WorldWideScience

Sample records for satellite precipitation estimation

  1. Regional Bias of Satellite Precipitation Estimates

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    Modrick, T. M.; Georgakakos, K. P.; Spencer, C. R.

    2012-12-01

    Satellite-based estimates of precipitation have improved the spatial availability of precipitation data particularly for regions with limited gauge networks due to limited accessibility or infrastructure. Understanding the quality and reliability of satellite precipitation estimates is important, especially when the estimates are utilitized for real-time hydrologic forecasting and for fast-responding phenomena. In partnership with the World Meteorological Organization (WMO), the U.S. Agency of International Development (USAID) and the National Ocean and Atmospheric Administration (NOAA), the Hydrologic Research Center has begun implementation of real-time flash flood warning systems for diverse regions around the world. As part of this effort, bias characteristics of satellite precipitation have been examined in these various regions, such includes portions of Southeastern Asia, Southeastern Europe, the Middle East, Central America, and the southern half of the African continent. The work has focused on the Global Hydro-Estimator (GHE) precipitation product from NOAA/NESDIS. These real-time systems utilize the GHE given low latency times of this product. This presentation focuses on the characterization of precipitation bias as compared to in-situ gauge records, and the regional variations or similarities. Additional analysis is currently underway considering regional bias for other satellite precipitation products (e.g., CMORPH) for comparison with the GHE results.

  2. How Well Can We Estimate Error Variance of Satellite Precipitation Data Around the World?

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    Gebregiorgis, A. S.; Hossain, F.

    2014-12-01

    The traditional approach to measuring precipitation by placing a probe on the ground will likely never be adequate or affordable in most parts of the world. Fortunately, satellites today provide a continuous global bird's-eye view (above ground) at any given location. However, the usefulness of such precipitation products for hydrological applications depends on their error characteristics. Thus, providing error information associated with existing satellite precipitation estimates is crucial to advancing applications in hydrologic modeling. In this study, we present a method of estimating satellite precipitation error variance using regression model for three satellite precipitation products (3B42RT, CMORPH, and PERSIANN-CCS) using easily available geophysical features and satellite precipitation rate. The goal of this work is to explore how well the method works around the world in diverse geophysical settings. Topography, climate, and seasons are considered as the governing factors to segregate the satellite precipitation uncertainty and fit a nonlinear regression equation as function of satellite precipitation rate. The error variance models were tested on USA, Asia, Middle East, and Mediterranean region. Rain-gauge based precipitation product was used to validate the errors variance of satellite precipitation products. Our study attests that transferability of model estimators (which help to estimate the error variance) from one region to another is practically possible by leveraging the similarity in geophysical features. Therefore, the quantitative picture of satellite precipitation error over ungauged regions can be discerned even in the absence of ground truth data.

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

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    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.

  4. Bias adjustment of satellite-based precipitation estimation using gauge observations: A case study in Chile

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    Yang, Zhongwen; Hsu, Kuolin; Sorooshian, Soroosh; Xu, Xinyi; Braithwaite, Dan; Verbist, Koen M. J.

    2016-04-01

    Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009-2014. The historical data (satellite and gauge) for 2009-2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide.

  5. Evaluation of satellite-based precipitation estimates in winter season using an object-based approach

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    Li, J.; Hsu, K.; AghaKouchak, A.; Sorooshian, S.

    2012-12-01

    Verification has become an integral component of satellite precipitation algorithms and products. A number of object-based verification methods have been proposed to provide diagnostic information regarding the precipitation products' ability to capture the spatial pattern, intensity, and placement of precipitation. However, most object-based methods are not capable of investigating precipitation objects at the storm-scale. In this study, an image processing approach known as watershed segmentation was adopted to detect the storm-scale rainfall objects. Then, a fuzzy logic-based technique was utilized to diagnose and analyze storm-scale object attributes, including centroid distance, area ratio, intersection area ratio and orientation angle difference. Three verification metrics (i.e., false alarm ratio, missing ratio and overall membership score) were generated for validation and verification. Three satellite-based precipitation products, including PERSIANN, CMORPH, 3B42RT, were evaluated against NOAA stage IV MPE multi-sensor composite rain analysis at 0.25° by 0.25° on a daily scale in the winter season of 2010 over the contiguous United States. Winter season is dominated by frontal systems which usually have larger area coverage. All three products and the stage IV observation tend to find large size storm objects. With respect to the evaluation attributes, PERSIANN tends to obtain larger area ratio and consequently has larger centroid distance to the stage IV observations, while 3B42RT are found to be closer to the stage IV for the object size. All evaluation products give small orientation angle differences but vary significantly for the missing ratio and false alarm ratio. This implies that satellite estimates can fail to detect storms in winter. The overall membership scores are close for all three different products which indicate that all three satellite-based precipitation products perform well for capturing the spatial and geometric characteristics of

  6. Interannual Variability of Tropical Precipitation: How Well Do Climate Models Agree With Current Satellite Estimates?

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    Robertson, Franklin R.; Marshall, Susan; Roads, John; Oglesby, Robert J.; Fitzjarrald, Dan; Goodman, H. Michael (Technical Monitor)

    2001-01-01

    Since the beginning of the World Climate Research Program's Global Precipitation Climatology Project (GPCP) satellite remote sensing of precipitation has made dramatic improvements, particularly for tropical regions. Data from microwave and infrared sensors now form the most critical input to precipitation data sets and can be calibrated with surface gauges to so that the strengths of each data source can be maximized in some statistically optimal sense. Recent availability of the TRMM (Tropical Rainfall Measuring Mission) has further aided in narrowing uncertainties in rainfall over die tropics and subtropics. Although climate modeling efforts have long relied on space-based precipitation estimates for validation, we now are in a position to make more quantitative assessments of model performance, particularly in tropical regions. An integration of the CCM3 using observed SSTs as a lower boundary condition is used to examine how well this model responds to ENSO forcing in terms of anomalous precipitation. An integration of the NCEP spectral model used for the Reanalysis-H effort is also examined. This integration is run with specified SSTs, but with no data assimilation. Our analysis focuses on two aspects of inter-annual variability. First are the spatial anomalies that are indicative of dislocations in Hadley and Walker circulations. Second, we consider the ability of models to replicate observed increases in oceanic precipitation that are noted in satellite observations for large ENSO events. Finally, we consider a slab ocean version of the CCM3 model with prescribed ocean beat transports that mimic upwelling anomalies, but which still allows the surface energy balance to be predicted. This less restrictive experiment is used to understand why model experiments with specified SSTs seem to have noticeably less interannual variability in precipitation than do the satellite observations.

  7. Bias reduction for Satellite Based Precipitation Estimates using statistical transformations in Guiana Shield

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    Ringard, Justine; Becker, Melanie; Seyler, Frederique; Linguet, Laurent

    2016-04-01

    Currently satellite-based precipitation estimates exhibit considerable biases, and there have been many efforts to reduce these biases by merging surface gauge measurements with satellite-based estimates. In Guiana Shield all products exhibited better performances during the dry season (August- December). All products greatly overestimate very low intensities (50 mm). Moreover the responses of each product are different according to hydro climatic regimes. The aim of this study is to correct spatially the bias of precipitation, and compare various correction methods to define the best methods depending on the rainfall characteristic correcting (intensity, frequency). Four satellites products are used: Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product (3B42V7) and real time product (3B42RT), the Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network (PERSIANN) and the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH), for six hydro climatic regimes between 2001 and 2012. Several statistical transformations are used to correct the bias. Statistical transformations attempt to find a function h that maps a simulated variable Ps such that its new distribution equals the distribution of the observed variable Po. The first is the use of a distribution derived transformations which is a mixture of the Bernoulli and the Gamma distribution, where the Bernoulli distribution is used to model the probability of precipitation occurrence and the Gamma distribution used to model precipitation intensities. The second a quantile-quantile relation using parametric transformation, and the last one is a common approach using the empirical CDF of observed and modelled values instead of assuming parametric distributions. For each correction 30% of both, simulated and observed data sets, are used to calibrate and the other part used to validate. The validation are test with statistical

  8. A strategy for merging objective estimates of global daily precipitation from gauge observations, satellite estimates, and numerical predictions

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    Nie, Suping; Wu, Tongwen; Luo, Yong; Deng, Xueliang; Shi, Xueli; Wang, Zaizhi; Liu, Xiangwen; Huang, Jianbin

    2016-07-01

    This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates (SEs), and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps. First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions (MPs) over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation (OI)-based merging scheme (referred as the HL-OI scheme) is used to combine unbiased gauge observations, SEs, and MPs to reduce random error from each source and to produce a gauge—satellite-model merged daily precipitation analysis, called BMEP-d (Beijing Climate Center Merged Estimation of Precipitation with daily resolution), with complete global coverage. The BMEP-d data from a four-year period (2011-14) demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality. Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD (GPCP one-degree daily) show that the consistencies between BMEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.

  9. Improved global high resolution precipitation estimation using multi-satellite multi-spectral information

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    Behrangi, Ali

    In respond to the community demands, combining microwave (MW) and infrared (IR) estimates of precipitation has been an active area of research since past two decades. The anticipated launching of NASA's Global Precipitation Measurement (GPM) mission and the increasing number of spectral bands in recently launched geostationary platforms will provide greater opportunities for investigating new approaches to combine multi-source information towards improved global high resolution precipitation retrievals. After years of the communities' efforts the limitations of the existing techniques are: (1) Drawbacks of IR-only techniques to capture warm rainfall and screen out no-rain thin cirrus clouds; (2) Grid-box- only dependency of many algorithms with not much effort to capture the cloud textures whether in local or cloud patch scale; (3) Assumption of indirect relationship between rain rate and cloud-top temperature that force high intensity precipitation to any cold cloud; (4) Neglecting the dynamics and evolution of cloud in time; (5) Inconsistent combination of MW and IR-based precipitation estimations due to the combination strategies and as a result of above described shortcomings. This PhD dissertation attempts to improve the combination of data from Geostationary Earth Orbit (GEO) and Low-Earth Orbit (LEO) satellites in manners that will allow consistent high resolution integration of the more accurate precipitation estimates, directly observed through LEO's PMW sensors, into the short-term cloud evolution process, which can be inferred from GEO images. A set of novel approaches are introduced to cope with the listed limitations and is consist of the following four consecutive components: (1) starting with the GEO part and by using an artificial-neural network based method it is demonstrated that inclusion of multi-spectral data can ameliorate existing problems associated with IR-only precipitating retrievals; (2) through development of Precipitation Estimation

  10. South African Weather Service operational satellite based precipitation estimation technique: applications and improvements

    Directory of Open Access Journals (Sweden)

    E. de Coning

    2010-11-01

    Full Text Available Extreme weather related to heavy or more frequent precipitation events seem to be a likely possibility for the future of our planet. While precipitation measurements can be done by means of rain gauges, the obvious disadvantages of point measurements are driving meteorologists towards remotely sensed precipitation methods. In South Africa more sophisticated and expensive nowcasting technology such as radar and lightning networks are available, supported by a fairly dense rain gauge network of about 1500 gauges. In the rest of southern Africa rainfall measurements are more difficult to obtain. The availability of the local version of the Unified Model and the Meteosat Second Generation satellite data make these products ideal components of precipitation measurement in data sparse regions such as Africa. In this article the local version of the Hydroestimator (originally from NOAA/NESDIS is discussed as well as its applications for precipitation measurement in this region. Hourly accumulations of the Hydroestimator are currently used as a satellite based precipitation estimator for the South African Flash Flood Guidance system. However, the Hydroestimator is by no means a perfect representation of the real rainfall. In this study the Hydroestimator and the stratiform rainfall field from the Unified Model are both bias corrected and then combined into a new precipitation field which can feed into the South African Flash Flood Guidance system. This new product should provide a more accurate and comprehensive input to the Flash Flood Guidance systems in South Africa as well as southern Africa. In this way the southern African region where data is sparse and very few radars are available can have access to more accurate flash flood guidance.

  11. Bayesian Estimation of Precipitation from Satellite Passive Microwave Observations Using Combined Radar-Radiometer Retrievals

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    Grecu, Mircea; Olson, William S.

