based map. Sample-based estimates showed that 92.63% (SE = 2.16%) of the humid tropical forest biome area within the country was covered by primary forest in the year 2000. Total gross forest cover loss from 2000 to 2011 equaled 2.44% (SE = 0.16%) of the humid tropical forest biome area. Forest loss comprised 1.32% (SE = 0.37%) of primary forest area and 9.08% (SE = 4.04%) of secondary forest area. Validation confirmed a high accuracy of the Landsat-based forest cover loss map, with a producer’s accuracy of 75.4% and user’s accuracy of 92.2%. The majority of forest loss was due to clearing (92%) with the rest attributed to natural processes (flooding, fires, and windstorms). The implemented Landsat data processing and classification system may be used for operational annual forest cover loss updates at the national level for REDD

  1. National satellite-based humid tropical forest change assessment in Peru in support of REDD+ implementation

    International Nuclear Information System (INIS)

    Potapov, P V; Dempewolf, J; Talero, Y; Hansen, M C; Stehman, S V; Vargas, C; Rojas, E J; Calderón, A; Giudice, R; Malaga, N; Zutta, B R; Castillo, D; Mendoza, E

    2014-01-01

    Transparent, consistent, and accurate national forest monitoring is required for successful implementation of reducing emissions from deforestation and forest degradation (REDD+) programs. Collecting baseline information on forest extent and rates of forest loss is a first step for national forest monitoring in support of REDD+. Peru, with the second largest extent of Amazon basin rainforest, has made significant progress in advancing its forest monitoring capabilities. We present a national-scale humid tropical forest cover loss map derived by the Ministry of Environment REDD+ team in Peru. The map quantifies forest loss from 2000 to 2011 within the Peruvian portion of the Amazon basin using a rapid, semi-automated approach. The available archive of Landsat imagery (11 654 scenes) was processed and employed for change detection to obtain annual gross forest cover loss maps. A stratified sampling design and a combination of Landsat (30 m) and RapidEye (5 m) imagery as reference data were used to estimate the primary forest cover area, total gross forest cover loss area, proportion of primary forest clearing, and to validate the Landsat-based map. Sample-based estimates showed that 92.63% (SE = 2.16%) of the humid tropical forest biome area within the country was covered by primary forest in the year 2000. Total gross forest cover loss from 2000 to 2011 equaled 2.44% (SE = 0.16%) of the humid tropical forest biome area. Forest loss comprised 1.32% (SE = 0.37%) of primary forest area and 9.08% (SE = 4.04%) of secondary forest area. Validation confirmed a high accuracy of the Landsat-based forest cover loss map, with a producer’s accuracy of 75.4% and user’s accuracy of 92.2%. The majority of forest loss was due to clearing (92%) with the rest attributed to natural processes (flooding, fires, and windstorms). The implemented Landsat data processing and classification system may be used for operational annual forest cover loss updates at the national level

  2. Validation of near infrared satellite based algorithms to relative atmospheric water vapour content over land

    International Nuclear Information System (INIS)

    Serpolla, A.; Bonafoni, S.; Basili, P.; Biondi, R.; Arino, O.

    2009-01-01

    This paper presents the validation results of ENVISAT MERIS and TERRA MODIS retrieval algorithms for atmospheric Water Vapour Content (WVC) estimation in clear sky condition on land. The MERIS algorithms exploits the radiance ratio of the absorbing channel at 900 nm with the almost absorption-free reference at 890 nm, while the MODIS one is based on the ratio of measurements centred at near 0.905, 0.936, and 0.94 μm with atmospheric window reflectance at 0.865 and 1.24 μm. The first test was performed in the Mediterranean area using WVC provided from both ECMWF and AERONET. As a second step, the performances of the algorithms were tested exploiting WVC computed from radio sounding (RAOBs)in the North East Australia. The different comparisons with respect to reference WVC values showed an overestimation of WVC by MODIS (root mean square error percentage greater than 20%) and an acceptable performance of MERIS algorithms (root mean square error percentage around 10%) [it

  3. Satellite-based Analysis of CO Variability over the Amazon Basin

    Science.gov (United States)

    Deeter, M. N.; Emmons, L. K.; Martinez-Alonso, S.; Tilmes, S.; Wiedinmyer, C.

    2017-12-01

    Pyrogenic emissions from the Amazon Basin exert significant influence on both climate and air quality but are highly variable from year to year. The ability of models to simulate the impact of biomass burning emissions on downstream atmospheric concentrations depends on (1) the quality of surface flux estimates (i.e., emissions inventories), (2) model dynamics (e.g., horizontal winds, large-scale convection and mixing) and (3) the representation of atmospheric chemical processes. With an atmospheric lifetime of a few months, carbon monoxide (CO) is a commonly used diagnostic for biomass burning. CO products are available from several satellite instruments and allow analyses of CO variability over extended regions such as the Amazon Basin with useful spatial and temporal sampling characteristics. The MOPITT ('Measurements of Pollution in the Troposphere') instrument was launched on the NASA Terra platform near the end of 1999 and is still operational. MOPITT is uniquely capable of measuring tropospheric CO concentrations using both thermal-infrared and near-infrared observations, resulting in the ability to independently retrieve lower- and upper-troposphere CO concentrations. We exploit the 18-year MOPITT record and related datasets to analyze the variability of CO over the Amazon Basin and evaluate simulations performed with the CAM-chem chemical transport model. We demonstrate that observed differences between MOPITT observations and model simulations provide important clues regarding emissions inventories, convective mixing and long-range transport.

  4. Satellite-based studies of maize yield spatial variations and their causes in China

    Science.gov (United States)

    Zhao, Y.

    2013-12-01

    Maize production in China has been expanding significantly in the past two decades, but yield has become relatively stagnant in the past few years, and needs to be improved to meet increasing demand. Multiple studies found that the gap between potential and actual yield of maize is as large as 40% to 60% of yield potential. Although a few major causes of yield gap have been qualitatively identified with surveys, there has not been spatial analysis aimed at quantifying relative importance of specific biophysical and socio-economic causes, information which would be useful for targeting interventions. This study analyzes the causes of yield variation at field and village level in Quzhou county of North China Plain (NCP). We combine remote sensing and crop modeling to estimate yields in 2009-2012, and identify fields that are consistently high or low yielding. To establish the relationship between yield and potential factors, we gather data on those factors through a household survey. We select targeted survey fields such that not only both extremes of yield distribution but also all soil texture categories in the county is covered. Our survey assesses management and biophysical factors as well as social factors such as farmers' access to agronomic knowledge, which is approximated by distance to the closest demonstration plot or 'Science and technology backyard'. Our survey covers 10 townships, 53 villages and 180 fields. Three to ten farmers are surveyed depending on the amount of variation present among sub pixels of each field. According to survey results, we extract the amount of variation within as well as between villages and or soil type. The higher within village or within field variation, the higher importance of management factors. Factors such as soil type and access to knowledge are more represented by between village variation. Through regression and analysis of variance, we gain more quantitative and thorough understanding of causes to yield variation at

  5. Assessment of the most recent satellite based digital elevation models of Egypt