    2006-01-01

    Precipitation estimation from satellite passive microwave radiometer observations is a problem that does not have a unique solution that is insensitive to errors in the input data. Traditionally, to make this problem well posed, a priori information derived from physical models or independent, high-quality observations is incorporated into the solution. In the present study, a database of precipitation profiles and associated brightness temperatures is constructed to serve as a priori information in a passive microwave radiometer algorithm. The precipitation profiles are derived from a Tropical Rainfall Measuring Mission (TRMM) combined radar radiometer algorithm, and the brightness temperatures are TRMM Microwave Imager (TMI) observed. Because the observed brightness temperatures are consistent with those derived from a radiative transfer model embedded in the combined algorithm, the precipitation brightness temperature database is considered to be physically consistent. The database examined here is derived from the analysis of a month-long record of TRMM data that yields more than a million profiles of precipitation and associated brightness temperatures. These profiles are clustered into a tractable number of classes based on the local sea surface temperature, a radiometer-based estimate of the echo-top height (the height beyond which the reflectivity drops below 17 dBZ), and brightness temperature principal components. For each class, the mean precipitation profile, brightness temperature principal components, and probability of occurrence are determined. The precipitation brightness temperature database supports a radiometer-only algorithm that incorporates a Bayesian estimation methodology. In the Bayesian framework, precipitation estimates are weighted averages of the mean precipitation values corresponding to the classes in the database, with the weights being determined according to the similarity between the observed brightness temperature principal

  12. Analysis of TRMM 3-Hourly Multi-Satellite Precipitation Estimates Computed in Both Real and Post-Real Time

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    Huffman, George J.; Adler, Robert F.; Stocker, Erich; Bolvin, David T.; Nelkin, Eric J.

    2002-01-01

    Satellite data form the core of the information available for estimating precipitation on a global basis. While it is possible to create such estimates solely from one sensor, researchers have increasingly moved to using combinations of sensors in an attempt to improve accuracy, coverage, and resolution. This poster updates a long-term project in which the authors are working to provide routine combined-sensor estimates of precipitation over the entire globe at relatively fine time and space intervals. The goal is to produce these globally complete precipitation estimates on a 25-km grid every 3 hours. Since late January 2002 we have been estimating precipitation for the latitude band 50 degrees N-S within about 6 hours of observation time. This work is 1 of only 2 or 3 such efforts in the world. Now we are preparing to provide similar estimates for the last 5 years. All of this work is being carried out as part of the Tropical Rainfall Measuring Mission (TRMM). Initially, TRMM was focused on providing excellent long-term averages of precipitation in tropical regions, but since its launch in November 1997 continued research has allowed the same satellite and data system to be used for addressing weather-scale problems as well.

  13. The Contribution Of Sampling Errors In Satellite Precipitation Estimates To High Flood Uncertainty In Subtropical South America

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    Demaria, E. M.; Valdes, J. B.; Nijssen, B.; Rodriguez, D.; Su, F.

    2009-12-01

    Satellite precipitation estimates are becoming increasingly available at temporal and spatial scales of interest for hydrological applications. Unfortunately precipitation estimated from global satellites is prone to errors hailing from different sources. The impact of sampling errors on the hydrological cycle of a large-size basin was assessed with a macroscale hydrological model. Synthetic precipitation fields were generated in a Monte Carlo fashion by perturbing observed precipitation fields with sampling errors. Three sampling intervals were chosen to generate the precipitation fields: one-hour, three-hours which is the canonical Global Precipitation Mission (GPM) sampling interval, and six-hours. The Variable Infiltration Capacity (VIC) model was used to assess the impact of sampling errors on hydrological fluxes and states in the Iguazu basin in South America for the period 1982-2005. The propagation of sampling errors through the hydrological cycle was evaluated for high flow events that have the 2% chance of being exceeded in any given time. Results show that observed event volumes are underestimated for small volumes for the three and six-hours sampling intervals but for the one-hour sampling interval the difference is almost negligible.The timing of the hydrograph is not affected by uncertainty existent in satellite-derived precipitation when it propagates through the hydrological cycle. Results of two non-parametric tests: the Kruskal-Wallis test on the mean ranks of the population and the Ansari-Bradley test on the equality of the variances indicate that sampling errors do no affect the occurrence of high flows since their probability distribution is not affected. The applicability of these results is limited to a humid climate. However the Iguazu basin is representative of several basins located in subtropical regions around the world, many of which are under-instrumented catchments, where satellite precipitation might be one of the few available data

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

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    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

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

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

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    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

  17. Observed and blended gauge-satellite precipitation estimates perspective on meteorological drought intensity over South Sulawesi, Indonesia

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    Setiawan, A. M.; Koesmaryono, Y.; Faqih, A.; Gunawan, D.

    2017-01-01

    South Sulawesi province as one of the rice production center for national food security are highly influenced by climate phenomenon that lead to drought condition. This paper quantifies meteorological drought based on Standardized Precipitation Index (SPI) recommended by the World Meteorological Organization (WMO) and Consecutive Dry Days (CDD) as one of the extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices were calculated by using (i) quality controlled daily and monthly observational precipitation data from 23 weather stations of various record lengths within 1967-2015 periods, and (ii) 0.05o x 0.05o blended gauge-satellite of daily and monthly precipitation estimates of the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset. Meteorological drought intensity represented by Average Duration of Drought Intensity (ADI) from three-monthly SPI (SPI3) show spatial differences characteristic between eastern and western region. Observed and CHIRPS have relatively similar perspective on meteorological drought intensity over South Sulawesi. Relatively high values of ADI and longest CDD observed mainly over south western part of study area.

  18. Temporal disaggregation of satellite-derived monthly precipitation estimates and the resulting propagation of error in partitioning of water at the land surface

    Directory of Open Access Journals (Sweden)

    S.A. Margulis

    2001-01-01

    Full Text Available Global estimates of precipitation can now be made using data from a combination of geosynchronous and low earth-orbit satellites. However, revisit patterns of polar-orbiting satellites and the need to sample mixed-clouds scenes from geosynchronous satellites leads to the coarsening of the temporal resolution to the monthly scale. There are prohibitive limitations to the applicability of monthly-scale aggregated precipitation estimates in many hydrological applications. The nonlinear and threshold dependencies of surface hydrological processes on precipitation may cause the hydrological response of the surface to vary considerably based on the intermittent temporal structure of the forcing. Therefore, to make the monthly satellite data useful for hydrological applications (i.e. water balance studies, rainfall-runoff modelling, etc., it is necessary to disaggregate the monthly precipitation estimates into shorter time intervals so that they may be used in surface hydrology models. In this study, two simple statistical disaggregation schemes are developed for use with monthly precipitation estimates provided by satellites. The two techniques are shown to perform relatively well in introducing a reasonable temporal structure into the disaggregated time series. An ensemble of disaggregated realisations was routed through two land surface models of varying complexity so that the error propagation that takes place over the course of the month could be characterised. Results suggest that one of the proposed disaggregation schemes can be used in hydrological applications without introducing significant error. Keywords: precipitation, temporal disaggregation, hydrological modelling, error propagation

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

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    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.

  20. How reliable are satellite precipitation estimates for driving hydrological models: a verification study over the Mediterranean area

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    Camici, Stefania; Ciabatta, Luca; Massari, Christian; Brocca, Luca

    2017-04-01

    Floods are one of the most common and dangerous natural hazards, causing every year thousands of casualties and damages worldwide. The main tool for assessing flood risk and reducing damages is represented by hydrologic early warning systems that allow to forecast flood events by using real time data obtained through ground monitoring networks (e.g., raingauges and radars). However, the use of such data, mainly rainfall, presents some issues firstly related to the network density and to the limited spatial representativeness of local measurements. A way to overcome these issues may be the use of satellite-based rainfall products (SRPs) that nowadays are available on a global scale at ever increasing spatial/temporal resolution and accuracy. However, despite the large availability and increased accuracy of SRPs (e.g., the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA); the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF); and the recent Global Precipitation Measurement (GPM) mission), remotely sensed rainfall data are scarcely used in hydrological modeling and only a small number of studies have been carried out to outline some guidelines for using satellite data as input for hydrological modelling. Reasons may be related to: 1) the large bias characterizing satellite precipitation estimates, which is dependent on rainfall intensity and season, 2) the spatial/temporal resolution, 3) the timeliness, which is often insufficient for operational purposes, and 4) a general (often not justified) skepticism of the hydrological community in the use of satellite products for land applications. The objective of this study is to explore the feasibility of using SRPs in a lumped hydrologic model (MISDc, "Modello Idrologico Semi-Distribuito in continuo", Masseroni et al., 2017) over 10 basins in the Mediterranean area with different sizes and physiographic characteristics. Specifically

  1. 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.

  2. Optimizing Satellite-Based Precipitation Estimation for Nowcasting of Rainfall and Flash Flood Events over the South African Domain

    OpenAIRE

    Estelle de Coning

    2013-01-01

    The South African Weather Service is mandated to issue warnings of hazardous weather events, including those related to heavy precipitation, in order to safeguard life and property. Flooding and flash flood events are common in South Africa. Frequent updates and real-time availability of precipitation data are crucial to support hydrometeorological warning services. Satellite rainfall estimation provides a very important data source for flash flood guidance systems as well as nowcasting of pr...

  3. Advantages of using satellite soil moisture estimates over precipitation products to assess regional vegetation water availability and activity

    Science.gov (United States)

    Chen, Tiexi

    2017-04-01

    To improve the understanding of water-vegetation relationships, direct comparative studies assessing the utility of satellite remotely sensed soil moisture, gridded precipitation products, and land surface model output are needed. A case study was investigated for a water-limited, lateral inflow receiving area in northeastern Australia during December 2008 to May 2009. In January 2009, monthly precipitation showed strong positive anomalies, which led to strong positive soil moisture anomalies. The precipitation anomalies disappeared within a month. In contrast, the soil moisture anomalies persisted for months. Positive anomalies of Normalized Difference Vegetation Index (NDVI) appeared in February, in response to water supply, and then persisted for several months. In addition to these temporal characteristics, the spatial patterns of NDVI anomalies were more similar to soil moisture patterns than to those of precipitation and land surface model output. The long memory of soil moisture mainly relates to the presence of clay-rich soils. Modeled soil moisture from four of five global land surface models failed to capture the memory length of soil moisture and all five models failed to present the influence of lateral inflow. This case study indicates that satellite-based soil moisture is a better predictor of vegetation water availability than precipitation in environments having a memory of several months and thus is able to persistently affect vegetation dynamics. These results illustrate the usefulness of satellite remotely sensed soil moisture in ecohydrology studies. This case study has the potential to be used as a benchmark for global land surface model evaluations. The advantages of using satellite remotely sensed soil moisture over gridded precipitation products are mainly expected in lateral-inflow and/or clay-rich regions worldwide.

  4. A probabilistic approach for assessing landslide-triggering event rainfall in Papua New Guinea, using TRMM satellite precipitation estimates

    Science.gov (United States)

    Robbins, J. C.

    2016-10-01

    Large and numerous landslides can result in widespread impacts which are felt particularly strongly in the largely subsistence-orientated communities residing in the most landslide-prone areas of Papua New Guinea (PNG). Understanding the characteristics of rainfall preceding these landslide events is essential for the development of appropriate early warning systems and forecasting models. Relationships between rainfall and landslides are frequently complex and uncertainties tend to be amplified by inconsistent and incomplete landslide catalogues and sparse rainfall data availability. To address some of these uncertainties a modified Bayesian technique has been used, in conjunction with the multiple time frames method, to produce thresholds of landslide probability associated with rainfall events of specific magnitude and duration. Satellite-derived precipitation estimates have been used to derive representative rainfall accumulations and intensities over a range of different rainfall durations (5, 10, 15, 30, 45, 60, 75 and 90 days) for rainfall events which resulted in landslides and those which did not result in landslides. Of the two parameter combinations (accumulation-duration and intensity-duration) analysed, rainfall accumulation and duration provide the best scope for identifying probabilistic thresholds for use in landslide warning and forecasting in PNG. Analysis of historical events and rainfall characteristics indicates that high accumulation (>250 mm), shorter duration (75 days), high accumulation (>1200 mm) rainfall events are more likely to lead to moderate- to high-impact landslides. This analysis has produced the first proxy probability thresholds for landslides in PNG and their application within an early warning framework has been discussed.

  5. Real-Time Global Flood Estimation Using Satellite-Based Precipitation and a Coupled Land Surface and Routing Model

    Science.gov (United States)

    Wu, Huan; Adler, Robert F.; Tian, Yudong; Huffman, George J.; Li, Hongyi; Wang, JianJian

    2014-01-01

    A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50 deg. N - 50 deg. S at relatively high spatial (approximately 12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is approximately 0.9 and the false alarm ratio is approximately 0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30 deg. S - 30 deg. N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.

  6. Improving Quantitative Precipitation Estimation via Data Fusion of High-Resolution Ground-based Radar Network and CMORPH Satellite-based Product

    Science.gov (United States)

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

    2015-12-01

    A large number of precipitation products at multi-scales have been developed based upon satellite, radar, and/or rain gauge observations. However, how to produce optimal rainfall estimation for a given region is still challenging due to the spatial and temporal sampling difference of different sensors. In this study, we develop a data fusion mechanism to improve regional quantitative precipitation estimation (QPE) by utilizing satellite-based CMORPH product, ground radar measurements, as well as numerical model simulations. The CMORPH global precipitation product is essentially derived based on retrievals from passive microwave measurements and infrared observations onboard satellites (Joyce et al. 2004). The fine spatial-temporal resolution of 0.05o Lat/Lon and 30-min is appropriate for regional hydrologic and climate studies. However, it is inadequate for localized hydrometeorological applications such as urban flash flood forecasting. Via fusion of the Regional CMORPH product and local precipitation sensors, the high-resolution QPE performance can be improved. The area of interest is the Dallas-Fort Worth (DFW) Metroplex, which is the largest land-locked metropolitan area in the U.S. In addition to an NWS dual-polarization S-band WSR-88DP radar (i.e., KFWS radar), DFW hosts the high-resolution dual-polarization X-band radar network developed by the center for Collaborative Adaptive Sensing of the Atmosphere (CASA). This talk will present a general framework of precipitation data fusion based on satellite and ground observations. The detailed prototype architecture of using regional rainfall instruments to improve regional CMORPH precipitation product via multi-scale fusion techniques will also be discussed. Particularly, the temporal and spatial fusion algorithms developed for the DFW Metroplex will be described, which utilizes CMORPH product, S-band WSR-88DP, and X-band CASA radar measurements. In order to investigate the uncertainties associated with each

  7. Assessing the potential of satellite-based precipitation estimates for flood frequency analysis in ungauged or poorly gauged tributaries of China's Yangtze River basin

    Science.gov (United States)

    Gao, Zhen; Long, Di; Tang, Guoqiang; Zeng, Chao; Huang, Jiesheng; Hong, Yang

    2017-07-01

    Flood frequency analysis (FFA) is critical for water resources engineering projects, particularly the design of hydraulic structures such as dams and reservoirs. However, it is often difficult to implement FFA in ungauged or poorly gauged basins because of the lack of consistent and long-term records of streamflow observations. The objective of this study was to evaluate the utility of satellite-based precipitation estimates for performing FFA in two presumably ungauged tributaries, the Jialing and Tuojiang Rivers, of the upper Yangtze River. Annual peak flow series were simulated using the Coupled Routing and Excess STorage (CREST) hydrologic model. Flood frequency was estimated by fitting the Pearson type III distribution of both observed and modeled streamflow with historic floods. Comparison of satellite-based precipitation products with a ground-based daily precipitation dataset for the period 2002-2014 reveals that 3B42V7 outperformed 3B42RT. The 3B42V7 product also shows consistent reliability in streamflow simulation and FFA (e.g., relative errors -20%-5% in the Jialing River). The results also indicate that complex terrain, drainage area, and reservoir construction are important factors that impact hydrologic model performance. The larger basin (156,736 km2) is more likely to produce satisfactory results than the small basin (19,613 km2) under similar circumstances (e.g., Jialing/Tuojiang calibrated by 3B42V7 for the calibration period: NSCE = 0.71/0.56). Using the same calibrated parameter sets from the entire Jialing River basin, the 3B42V7/3B42RT-driven hydrologic model performs better for two tributaries of the Jialing River (e.g., for the calibration period, NSCE = 0.71/0.60 in the Qujiang River basin and 0.54/0.38 in the Fujiang River basin) than for the upper mainstem of the Jialing River (NSCE = 0.34/0.32), which has more cascaded reservoirs with all these tributaries treated as ungauged basins for model validation. Overall, this study underscores

  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. Making Satellite Precipitation Data Work for the Developing World

    Science.gov (United States)

    Gebregiorgis, A. S.; Hossain, F.

    2013-12-01

    The traditional approach to measuring precipitation by placing a probe on the ground will likely never be adequate or affordable in most parts of the world. Fortunately, satellites today provide a continuous global bird's-eye view (above ground) at any given location.However, the usefulness of such precipitation products for hydrological applications depends on their error characteristics and how intelligently we can harness the implications of uncertainty for surface hydrology. Satellite precipitation data is most useful where there exists little to none conventional measurements. As a result, the conventional method of comparing satellite estimate against in-situ records to 'harness' the uncertainty is unrealistic and impractical. As a community tasked with the job of making satellite precipitation 'work' for applications in most parts of the world, there is now a need think outside the box. The manuscript aims to describe a method that will 'truly' work in the developing world. The proposed manuscript aims to provide a broad view summary of our work on making hydrologically merged precipitation data work in the Middle East, Europe, Asia and Mediterranean regions. The aim will be to appeal to a broad range of water managers, climate decision makers and policy and planners in the developing world. The merged precipitation data has already been created for 2002-2010 and will be made freely available to BAMS readers through our ftp site. Globally selected study regions for developing and validating error variance regression model and satellite rainfall products merging scheme

  10. A hybrid framework for verification of satellite precipitation products

    Science.gov (United States)

    Li, J.; Hsu, K.; AghaKouchak, A.; Sorooshian, S.

    2011-12-01

    Advances in satellite technology have led to the development of many remote-sensing algorithms to estimate precipitation at quasi-global scales. A number of satellite precipitation products are provided at high spatial and temporal resolutions that are suitable for short-term hydrologic applications. Several coordinated validation activities have been established to evaluate the accuracy of satellite precipitation. Traditional verification measures summarize pixel-to-pixel differences between observation and estimates. Object-based verification methods, however, extend pixel based validation to address errors related to spatial patterns and storm structure, such as the shape, volume, and distribution of precipitation rain-objects. In this investigation, a 2D watershed segmentation technique is used to identify rain storm objects and is further adopted in a hybrid verification framework to diagnose the storm-scale rainfall objects from both satellite-based precipitation estimates and ground observations (radar estimates). Five key scores are identified in the objective-based verification framework, including false alarm ratio, missing ratio, maximum of total interest, equal weight and weighted summation of total interest. These scores indicate the performance of satellite estimates with features extracted from the segmented storm objects. The proposed object-based verification framework was used to evaluate PERSIANN, PERSIANN-CCS, CMORPH, 3B42RT against NOAA stage IV MPE multi-sensor composite rain analysis. All estimates are evaluated at 0.25°x0.25° daily-scale in summer 2008 over the continental United States (CONUS). The five final scores for each precipitation product are compared with the median of maximum interest (MMI) of the Method for Object-Based Diagnostic Evaluation (MODE). The results show PERSIANN and CMORPH outperform 3B42RT and PERSIANN-CCS. Different satellite products presented distinct features of precipitation. For example, the sizes of

  11. Investigating Satellite Microwave observations of Precipitation in Different Climate Regimes

    Science.gov (United States)

    Wang, N.; Ferraro, R. R.

    2013-12-01

    Microwave satellite remote sensing of precipitation over land is a challenging problem due to the highly variable land surface emissivity, which, if not properly accounted for, can be much greater than the precipitation signal itself, especially in light rain/snow conditions. Additionally, surfaces such as arid land, deserts and snow cover have brightness temperature characteristics similar to precipitation Ongoing work by GPM microwave radiometer team is constructing databases through a variety of means, however, there is much uncertainty as to what is the optimal information needed for the wide array of sensors in the GPM constellation, including examination of regional conditions. The original data sets will focus on stratification by emissivity class, surface temperature and total perceptible water. We'll perform sensitivity studies to determine the potential role of ancillary data (e.g., land surface temperature, snow cover/water equivalent, etc.) to improve precipitation estimation over land in different climate regimes, including rain and snow. In other words, what information outside of the radiances can help describe the background and subsequent departures from it that are active precipitating regions? It is likely that this information will be a function of the various precipitation regimes. Statistical methods such as Principal Component Analysis (PCA) will be utilized in this task. Databases from a variety of sources are being constructed. They include existing satellite microwave measurements of precipitating and non-precipitating conditions, ground radar precipitation rate estimates, surface emissivity climatology from satellites, surface temperature and TPW from NWP reanalysis. Results from the analysis of these databases with respect to the microwave precipitation sensitivity to the variety of environmental conditions in different climate regimes will be discussed.

  12. Evaluating cloud precipitation efficiency with satellite retrievals of water isotopologues

    Science.gov (United States)

    Bailey, A.; Noone, D. C.; Wood, R.

    2015-12-01

    The efficiency with which clouds precipitate is believed to influence climate by modifying cloud lifetime and, ultimately, cloud amount. Aerosols can influence this linkage by reducing the effective radii of cloud droplets and suppressing precipitation. This relationship, however, is not unidirectional. Cloud precipitation efficiency can also regulate particle concentrations, since precipitation effectively scavenges aerosols from the atmosphere. One challenge in studying how aerosols, clouds, and precipitation processes interrelate is that observational constraints are difficult to attain. This work evaluates the ability of isotope ratios in water vapor to quantify cloud precipitation efficiency across the tropical and subtropical oceans. Theory suggests isotope ratios will record the precipitation efficiency of a convective plume, since heavier isotopologues precipitate preferentially; and a recent analysis of in situ measurements from the Mauna Loa Observatory (MLO, Hawaii, USA) verifies this to be the case. The challenge now lies in understanding whether satellite retrievals of isotope ratios in water vapor are sensitive enough to track precipitation efficiency globally. To answer this question, vertical profiles of the D/H ratio derived from NASA's Tropospheric Emission Spectrometer (TES) are first compared with the MLO in situ measurements. A qualitative match indicates the satellite retrievals can distinguish high from low precipitation efficiency convection. To expand the analysis geographically, TES profiles between 40°S and 40°N are compared with estimates of precipitation efficiency derived from the Tropical Rainfall Measuring Mission (TRMM) and ECMWF's ERA-Interim. Retrievals are binned by lower-tropospheric humidity and by vertical velocity in order to minimize large-scale thermodynamical influences. Co-located cloud retrievals provide the context necessary to evaluate the utility of these new estimates in elucidating cloud feedbacks on climate.

  13. A global satellite-assisted precipitation climatology

    Science.gov (United States)

    Funk, C.; Verdin, A.; Michaelsen, J.; Peterson, P.; Pedreros, D.; Husak, G.

    2015-10-01

    Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high-resolution (0.05°) global precipitation climatologies that perform reasonably well in data-sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate

  14. A global satellite assisted precipitation climatology

    Directory of Open Access Journals (Sweden)

    C. Funk

    2015-05-01

    Full Text Available Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high resolution (0.05° global precipitation climatologies that perform reasonably well in data sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology

  15. A global satellite assisted precipitation climatology

    Science.gov (United States)

    Funk, Christopher C.; Verdin, Andrew P.; Michaelsen, Joel C.; Pedreros, Diego; Husak, Gregory J.; Peterson, P.

    2015-01-01

    Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high resolution (0.05°) global precipitation climatologies that perform reasonably well in data sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate

  16. Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

    Science.gov (United States)

    Sunilkumar, K.; Narayana Rao, T.; Satheeshkumar, S.

    2016-05-01

    This paper describes the establishment of a dense rain gauge network and small-scale variability in rain events (both in space and time) over a complex hilly terrain in Southeast India. Three years of high-resolution gauge measurements are used to validate 3-hourly rainfall and sub-daily variations of four widely used multi-satellite precipitation estimates (MPEs). The network, established as part of the Megha-Tropiques validation program, consists of 36 rain gauges arranged in a near-square grid area of 50 km × 50 km with an intergauge distance of 6-12 km. Morphological features of rainfall in two principal rainy seasons (southwest monsoon, SWM, and northeast monsoon, NEM) show marked differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale/long-lived systems (during wet spells), whereas the contribution from small-scale/short-lived systems is considerable during the SWM. Rain events with longer duration and copious rainfall are seen mostly in the western quadrants (a quadrant is 1/4 of the study region) in the SWM and northern quadrants in the NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits large spatial and seasonal variability with larger diurnal amplitudes at all the gauge locations (except for 1) during the SWM and smaller and insignificant diurnal amplitudes at many gauge locations during the NEM. On average, the diurnal amplitudes are a factor of 2 larger in the SWM than in the NEM. The 24 h harmonic explains about 70 % of total variance in the SWM and only ˜ 30 % in the NEM. During the SWM, the rainfall peak is observed between 20:00 and 02:00 IST (Indian Standard Time) and is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the

  17. River Forecasting Center Quantitative Precipitation Estimate Archive

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Radar indicated-rain gage verified and corrected hourly precipitation estimate on a corrected ~4km HRAP grid. This archive contains hourly estimates of precipitation...

  18. 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).

  19. Precipitation sensitivity to warming estimated from long island records

    Science.gov (United States)

    Polson, D.; Hegerl, G. C.; Solomon, S.

    2016-07-01

    Some of the most damaging impacts of climate change are a consequence of changes to the global water cycle. Atmospheric warming causes the water cycle to intensify, increasing both atmospheric water vapor concentrations and global precipitation and enhancing existing patterns of precipitation minus evaporation (P - E). This relationship between temperature and precipitation therefore makes understanding how precipitation has changed with global temperatures in the past crucial for projecting changes with future warming. In situ observations cannot readily estimate global precipitation sensitivity to temperature (dP/dT), as land precipitation changes are affected by water limitation. Satellite observations of precipitation over ocean are only available after 1979, but studies based on them suggest a precipitation sensitivity over wet tropical (30N-30S) oceans that exceeds the Clausius-Clapeyron value. Here, we determine for the first time precipitation sensitivity using longer (1930-2005), island-based in situ observations to estimate dP/dT over islands. The records show a robust pattern of increasing precipitation in the tropics and decreasing precipitation in the subtropics, as predicted from physical arguments, and heavy precipitation shows a stronger sensitivity than mean precipitation over many islands. The pattern and magnitude of island-based dP/dT agree with climate models if masked to island locations, supporting model predictions of future changes.

  20. Advances in Satellite Microwave Precipitation Retrieval Algorithms Over Land

    Science.gov (United States)

    Wang, N. Y.; You, Y.; Ferraro, R. R.

    2015-12-01

    Precipitation plays a key role in the earth's climate system, particularly in the aspect of its water and energy balance. Satellite microwave (MW) observations of precipitation provide a viable mean to achieve global measurement of precipitation with sufficient sampling density and accuracy. However, accurate precipitation information over land from satellite MW is a challenging problem. The Goddard Profiling Algorithm (GPROF) algorithm for the Global Precipitation Measurement (GPM) is built around the Bayesian formulation (Evans et al., 1995; Kummerow et al., 1996). GPROF uses the likelihood function and the prior probability distribution function to calculate the expected value of precipitation rate, given the observed brightness temperatures. It is particularly convenient to draw samples from a prior PDF from a predefined database of observations or models. GPROF algorithm does not search all database entries but only the subset thought to correspond to the actual observation. The GPM GPROF V1 database focuses on stratification by surface emissivity class, land surface temperature and total precipitable water. However, there is much uncertainty as to what is the optimal information needed to subset the database for different conditions. To this end, we conduct a database stratification study of using National Mosaic and Multi-Sensor Quantitative Precipitation Estimation, Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Technology Microwave Sounder (ATMS) and reanalysis data from Modern-Era Retrospective Analysis for Research and Applications (MERRA). Our database study (You et al., 2015) shows that environmental factors such as surface elevation, relative humidity, and storm vertical structure and height, and ice thickness can help in stratifying a single large database to smaller and more homogeneous subsets, in which the surface condition and precipitation vertical profiles are similar. It is found that the probability of detection (POD) increases

  1. A Generalized Statistical Uncertainty Model for Satellite Precipitation Products

    Science.gov (United States)

    Sarachi, S.

    2013-12-01

    A mixture model of Generalized Normal Distribution and Gamma distribution (GND-G) is used to model the joint probability distribution of satellite-based and stage IV radar rainfall under a given spatial and temporal resolution (e.g. 1°x1° and daily rainfall). The distribution parameters of GND-G are extended across various rainfall rates and spatial and temporal resolutions. In the study, GND-G is used to describe the uncertainty of the estimates from Precipitation Estimation from Remote Sensing Information using Artificial Neural Network algorithm (PERSIANN). The stage IV-based multi-sensor precipitation estimates (MPE) are used as reference measurements .The study area for constructing the uncertainty model covers a 15°×15°box of 0.25°×0.25° cells over the eastern United States for summer 2004 to 2009. Cells are aggregated in space and time to obtain data with different resolutions for the construction of the model's parameter space. Result shows that comparing to the other statistical uncertainty models, GND-G fits better than the other models, such as Gaussian and Gamma distributions, to the reference precipitation data. The impact of precipitation uncertainty to the stream flow is further demonstrated by Monte Carlo simulation of precipitation forcing in the hydrologic model. The NWS DMIP2 basins over Illinois River basin south of Siloam is selected in this case study. The data covers the time period of 2006 to 2008.The uncertainty range of stream flow from precipitation of GND-G distributions calculated and will be discussed.

  2. Utilization of Precipitation and Moisture Products Derived from Satellites to Support NOAA Operational Precipitation Forecasts

    Science.gov (United States)

    Ferraro, R.; Zhao, L.; Kuligowski, R. J.; Kusselson, S.; Ma, L.; Kidder, S. Q.; Forsythe, J. M.; Jones, A. S.; Ebert, E. E.; Valenti, E.

    2012-12-01

    NOAA/NESDIS operates a constellation of polar and geostationary orbiting satellites to support weather forecasts and to monitor the climate. Additionally, NOAA utilizes satellite assets from other U.S. agencies like NASA and the Department of Defense, as well as those from other nations with similar weather and climate responsibilities (i.e., EUMETSAT and JMA). Over the past two decades, through joint efforts between U.S. and international government researchers, academic partners, and private sector corporations, a series of "value added" products have been developed to better serve the needs of weather forecasters and to exploit the full potential of precipitation and moisture products generated from these satellites. In this presentation, we will focus on two of these products - Ensemble Tropical Rainfall Potential (eTRaP) and Blended Total Precipitable Water (bTPW) - and provide examples on how they contribute to hydrometeorological forecasts. In terms of passive microwave satellite products, TPW perhaps is most widely used to support real-time forecasting applications, as it accurately depicts tropospheric water vapor and its movement. In particular, it has proven to be extremely useful in determining the location, timing, and duration of "atmospheric rivers" which contribute to and sustain flooding events. A multi-sensor approach has been developed and implemented at NESDIS in which passive microwave estimates from multiple satellites and sensors are merged to create a seamless, bTPW product that is more efficient for forecasters to use. Additionally, this product is being enhanced for utilization for television weather forecasters. Examples will be shown to illustrate the roll of atmospheric rivers and contribution to flooding events, and how the bTPW product was used to improve the forecast of these events. Heavy rains associated with land falling tropical cyclones (TC) frequently trigger floods that cause millions of dollars of damage and tremendous loss

  3. Evaluation of extreme precipitation estimates from TRMM in Angola

    Science.gov (United States)

    Pombo, Sandra; de Oliveira, Rodrigo Proença

    2015-04-01

    In situ ground observation measurement of precipitation is difficult in vast and sparsely populated areas, with poor road networks. This paper examines the use of remote sensors installed in satellites and evaluates the accuracy of TRMM 3B42 annual maximum daily precipitation estimates in Angola, in West Africa, a region where ground monitoring networks are generally. TRMM 3B42 estimates of annual maximum daily precipitation are compared to ground observation data from 159 locations. As a direct comparison between the two datasets for a common specific period and sites is not possible, a statistical approach was adopted to test the hypothesis that the TRMM 3B42 estimates and the ground monitoring records exhibit similar statistical characteristics. The study shows that the annual maximum daily precipitation estimates obtained from TRMM 3B42 slightly underestimate the quantiles obtained from the in situ observations. The use of remote sensing products to estimate extreme precipitation values for engineering design purposes is however promising. A maximum daily precipitation map for a return period of 20 years was computed and in the future, as the length of the remote sensing data series increases, it may be possible to estimate annual maximum daily precipitation estimates exclusively from these datasets for larger return periods. The paper also presents maps of the PdT/PDT ratios, where PdT is the annual maximum precipitation for a duration d and a return period of T years, and PDT is the annual maximum daily precipitation for a return period of T years. In conjunction with these maps it is possible to estimate the maximum precipitation for durations between 3 h and 5 days.

  4. 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.

  5. Utilizing Satellite-derived Precipitation Products in Hydrometeorological Applications

    Science.gov (United States)

    Liu, Z.; Ostrenga, D.; Teng, W. L.; Kempler, S. J.; Huffman, G. J.

    2012-12-01

    Each year droughts and floods happen around the world and can cause severe property damages and human casualties. Accurate measurement and forecast are important for preparedness and mitigation efforts. Through multi-satellite blended techniques, significant progress has been made over the past decade in satellite-based precipitation product development, such as, products' spatial and temporal resolutions as well as timely availability. These new products are widely used in various research and applications. In particular, the TRMM Multi-satellite Precipitation Analysis (TMPA) products archived and distributed by the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) provide 3-hourly, daily and monthly near-global (50° N - 50° S) precipitation datasets for research and applications. Two versions of TMPA products are available, research (3B42, 3B43, rain gauge adjusted) and near-real-time (3B42RT). At GES DISC, we have developed precipitation data services to support hydrometeorological applications in order to maximize the TRMM mission's societal benefits. In this presentation, we will present examples of utilizing TMPA precipitation products in hydrometeorological applications including: 1) monitoring global floods and droughts; 2) providing data services to support the USDA Crop Explorer; 3) support hurricane monitoring activities and research; and 4) retrospective analog year analyses to improve USDA's world agricultural supply and demand estimates. We will also present precipitation data services that can be used to support hydrometeorological applications including: 1) User friendly TRMM Online Visualization and Analysis System (TOVAS; URL: http://disc2.nascom.nasa.gov/Giovanni/tovas/); 2) Mirador (http://mirador.gsfc.nasa.gov/), a simplified interface for searching, browsing, and ordering Earth science data at GES DISC; 3) Simple Subset Wizard (http://disc.sci.gsfc.nasa.gov/SSW/ ) for data subsetting and format conversion; 4) Data

  6. A Bayesian kriging approach for blending satellite and ground precipitation observations

    Science.gov (United States)

    Verdin, Andrew; Rajagopalan, Balaji; Kleiber, William; Funk, Chris

    2015-02-01

    Drought and flood management practices require accurate estimates of precipitation. Gauge observations, however, are often sparse in regions with complicated terrain, clustered in valleys, and of poor quality. Consequently, the spatial extent of wet events is poorly represented. Satellite-derived precipitation data are an attractive alternative, though they tend to underestimate the magnitude of wet events due to their dependency on retrieval algorithms and the indirect relationship between satellite infrared observations and precipitation intensities. Here we offer a Bayesian kriging approach for blending precipitation gauge data and the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates for Central America, Colombia, and Venezuela. First, the gauge observations are modeled as a linear function of satellite-derived estimates and any number of other variables—for this research we include elevation. Prior distributions are defined for all model parameters and the posterior distributions are obtained simultaneously via Markov chain Monte Carlo sampling. The posterior distributions of these parameters are required for spatial estimation, and thus are obtained prior to implementing the spatial kriging model. This functional framework is applied to model parameters obtained by sampling from the posterior distributions, and the residuals of the linear model are subject to a spatial kriging model. Consequently, the posterior distributions and uncertainties of the blended precipitation estimates are obtained. We demonstrate this method by applying it to pentadal and monthly total precipitation fields during 2009. The model's performance and its inherent ability to capture wet events are investigated. We show that this blending method significantly improves upon the satellite-derived estimates and is also competitive in its ability to represent wet events. This procedure also provides a means to estimate a full conditional distribution

  7. Tropical convective systems life cycle characteristics from geostationary satellite and precipitating estimates derived from TRMM and ground weather radar observations for the West African and South American regions

    Science.gov (United States)

    Fiolleau, T.; Roca, R.; Angelis, F. C.; Viltard, N.

    2012-12-01

    In the tropics most of the rainfall comes in the form of individual storm events embedded in the synoptic circulations (e.g., monsoons). Understanding the rainfall and its variability hence requires to document these highly contributing tropical convective systems (MCS). Our knowledge of the MCS life cycle, from a physical point of view mainly arises from individual observational campaigns heavily based on ground radar observations. While this large part of observations enabled the creation of conceptual models of MCS life cycle, it nevertheless does not reach any statistically significant integrated perspective yet. To overcome this limitation, a composite technique, that will serve as a Day-1 algorithm for the Megha-Tropiques mission, is considered in this study. this method is based on a collocation in space and time of the level-2 rainfall estimates (BRAIN) derived from the TMI radiometer onboard TRMM with the cloud systems identified by a new MCS tracking algorithm called TOOCAN and based on a 3-dimensional segmentation (image + time) of the geostationary IR imagery. To complete this study, a similar method is also developed collocating the cloud systems with the precipitating features derived from the ground weather radar which has been deployed during the CHUVA campaign over several Brazilian regions from 2010 up to now. A comparison of the MCSs life cycle is then performed for the 2010-2012 summer seasons over the West African, and South American regions. On the whole region of study, the results show that the temporal evolution of the cold cloud shield associated to MCSs describes a symmetry between the growth and the decay phases. It is also shown that the parameters of the conceptual model of MCSs are strongly correlated, reducing thereby the problem to a single degree of freedom. At the system scale, over both land and oceanic regions, rainfall is described by an increase at the beginning (the first third) of the life cycle and then smoothly decreases

  8. Next-Generation Satellite Precipitation Products for Understanding Global and Regional Water Variability

    Science.gov (United States)

    Hou, Arthur Y.

    2011-01-01

    A major challenge in understanding the space-time variability of continental water fluxes is the lack of accurate precipitation estimates over complex terrains. While satellite precipitation observations can be used to complement ground-based data to obtain improved estimates, space-based and ground-based estimates come with their own sets of uncertainties, which must be understood and characterized. Quantitative estimation of uncertainties in these products also provides a necessary foundation for merging satellite and ground-based precipitation measurements within a rigorous statistical framework. Global Precipitation Measurement (GPM) is an international satellite mission that will provide next-generation global precipitation data products for research and applications. It consists of a constellation of microwave sensors provided by NASA, JAXA, CNES, ISRO, EUMETSAT, DOD, NOAA, NPP, and JPSS. At the heart of the mission is the GPM Core Observatory provided by NASA and JAXA to be launched in 2013. The GPM Core, which will carry the first space-borne dual-frequency radar and a state-of-the-art multi-frequency radiometer, is designed to set new reference standards for precipitation measurements from space, which can then be used to unify and refine precipitation retrievals from all constellation sensors. The next-generation constellation-based satellite precipitation estimates will be characterized by intercalibrated radiometric measurements and physical-based retrievals using a common observation-derived hydrometeor database. For pre-launch algorithm development and post-launch product evaluation, NASA supports an extensive ground validation (GV) program in cooperation with domestic and international partners to improve (1) physics of remote-sensing algorithms through a series of focused field campaigns, (2) characterization of uncertainties in satellite and ground-based precipitation products over selected GV testbeds, and (3) modeling of atmospheric processes and

  9. Depiction of global drought by reanalysis and real-time satellite precipitation products

    Science.gov (United States)

    Wood, Eric; Zhan, Wang

    2017-04-01

    Reanalysis precipitation is routinely used as a surrogate of observations due to its high spatial and temporal resolution and global coverage, and thus widely used in hydrologic and agricultural applications. The resultant product is largely dependent on the accuracy of reanalysis precipitation datasets. With advances in satellite remote sensing technology, the latest generation of reanalysis systems starts to include real time satellite precipitation estimates as inputs to their assimilation system. In this presentation, reanalysis precipitations datasets and real-time satellite rainfall products are used for the depiction of global drought events by comparing them against an observational reference dataset, namely the Princeton Global Forcing (PGF) dataset, during the period of March 2000 to December 2012. The selected reanalyses are the Climate Forecast System Reanalysis (CFSR), ERA-Interim, and the Modern-Era Retrospective Analysis for Research and Applications, version 1 (MERRA) and 2 (MERRA-2). Three real-time satellite precipitation estimates; namely the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42RT, the Climate Prediction Center (CPC) morphing algorithm (CMORPH) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) are included in the study. Our results show that all datasets depict Sub-Saharan African drought events with limited skill, as opposed to mid latitude regions. Reanalyses and satellite real-time precipitation datasets have comparative skill in the low latitudes. Specific drought events are analyzed that demonstrate the drought depiction from the various datasets. In North America, Asia and Europe, drought events are better replicated and inter-dataset variability is significantly smaller. Overall, temporal characteristics of identified drought events are better estimated than their spatial extent.

  10. 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.

  11. Evaluation of Satellite and Ground Based Precipitation Products for Flood Forecasting

    Science.gov (United States)

    Chintalapudi, S.; Sharif, H.; Yeggina, S.

    2012-04-01

    The development in satellite-derived rainfall estimates encouraged the hydrological modeling in sparse gauged basins or ungauged basins. Especially, physically-based distributed hydrological models can benefit from the good spatial and temporal coverage of satellite precipitation products. In this study, three satellite derived precipitation datasets (TRMM, CMORPH, and PERSIANN), NEXRAD, and rain gauge precipitation datasets were used to drive the hydrological model. The physically-based, distributed hydrological model Gridded Surface Subsurface Hydrological Analysis (GSSHA) was used in this study. Focus will be on the results from the Guadalupe River Basin above Canyon Lake and below Comfort, Texas. The Guadalupe River Basin above Canyon Lake and below Comfort Texas drains an area of 1232 km2. Different storm events will be used in these simulations. August 2007 event was used as calibration and June 2007 event was used as validation. Results are discussed interms of accuracy of satellite precipitation estimates with the ground based precipitation estimates, predicting peak discharges, runoff volumes, time lag, and spatial distribution. The initial results showed that, model was able to predict the peak discharges and runoff volumes when using NEXRAD MPE data, and TRMM 3B42 precipitation product. The results also showed that there was time lag in hydrographs driven by both PERSIANN and CMORPH data sets.

  12. 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.

  13. Microwave retrievals of terrestrial precipitation over snow-covered surfaces: A lesson from the GPM satellite

    Science.gov (United States)

    Ebtehaj, A. M.; Kummerow, C. D.

    2017-06-01

    Satellites are playing an ever-increasing role in estimating precipitation over remote areas. Improving satellite retrievals of precipitation requires increased understanding of its passive microwave signatures over different land surfaces. Snow-covered surfaces are notoriously difficult to interpret because they exhibit both emission from the land below and scattering from the ice crystals. Using data from the Global Precipitation Measurement (GPM) satellite, we demonstrate that microwave brightness temperatures of rain and snowfall transition from a scattering to an emission regime from summer to winter, due to expansion of less emissive snow cover. Evidence suggests that the combination of low- (10-19 GHz) and high-frequency (89-166 GHz) channels provides the maximum amount of information for snowfall detection. The results demonstrate that, using a multifrequency matching method, the probability of snowfall detection can even be higher than rainfall—chiefly because of the information content of the low-frequency channels that respond to the (near) surface temperature.

  14. Heavy precipitation retrieval from combined satellite observations and ground-based lightning measurements

    Science.gov (United States)

    Mugnai, A.; Dietrich, S.; Casella, D.; di Paola, F.; Formenton, M.; Sanò, P.

    2010-09-01

    We have developed a series of algorithms for the retrieval of precipitation (especially, heavy precipitation) over the Mediterranean area using satellite observations from the available microwave (MW) radiometers onboard low Earth orbit (LEO) satellites and from the visible-infrared (VIS-IR) SEVIRI radiometer onboard the European geosynchronous (GEO) satellite Meteosat Second Generation (MSG), in conjunction with lightning data from ground-based networks - such as ZEUS and LINET. These are: • A new approach for precipitation retrieval from space (which we call the Cloud Dynamics and Radiation Database approach, CDRD) that incorporates lightning and environmental/dynamical information in addition to the upwelling microwave brightness temperatures (TB’s) so as to reduce the retrieval uncertainty and improve the retrieval performance; • A new combined MW-IR technique for producing frequent precipitation retrievals from space (which we call PM-GCD technique), that uses passive-microwave (PM) retrievals in conjunction with lightning information and the Global Convection Detection (GCD) technique to discriminate deep convective clouds within the GEO observations; • A new morphing approach (which we call the Lightning-based Precipitation Evolving Technique, L-PET) that uses the available lightning measurements for propagating the rainfall estimates from satellite-borne MW radiometers to a much higher time resolution than the MW observations. We will present and discuss our combined MW/IR/lightning precipitation algorithms and analyses with special reference to some case studies over the western Mediterranean.

  15. Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

    Science.gov (United States)

    Zambrano, Francisco; Wardlow, Brian; Tadesse, Tsegaye

    2016-10-01

    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 (+30 years), 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 evaluate their applicability for agricultural drought evaluation 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-based precipitation estimates. 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-based estimates. Nine statistics were used to evaluate the performance of satellite products to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to

  16. Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia

    Directory of Open Access Journals (Sweden)

    Hao Guo

    2015-06-01

    Full Text Available This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA, Climate Prediction Center morphing technique (CMORPH, Global Satellite Mapping of Precipitation (GSMaP and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN. All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB (128.17% while GSMaP_Gauge shows consistent high correlation coefficient (CC (>0.8 but RB fluctuates between −57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67. Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%, CSI (less than 45% and relatively high FAR (more than 35%.

  17. Simulation of Ship-Track versus Satellite-Sensor Differences in Oceanic Precipitation Using an Island-Based Radar

    Directory of Open Access Journals (Sweden)

    Jörg Burdanowitz

    2017-06-01

    Full Text Available The point-to-area problem strongly complicates the validation of satellite-based precipitation estimates, using surface-based point measurements. We simulate the limited spatial representation of light-to-moderate oceanic precipitation rates along ship tracks with respect to areal passive microwave satellite estimates using data from a subtropical island-based radar. The radar data serves to estimate the discrepancy between point-like and areal precipitation measurements. From the spatial discrepancy, two statistical adjustments are derived so that along-track precipitation ship data better represent areal precipitation estimates from satellite sensors. The first statistical adjustment uses the average duration of a precipitation event as seen along a ship track, and the second adjustment uses the median-normalized along-track precipitation rate. Both statistical adjustments combined reduce the root mean squared error by 0.24 mm h − 1 (55% compared to the unadjusted average track of 60 radar pixels in length corresponding to a typical ship speed of 24–34 km h − 1 depending on track orientation. Beyond along-track averaging, the statistical adjustments represent an important step towards a more accurate validation of precipitation derived from passive microwave satellite sensors using point-like along-track surface precipitation reference data.

  18. Improving High-resolution Spatial Estimates of Precipitation in the Equatorial Americas

    Science.gov (United States)

    Verdin, A.; Rajagopalan, B.; Funk, C. C.

    2013-12-01

    Drought and flood management practices require accurate estimates of precipitation in space and time. However, data is sparse in regions with complicated terrain (such as the Equatorial Americas), often in valleys (where people farm), and of poor quality. Consequently, extreme precipitation events are poorly represented. Satellite-derived rainfall data is an attractive alternative in such regions and is being widely used, though it too suffers from problems such as underestimation of extreme events (due to its dependency on retrieval algorithms) and the indirect relationship between satellite radiation observations and precipitation intensities. Thus, it seems appropriate to blend satellite-derived rainfall data of extensive spatial coverage with rain gauge data in order to provide a more robust estimate of precipitation. To this end, in this research we offer three techniques to blend rain gauge data and the Climate Hazards group InfraRed Precipitation (CHIRP) satellite-derived precipitation estimate for Central America and Colombia. In the first two methods, the gauge data is assigned to the closest CHIRP grid point, where the error is defined as r = Yobs - Ysat. The spatial structure of r is then modeled using physiographic information (Easting, Northing, and Elevation) by two methods (i) a traditional Cokriging approach whose variogram is calculated in Euclidean space and (ii) a nonparametric method based on local polynomial functional estimation. The models are used to estimate r at all grid points, which is then added to the CHIRP, thus creating an improved satellite estimate. We demonstrate these methods by applying them to pentadal and monthly total precipitation fields during 2009. The models' predictive abilities and their ability to capture extremes are investigated. These blending methods significantly improve upon the satellite-derived estimates and are also competitive in their ability to capture extreme precipitation. The above methods assume

  19. Uncertainty Estimation of Global Precipitation Measurement through Objective Validation Strategy

    Science.gov (United States)

    KIM, H.; Utsumi, N.; Seto, S.; Oki, T.

    2014-12-01

    Since Tropical Rainfall Measuring Mission (TRMM) has been launched in 1997 as the first satellite mission dedicated to measuring precipitation, the spatiotemporal gaps of precipitation observation have been filled significantly. On February 27th, 2014, Dual-frequency Precipitation Radar (DPR) satellite has been launched as a core observatory of Global Precipitation Measurement (GPM), an international multi-satellite mission aiming to provide the global three hourly map of rainfall and snowfall. In addition to Ku-band, Ka-band radar is newly equipped, and their combination is expected to introduce higher precision than the precipitation measurement of TRMM/PR. In this study, the GPM level-2 orbit products are evaluated comparing to various precipitation observations which include TRMM/PR, in-situ data, and ground radar. In the preliminary validation over intercross orbits of DPR and TRMM, Ku-band measurements in both satellites shows very close spatial pattern and intensity, and the DPR is capable to capture broader range of precipitation intensity than of the TRMM. Furthermore, we suggest a validation strategy based on 'objective classification' of background atmospheric mechanisms. The Japanese 55-year Reanalysis (JRA-55) and auxiliary datasets (e.g., tropical cyclone best track) is used to objectively determine the types of precipitation. Uncertainty of abovementioned precipitation products is quantified as their relative differences and characterized for different precipitation mechanism. Also, it is discussed how the uncertainty affects the synthesis of TRMM and GPM for a long-term satellite precipitation observation records which is internally consistent.

  20. 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.

    2016-09-01

    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 ˜100 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.

  1. 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.

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

    Science.gov (United States)

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

    2010-05-01

    As accuracy of satellite precipitation estimates improves and observation frequency increases, application of those data to societal benefit areas, such as weather forecasts and flood predictions, is expected, in addition to research of precipitation climatology to analyze precipitation systems. There is, however, limitation on single satellite observation in coverage and frequency. Currently, the Global Precipitation Measurement (GPM) mission is scheduled under international collaboration to fulfill various user requirements that cannot be achieved by the single satellite, like the Tropical Rainfall Measurement Mission (TRMM). The GPM mission is an international mission to achieve high-accurate and high-frequent rainfall observation over a global area. GPM is composed of a TRMM-like non-sun-synchronous orbit satellite (GPM core satellite) and constellation of satellites carrying microwave radiometer instruments. The GPM core satellite carries the Dual-frequency Precipitation Radar (DPR), which is being developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT), and microwave radiometer provided by the National Aeronautics and Space Administration (NASA). Development of DPR instrument is in good progress for scheduled launch in 2013, and DPR Critical Design Review has completed in July - September 2009. Constellation satellites, which carry a microwave imager and/or sounder, are planned to be launched around 2013 by each partner agency for its own purpose, and will contribute to extending coverage and increasing frequency. JAXA's future mission, the Global Change Observation Mission (GCOM) - Water (GCOM-W) satellite will be one of constellation satellites. The first generation of GCOM-W satellite is scheduled to be launched in 2011, and it carries the Advanced Microwave Scanning Radiometer 2 (AMSR2), which is being developed based on the experience of the AMSR-E on EOS Aqua satellite

  3. Object-based Evaluation of Satellite Precipitation Retrievals: A Case Study of the Summer Season over CONUS

    Science.gov (United States)

    Li, J.; Xu, P.

    2015-12-01

    Satellite precipitation retrievals that have high spatial and temporal resolutions are suitable for various applications, such as hydrologic modeling and watershed management. Many validation studies have been established to understand the strengths and limitations of these satellite precipitation retrievals. In this study, an object-based validation approach is adopted to evaluate several satellite precipitation retrievals focusing on the spatial and geometric patterns of precipitation. This object-based validation approach identifies precipitation objects using an image processing technique referred to as watershed transform. Several object attributes are diagnosed and analyzed based on the distance measurement. Three object-based verification scores are summarized to determine the overall performances of satellite precipitation retrievals. The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) were evaluated using the object-based approach. The NOAA stage IV MPE multi-sensor composite rain analysis was utilized as the ground observations. The comparative assessments were conducted at 0.25° by 0.25° on a daily scale in the summer season of 2014 over the continental United States (CONUS). The results suggest that IMERG possesses the similar spatial pattern of local-scale precipitation areas against stage IV observations. In addition, IMERG depicts the sizes and locations of precipitation areas more accurately against stage IV.

  4. Precipitation retrieval from satellite within EUMETSAT's H-SAF

    Science.gov (United States)

    Mugnai, A.; Dietrich, S.; Levizzani, V.; Casella, D.; Cattani, E.; di Paola, F.; Formenton, M.; Laviola, S.; Sanò, P.

    2010-09-01

    The EUMETSAT Satellite Application Facility on support to Operational Hydrology and Water Management (H-SAF) was established by the EUMETSAT Council on July 3, 2005 and started activity at the official date of September 1, 2005. The Italian Meteorological Service serves as "Host Institute" on behalf of 12 European countries. The Project Plan focuses on the generation of the following products for the European and Mediterranean regions: • instantaneous and accumulated precipitation, including liquid/solid discrimination; • soil moisture in the surface layer and in the roots region; • snow parameters such as effective cover, wet/dry discrimination and water equivalent. In addition to products development and generation, the project includes a products validation programme and a hydrological validation programme. The development programme duration is 5 years, ending on August 31, 2010. A follow-on Continuous Development and Operations Phase (CDOP) will start in September 2010 to provide long-term perspective (2010-2017) to the initiative. Precipitation products are being generated according to algorithms developed by CNR-ISAC in collaboration with the international community, by exploiting the following satellites and instruments: • MW conically-scanning radiometers (SSM/I and SSMIS) on LEO satellites (DMSP); • MW cross-track scanning radiometers (AMSU-A and AMSU-B / MHS) on LEO operational satellites (NOAA and MetOp); • VIS/IR imagers (SEVIRI) on GEO satellites (MSG). These products are generated routinely at the Italian Centro Nazionale di Meteorologia e Climatologia Aeronautica (CNMCA), which is responsible of operational product generation and dissemination. Whilst precipitation products continue to be developed and improved, major focus is now on product validation. Products are generated in a pre-operational fashion, with a delay of few minutes to few hours from observation, depending on product and satellite data access. Access to products is

  5. Real Time River Forecasting Center Quantitative Precipitation Estimate

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Radar indicated-rain gage verified and corrected hourly precipitation estimate on a corrected ~4km HRAP grid. This archive contains hourly estimates of precipitation...

  6. Global Precipitation: Means, Variations and Trends During the Satellite Era (1979-2014)

    Science.gov (United States)

    Adler, Robert F.; Gu, Guojun; Sapiano, Matthew; Wang, Jian-Jian; Huffman, George J.

    2017-07-01

    Global precipitation variations over the satellite era are reviewed using the Global Precipitation Climatology Project (GPCP) monthly, globally complete analyses, which integrate satellite and surface gauge information. Mean planetary values are examined and compared, over ocean, with information from recent satellite programs and related estimates, with generally positive agreements, but with some indication of small underestimates for GPCP over the global ocean. Variations during the satellite era in global precipitation are tied to ENSO events, with small increases during El Ninos, and very noticeable decreases after major volcanic eruptions. No overall significant trend is noted in the global precipitation mean value, unlike that for surface temperature and atmospheric water vapor. However, there is a pattern of positive and negative trends across the planet with increases over tropical oceans and decreases over some middle latitude regions. These observed patterns are a result of a combination of inter-decadal variations and the effect of the global warming during the period. The results reviewed here indicate the value of such analyses as GPCP and the possible improvement in the information as the record lengthens and as new, more sophisticated and more accurate observations are included.

  7. 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

  8. A statistical method to get surface level air-temperature from satellite observations of precipitable water

    Digital Repository Service at National Institute of Oceanography (India)

    Pankajakshan, T.; Shikauchi, A.; Sugimori, Y.; Kubota, M.

    Vol. 49, pp. 551 to 558. 1993 A Statistical Method to Get Surface Level Air-Temperature from Satellite Observations of Precipitable Water PANKAJAKSHAN THADATHIL*, AKIRA SHIKAUCHI, YASUHIRO SUGIMORI and MASAHISA KUBOTA School of Marine Science... observations for getting the estimates of heat flux across the air-sea boundary (Miller, 1981; Liu, 1988). Bulk method has widely been used for this purpose and the parameters required are: sea surface temperature, and wind speed, air-temperature and specific...

  9. PM-GCD - a combined IR-MW satellite technique for frequent retrieval of heavy precipitation

    Science.gov (United States)

    Casella, D.; Dietrich, S.; di Paola, F.; Formenton, M.; Mugnai, A.; Porcù, F.; Sanò, P.

    2012-01-01

    Precipitation retrievals based on measurements from microwave (MW) radiometers onboard low-Earth-orbit (LEO) satellites can reach high level of accuracy - especially regarding convective precipitation. At the present stage though, these observations cannot provide satisfactory coverage of the evolution of intense and rapid precipitating systems. As a result, the obtained precipitation retrievals are often of limited use for many important applications - especially in supporting authorities for flood alerts and weather warnings. To tackle this problem, over the past two decades several techniques have been developed combining accurate MW estimates with frequent infrared (IR) observations from geosynchronous (GEO) satellites, such as the European Meteosat Second Generation (MSG). In this framework, we have developed a new fast and simple precipitation retrieval technique which we call Passive Microwave - Global Convective Diagnostic, (PM-GCD). This method uses MW retrievals in conjunction with the Global Convective Diagnostic (GCD) technique which discriminates deep convective clouds based on the difference between the MSG water vapor (6.2 μm) and thermal-IR (10.8 μm) channels. Specifically, MSG observations and the GCD technique are used to identify deep convective areas. These areas are then calibrated using MW precipitation estimates based on observations from the Advanced Microwave Sounding Unit (AMSU) radiometers onboard operational NOAA and Eumetsat satellites, and then finally propagated in time with a simple tracking algorithm. In this paper, we describe the PM-GCD technique, analyzing its results for a case study that refers to a flood event that struck the island of Sicily in southern Italy on 1-2 October 2009.

  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. Hydrologic evaluation of a Generalized Statistical Uncertainty Model for Satellite Precipitation Products

    Science.gov (United States)

    Sarachi, S.; Hsu, K. L.; Sorooshian, S.

    2014-12-01

    Development of satellite based precipitation retrieval algorithms and using them in hydroclimatic studies have been of great interest to hydrologists. It is important to understand the uncertainty associated with precipitation products and how they further contribute to the variability in stream flow simulation. In this study a mixture model of Generalized Normal Distribution and Gamma distribution (GND-G) is used to model the joint probability distribution of satellite-based (PERSIANN) and stage IV radar rainfall. The study area for constructing the uncertainty model covers a 15°×15°box of 0.25°×0.25° cells over the eastern United States for summer 2004 to 2009. Cells are aggregated in space and time to obtain data with different resolutions for the construction of the model's parameter space. This uncertainty model is evaluated using data from National Weather Service (NWS) Distributed Hydrologic Model Intercomparison Project - Phase 2 (DMIP 2) basin over Illinois River basin south of Siloam, OK. This data covers the time period of 2006 to 2008.The uncertainty range of precipitation is estimated. The impact of precipitation uncertainty to the stream flow estimation is demonstrated by Monte Carlo simulation of precipitation forcing in the Sacramento Soil Moisture Accounting (SAC-SMA) model. The results show that using precipitation along with its uncertainty distribution as forcing to SAC-SMA make it possible to have an estimation of the uncertainty associated with the stream flow simulation ( in this case study %90 confidence interval is used). The mean of this stream flow confidence interval is compared to the reference stream flow for evaluation of the model and the results show that this method helps to better estimate the variability of the stream flow simulation along with its statistics e.g. percent bias and root mean squared error.

  12. Estimating Monthly Rainfall from Geostationary Satellite Imagery Over Amazonia, Brazil.

    Science.gov (United States)

    Cutrim, Elen Maria Camara

    The infrared regression and the grid-history satellite rainfall estimating techniques were utilized to estimate monthly rainfall in Amazonia during one month of the rainy season (March, 1980) and one month of the dry season (September, 1980). The estimates were based on 3-hourly SMS-II infrared and visible images. Three sets of coefficients for the grid history method (Marajo, Arabian Sea, and GATE) were used to estimate rainfall. The estimated rain was compared with gauge measurements over the region. The infrared regression technique overestimated by a factor of 1.5. The Marajo coefficients yielded the best estimate, especially for eastern Amazonia. In the wet month Marajo coefficients overestimated rain by 10% and in the dry month by 70%. The Arabian Sea coefficients overestimated rain and the GATE coefficients slightly underestimated rain for Amazonia. Two maps of monthly rainfall over Amazonia were constructed for March and September, 1980, combining the ground station and satellite inferred rainfall of the grid history method using the Marajo coefficients. The satellite observations and ground data were mutually compatible and were contourable on these final, composite maps. Monthly rainfall was found to be much more inhomogeneous than previously reported. In March there was a belt of high precipitation trending southwest, with higher values and sharpest gradients in the coastal area. The upper Amazon was also an area of high precipitation, both north and south of the equator. In Roraima rainfall decreased drastically to the north. In September, the area of highest precipitation was the northwestern part of Amazonas State (northern hemisphere). Rainfall elsewhere was very localized and in northeastern Amazonia varied from 0 to 150 mm. Even though the grid history method presented better results for estimating rainfall over Amazonia, the IR model could be utilized more efficiently and economically on an operational basis if the calibration were properly made

  13. PoPSat: The Polar Precipitation Satellite Mission

    Science.gov (United States)

    Binder, Matthias J.; Agten, Dries; Arago-Higueras, Nadia; Borderies, Mary; Diaz-Schümmer, Carlos; Jamali, Maryam; Jimenez-Lluva, David; Kiefer, Joshua; Larsson, Anna; Lopez-Gilabert, Lola; Mione, Michele; Mould, Toby JD; Pavesi, Sara; Roth, Georg; Tomicic, Maja

    2017-04-01

    The terrestrial water cycle is one of many unique regulatory systems on planet Earth. It is directly responsible for sustaining biological life on land and human populations by ensuring sustained crop yields. However, this delicate balanced system continues to be influenced significantly by a changing climate, which has had drastic impacts particularly on the polar regions. Precipitation is a key process in the weather and climate system, due to its storage, transport and release of latent heat in the atmosphere. It has been extensively investigated in low latitudes, in which detailed models have been established for weather prediction. However, a gap has been left in higher latitudes above 65°, which show the strongest response to climate changes and where increasing precipitations have been foreseen in the future. In order to establish a global perspective of atmospheric processes, space observation of high-latitude areas is crucial to produce globally consistent data. The increasing demand for those data has driven a critical need to devise a mission which fills the gaps in current climate models. The authors propose the Polar Precipitation Satellite (PoPSat), an innovative satellite mission to provide enhanced observation of light and medium precipitation, focusing on snowfall and light rain in high latitudes. PoPSat is the first mission aimed to provide high resolution 3D structural information about snow and light precipitation systems and cloud structure in the covered areas. The satellite is equipped with a dual band (Ka and W band) phased-array radar. These antennas provide a horizontal resolution of 2 km and 4 km respectively which will exceed all other observations made to date at high-latitudes, while providing the additional capability to monitor snowfall. The data gathered will be compatible and complementary with measurements made during previous missions. PoPSat has been designed to fly on a sun-synchronous, dawn-dusk orbit at 460 km. This orbit

  14. An Ensemble Generator for Quantitative Precipitation Estimation Based on Censored Shifted Gamma Distributions

    Science.gov (United States)

    Wright, D.; Kirschbaum, D.; Yatheendradas, S.

    2016-12-01

    The considerable uncertainties associated with quantitative precipitation estimates (QPE), whether from satellite platforms, ground-based weather radar, or numerical weather models, suggest that such QPE should be expressed as distributions or ensembles of possible values, rather than as single values. In this research, we borrow a framework from the weather forecast verification community, to "correct" satellite precipitation and generate ensemble QPE. This approach is based on the censored shifted gamma distribution (CSGD). The probability of precipitation, central tendency (i.e. mean), and the uncertainty can be captured by the three parameters of the CSGD. The CSGD can then be applied for simulation of rainfall ensembles using a flexible nonlinear regression framework, whereby the CSGD parameters can be conditioned on one or more reference rainfall datasets and on other time-varying covariates such as modeled or measured estimates of precipitable water and relative humidity. We present the framework and initial results by generating precipitation ensembles based on the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) dataset, using both NLDAS and PERSIANN-CDR precipitation datasets as references. We also incorporate a number of covariates from MERRA2 reanalysis including model-estimated precipitation, precipitable water, relative humidity, and lifting condensation level. We explore the prospects for applying the framework and other ensemble error models globally, including in regions where high-quality "ground truth" rainfall estimates are lacking. We compare the ensemble outputs against those of an independent rain gage-based ensemble rainfall dataset. "Pooling" of regional rainfall observations is explored as one option for improving ensemble estimates of rainfall extremes. The approach has potential applications in near-realtime, retrospective, and scenario modeling of rainfall-driven hazards such as floods and landslides

  15. JPSS Preparations at the Satellite Proving Ground for Marine, Precipitation, and Satellite Analysis

    Science.gov (United States)

    Folmer, M. J.; Berndt, E.; Clark, J.; Orrison, A.; Kibler, J.; Sienkiewicz, J. M.; Nelson, J. A., Jr.; Goldberg, M.

    2016-12-01

    The National Oceanic and Atmospheric Administration (NOAA) Satellite Proving Ground (PG) for Marine, Precipitation, and Satellite Analysis (MPS) has been demonstrating and evaluating Suomi National Polar-orbiting Partnership (S-NPP) products along with other polar-orbiting satellite platforms in preparation for the Joint Polar Satellite System - 1 (JPSS-1) launch in March 2017. The first S-NPP imagery was made available to the MPS PG during the evolution of Hurricane Sandy in October 2012 and has since been popular in operations. Since this event the MPS PG Satellite Liaison has been working with forecasters on ways to integrate single-channel and multispectral imagery from the Visible Infrared Imaging Radiometer Suite (VIIRS), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR)into operations to complement numerical weather prediction and geostationary satellite savvy National Weather Service (NWS) National Centers. Additional unique products have been introduced to operations to address specific forecast challenges, including the Cooperative Institute for Research in the Atmosphere (CIRA) Layered Precipitable Water, the National Environmental Satellite, Data, and Information Service (NESDIS) Snowfall Rate product, NOAA Unique Combined Atmospheric Processing System (NUCAPS) Soundings, ozone products from the Atmospheric Infrared Sounder (AIRS), Cross-track Infrared Sounder/Advanced Technology Microwave Sounder (CrIS/ATMS), and Infrared Atmospheric Sounding Interferometer (IASI). In addition, new satellite domains have been created to provide forecasters at the NWS Ocean Prediction Center and Weather Prediction Center with better quality imagery at high latitudes. This has led to research projects that are addressing forecast challenges such as tropical to extratropical transition and explosive cyclogenesis. This presentation will provide examples of how the MPS PG has been introducing and integrating

  16. Analysis of satellite precipitation over East Africa during last decades

    Science.gov (United States)

    Cattani, Elsa; Wenhaji Ndomeni, Claudine; Merino, Andrés; Levizzani, Vincenzo

    2016-04-01

    Daily accumulated precipitation time series from satellite retrieval algorithms (e.g., ARC2 and TAMSAT) are exploited to extract the spatial and temporal variability of East Africa (EA - 5°S-20°N, 28°E-52°E) precipitation during last decades (1983-2013). The Empirical Orthogonal Function (EOF) analysis is applied to precipitation time series to investigate the spatial and temporal variability in particular for October-November-December referred to as the short rain season. Moreover, the connection among EA's precipitation, sea surface temperature, and soil moisture is analyzed through the correlation with the dominant EOF modes of variability. Preliminary results concern the first two EOF's modes for the ARC2 data set. EOF1 is characterized by an inter-annual variability and a positive correlation between precipitation and El Niño, positive Indian Ocean Dipole mode, and soil moisture, while EOF2 shows a dipole structure of spatial variability associated with a longer scale temporal variability. This second dominant mode is mostly linked to sea surface temperature variations in the North Atlantic Ocean. Further analyses are carried out by computing the time series of the joint CCI/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI, http://etccdi.pacificclimate.org/index.shtml), i.e. RX1day, RX5day, CDD, CDD, CWD, SDII, PRCPTOT, R10, R20. The purpose is to identify the occurrenes of extreme events (droughts and floods) and extract precipitation temporal variation by trend analysis (Mann-Kendall technique). Results for the ARC2 data set demonstrate the existence of a dipole spatial pattern in the linear trend of the time series of PRCPTOT (annual precipitation considering days with a rain rate > 1 mm) and SDII (average precipitation on wet days over a year). A negative trend is mainly present over West Ethiopia and Sudan, whereas a positive trend is exhibited over East Ethiopia and Somalia. CDD (maximum number of consecutive dry days) and

  17. Impact of Missing Passive Microwave Sensors on Multi-Satellite Precipitation Retrieval Algorithm

    Directory of Open Access Journals (Sweden)

    Bin Yong

    2015-01-01

    Full Text Available The impact of one or two missing passive microwave (PMW input sensors on the end product of multi-satellite precipitation products is an interesting but obscure issue for both algorithm developers and data users. On 28 January 2013, the Version-7 TRMM Multi-satellite Precipitation Analysis (TMPA products were reproduced and re-released by National Aeronautics and Space Administration (NASA Goddard Space Flight Center because the Advanced Microwave Sounding Unit-B (AMSU-B and the Special Sensor Microwave Imager-Sounder-F16 (SSMIS-F16 input data were unintentionally disregarded in the prior retrieval. Thus, this study investigates the sensitivity of TMPA algorithm results to missing PMW sensors by intercomparing the “early” and “late” Version-7 TMPA real-time (TMPA-RT precipitation estimates (i.e., without and with AMSU-B, SSMIS-F16 sensors with an independent high-density gauge network of 200 tipping-bucket rain gauges over the Chinese Jinghe river basin (45,421 km2. The retrieval counts and retrieval frequency of various PMW and Infrared (IR sensors incorporated into the TMPA system were also analyzed to identify and diagnose the impacts of sensor availability on the TMPA-RT retrieval accuracy. Results show that the incorporation of AMSU-B and SSMIS-F16 has substantially reduced systematic errors. The improvement exhibits rather strong seasonal and topographic dependencies. Our analyses suggest that one or two single PMW sensors might play a key role in affecting the end product of current combined microwave-infrared precipitation estimates. This finding supports algorithm developers’ current endeavor in spatiotemporally incorporating as many PMW sensors as possible in the multi-satellite precipitation retrieval system called Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission (IMERG. This study also recommends users of satellite precipitation products to switch to the newest Version-7 TMPA datasets and

  18. Evaluation of Satellite Precipitation and Hydrological Model Predictions for Flood Events Over The Guadalupe River Basin, Texas

    Science.gov (United States)

    Sharif, H. O.; Furl, C.

    2016-12-01

    In this study, we evaluate the quality of several satellite precipitation products in comparison to gauge corrected ground based radar estimaties for moderate to high magnitude events across the Guadalupe River system in south Texas. The analysis is conducted across four partially nested watersheds (200-10,000 km2) such that scale effects can also be examined. Additionally, the precipitation data sets are used as input to the fully-distributed, physics-based Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model to examine rainfall error propagation through the hydrologic model predictions. Both gauge corrected and uncorrected satellite products are used encompassing a variety of latent delivery times, spatial resolutions, and temporal resolutions. Satellite precipitation datasets used in the study include various products from GPM, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system, the NOAA CPC Morphing Technique (CMORPH), and the Tropical Rainfall Measuring Mission (TRMM).

  19. Performance of the Dual-frequency Precipitation Radar on the GPM core satellite

    Science.gov (United States)

    Iguchi, Toshio; Seto, Shinta; Awaka, Jun; Meneghini, Robert; Kubota, Takuji; Oki, Riko; Chandra, Venkatchalam; Kawamoto, Nozomi

    2016-04-01

    The GPM core satellite was launched on February 28, 2014. This paper describes some of the results of precipitation measurements with the Dual-Frequency Precipitation Radar (DPR) on the GPM core satellite. The DPR, which was developed by Japan Aerospace Exploration Agency (JAXA) and National Institute of Information and Communications Technology (NICT), consists of two radars: Ku-band precipitation radar (KuPR) and Ka-band radar (KaPR). The performance of the DPR is evaluated by comparing the level 2 products with the corresponding TRMM/PR data and surface rain measurements. The scanning geometry and footprint size of KuPR and those of PR are nearly identical. The major differences between them are the sensitivity, visiting frequency, and the rain retrieval algorithm. KuPR's sensitivity is twice as good as PR. The increase of sensitivity reduces the cases of missing light rain. Since relatively light rain prevails in Japan, the difference in sensitivity may cause a few percentage points in the bias. Comparisons of the rain estimates by GPM/DPR with AMeDAS rain gauge data over Japan show that annual KuPR's estimates over Japan agree quite well with the rain gauge estimates although the monthly or local statistics of these two kinds of data scatter substantially. KuPR's esimates are closer to the gauge estimates than the TRMM/PR. Possible sources of the differences that include sampling errors, sensitivity, and the algorithm are examined.

  20. Comparison of satellite reflectance algorithms for estimating ...

    Science.gov (United States)

    We analyzed 10 established and 4 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense water truth collected within one hour of image acquisition to develop simple proxies for algal blooms and to facilitate portability between multispectral satellite imagers for regional algal bloom monitoring. Narrow band hyperspectral aircraft images were upscaled spectrally and spatially to simulate 5 current and near future satellite imaging systems. Established and new Chl-a algorithms were then applied to the synthetic satellite images and then compared to calibrated Chl-a water truth measurements collected from 44 sites within one hour of aircraft acquisition of the imagery. Masks based on the spatial resolution of the synthetic satellite imagery were then applied to eliminate mixed pixels including vegetated shorelines. Medium-resolution Landsat and finer resolution data were evaluated against 29 coincident water truth sites. Coarse-resolution MODIS and MERIS-like data were evaluated against 9 coincident water truth sites. Each synthetic satellite data set was then evaluated for the performance of a variety of spectrally appropriate algorithms with regard to the estimation of Chl-a concentrations against the water truth data set. The goal is to inform water resource decisions on the appropriate satellite data acquisition and processing for the es

  1. Online Assessment of Satellite-Derived Global Precipitation Products

    Science.gov (United States)

    Liu, Zhong; Ostrenga, D.; Teng, W.; Kempler, S.

    2012-01-01

    Precipitation is difficult to measure and predict. Each year droughts and floods cause severe property damages and human casualties around the world. Accurate measurement and forecast are important for mitigation and preparedness efforts. Significant progress has been made over the past decade in satellite precipitation product development. In particular, products' spatial and temporal resolutions as well as timely availability have been improved by blended techniques. Their resulting products are widely used in various research and applications. However biases and uncertainties are common among precipitation products and an obstacle exists in quickly gaining knowledge of product quality, biases and behavior at a local or regional scale, namely user defined areas or points of interest. Current online inter-comparison and validation services have not addressed this issue adequately. To address this issue, we have developed a prototype to inter-compare satellite derived daily products in the TRMM Online Visualization and Analysis System (TOVAS). Despite its limited functionality and datasets, users can use this tool to generate customized plots within the United States for 2005. In addition, users can download customized data for further analysis, e.g. comparing their gauge data. To meet increasing demands, we plan to increase the temporal coverage and expanded the spatial coverage from the United States to the globe. More products have been added as well. In this poster, we present two new tools: Inter-comparison of 3B42RT and 3B42 Inter-comparison of V6 and V7 TRMM L-3 monthly products The future plans include integrating IPWG (International Precipitation Working Group) Validation Algorithms/statistics, allowing users to generate customized plots and data. In addition, we will expand the current daily products to monthly and their climatology products. Whenever the TRMM science team changes their product version number, users would like to know the differences by

  2. Analysis of long term trends of precipitation estimates acquired using radar network in Turkey

    Science.gov (United States)

    Tugrul Yilmaz, M.; Yucel, Ismail; Kamil Yilmaz, Koray

    2016-04-01

    Precipitation estimates, a vital input in many hydrological and agricultural studies, can be obtained using many different platforms (ground station-, radar-, model-, satellite-based). Satellite- and model-based estimates are spatially continuous datasets, however they lack the high resolution information many applications often require. Station-based values are actual precipitation observations, however they suffer from their nature that they are point data. These datasets may be interpolated however such end-products may have large errors over remote locations with different climate/topography/etc than the areas stations are installed. Radars have the particular advantage of having high spatial resolution information over land even though accuracy of radar-based precipitation estimates depends on the Z-R relationship, mountain blockage, target distance from the radar, spurious echoes resulting from anomalous propagation of the radar beam, bright band contamination and ground clutter. A viable method to obtain spatially and temporally high resolution consistent precipitation information is merging radar and station data to take advantage of each retrieval platform. An optimally merged product is particularly important in Turkey where complex topography exerts strong controls on the precipitation regime and in turn hampers observation efforts. There are currently 10 (additional 7 are planned) weather radars over Turkey obtaining precipitation information since 2007. This study aims to optimally merge radar precipitation data with station based observations to introduce a station-radar blended precipitation product. This study was supported by TUBITAK fund # 114Y676.

  3. Validation of Satellite Precipitation (trmm 3B43) in Ecuadorian Coastal Plains, Andean Highlands and Amazonian Rainforest

    Science.gov (United States)

    Ballari, D.; Castro, E.; Campozano, L.

    2016-06-01

    Precipitation monitoring is of utmost importance for water resource management. However, in regions of complex terrain such as Ecuador, the high spatio-temporal precipitation variability and the scarcity of rain gauges, make difficult to obtain accurate estimations of precipitation. Remotely sensed estimated precipitation, such as the Multi-satellite Precipitation Analysis TRMM, can cope with this problem after a validation process, which must be representative in space and time. In this work we validate monthly estimates from TRMM 3B43 satellite precipitation (0.25° x 0.25° resolution), by using ground data from 14 rain gauges in Ecuador. The stations are located in the 3 most differentiated regions of the country: the Pacific coastal plains, the Andean highlands, and the Amazon rainforest. Time series, between 1998 - 2010, of imagery and rain gauges were compared using statistical error metrics such as bias, root mean square error, and Pearson correlation; and with detection indexes such as probability of detection, equitable threat score, false alarm rate and frequency bias index. The results showed that precipitation seasonality is well represented and TRMM 3B43 acceptably estimates the monthly precipitation in the three regions of the country. According to both, statistical error metrics and detection indexes, the coastal and Amazon regions are better estimated quantitatively than the Andean highlands. Additionally, it was found that there are better estimations for light precipitation rates. The present validation of TRMM 3B43 provides important results to support further studies on calibration and bias correction of precipitation in ungagged watershed basins.

  4. Application of Multi-Satellite Precipitation Analysis to Floods and Landslides

    Science.gov (United States)

    Adler, Robert; Hong, Yang; Huffman, George

    2007-01-01

    Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers. In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described

  5. Capabilities and uncertainties of aircraft measurements for the validation of satellite precipitation products – a virtual case study

    Directory of Open Access Journals (Sweden)

    Andrea Lammert

    2015-08-01

    Full Text Available Remote sensing sensors on board of research aircraft provide detailed measurements of clouds and precipitation which can be used as reference data to validate satellite products. Such satellite derived precipitation data using passive microwave radiometers with a resolution of typically 50×50km2$50\\times50\\,\\text{km}^2$ stands against high spatial and temporal resolved airborne measurements, but only along a chosen line. This paper focuses on analysis on the uncertainty arising from the different spatial resolution and coverage. Therefore we use a perfect model approach, with a high resolved forecast model yielding perfect virtual aircraft and satellite observations. The mean precipitation and standard deviation per satellite box were estimated with a Gaussian approach. The comparison of the mean values shows a high correlation of 0.92, but a very wide spread. As criterion to define good agreement between satellite mean and reference, we choose a deviation of one standard deviation of the virtual aircraft as threshold. Considering flight tracks in the range of 50 km (one overflight, the perfect agreement of satellite and aircraft observations is only detected in 65 % of the cases. To increase this low reliability the precipitation distributions of the virtual aircraft were fitted by a gamma density function. Using the same quality criterion, the usage of gamma density fit yields an improvement of the Aircraft reliability up to 80 %.

  6. Potential Utility of the Real-Time TMPA-RT Precipitation Estimates in Streamflow Prediction

    Science.gov (United States)

    Su, Fengge; Gao, Huilin; Huffman, George J.; Lettenmaier, Dennis P.

    2010-01-01

    We investigate the potential utility of the real-time Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA-RT) data for streamflow prediction, both through direct comparisons of TMPA-RT estimates with a gridded gauge product, and through evaluation of streamflow simulations over four tributaries of La Plata Basin (LPB) in South America using the two precipitation products. Our assessments indicate that the relative accuracy and the hydrologic performance of TMPA-RT-based streamflow simulations generally improved after February 2005. The improvements in TMPA-RT since 2005 are closely related to upgrades in the TMPA-RT algorithm in early February, 2005 which include use of additional microwave sensors (AMSR-E and AMSU-B) and implementation of different calibration schemes. Our work suggests considerable potential for hydrologic prediction using purely satellite-derived precipitation estimates (no adjustments by in situ gauges) in parts of the globe where in situ observations are sparse.

  7. Evaluation of Six High-Resolution Satellite and Ground-Based Precipitation Products over Malaysia

    Directory of Open Access Journals (Sweden)

    Mou Leong Tan

    2015-01-01

    Full Text Available Satellite precipitation products (SPPs potentially constitute an alternative to sparse rain gauge networks for assessing the spatial distribution of precipitation. However, applications of these products are still limited due to the lack of robust quality assessment. This study compares daily, monthly, seasonal, and annual rainfall amount at 342 rain gauges over Malaysia to estimations using five SPPs (3B42RT, 3B42V7, GPCP-1DD, PERSIANN-CDR, and CMORPH and a ground-based precipitation product (APHRODITE. The performance of the precipitation products was evaluated from 2003 to 2007 using continuous (RMSE, R2, ME, MAE, and RB and categorical (ACC, POD, FAR, CSI, and HSS statistical approaches. Overall, 3B42V7 and APHRODITE performed the best, while the worst performance was shown by GPCP-1DD. 3B42RT, 3B42V7, and PERSIANN-CDR slightly overestimated observed precipitation by 2%, 4.7%, and 2.1%, respectively. By contrast, APHRODITE and CMORPH significantly underestimated precipitations by 19.7% and 13.2%, respectively, whereas GPCP-1DD only slightly underestimated by 2.8%. All six precipitation products performed better in the northeast monsoon than in the southwest monsoon. The better performances occurred in eastern and southern Peninsular Malaysia and in the north of East Malaysia, which receives higher rainfall during the northeast monsoon, whereas poor performances occurred in the western and dryer Peninsular Malaysia. All precipitation products underestimated the no/tiny (<1 mm/day and extreme (≥20 mm/day rainfall events, while they overestimated low (1–20 mm/day rainfall events. 3B42RT and 3B42V7 showed the best ability to detect precipitation amounts with the highest HSS value (0.36. Precipitations during flood events such as those which occurred in late 2006 and early 2007 were estimated the best by 3B42RT and 3B42V7, as shown by an R2 value ranging from 0.49 to 0.88 and 0.52 to 0.86, respectively. These results on SPPs’ uncertainties

  8. 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.

  9. Combining Satellite Microwave Radiometer and Radar Observations to Estimate Atmospheric Latent Heating Profiles

    Science.gov (United States)

    Grecu, Mircea; Olson, William S.; Shie, Chung-Lin; L'Ecuyer, Tristan S.; Tao, Wei-Kuo

    2009-01-01

    In this study, satellite passive microwave sensor observations from the TRMM Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q1-QR) in regions of precipitation. The TMI heating algorithm (TRAIN) is calibrated, or "trained" using relatively accurate estimates of heating based upon spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based upon a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically-integrated condensation and surface precipitation. Estimates of Q1-QR from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q1 produced by combining TMI Q1-QR with independently derived estimates of QR show reasonable agreement with rawinsonde-based analyses of Q1 from two field campaigns, although the satellite estimates exhibit heating profile structure with sharper and more intense heating peaks than the rawinsonde estimates. 2

  10. Real-Time Application of Multi-Satellite Precipitation Analysis for Floods and Landslides

    Science.gov (United States)

    Adler, Robert; Hong, Yang; Huffman, George

    2007-01-01

    Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets-- both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers, In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described

  11. Research project in support of the GPCC: error estimation and development of a new method for the combination of conventionally observed and other precipitation data (especially satellite data) for operational analysis for global precipitation. Final report; Begleitendes FE-Vorhaben zum WZN: Fehlerschaetzung und Implementierung einer Methode zur optimalen Verknuepfung konventioneller und anderer Niederschlagsdaten (insbesondere Satellitendaten) zu operationellen globalen Niederschlagsanalysen. Schlussbericht

    Energy Technology Data Exchange (ETDEWEB)

    Rudolf, B.

    1998-04-01

    The Global Precipitation Climatology Centre (GPCC) was established in order to provide climate research with global gridded precipitation datasets derived from observational data. The centre is a German contribution to the World Climate Research Programme and is integrated in its major project Global Energy and Water Cycle Experiment (GEWEX). The goals of the research project described here were (1) improvement of the accuracy of the global precipitation analyses of GPCC as well as (2) quantification of the errors of the analysis products. Within the project following details have been carried out: - Development of the databank (procedures of station identification and data access). - Quality control for the input data and for the resulting products. - Improvement of the control methods for monthly precipitation data. - Development of an operational method for estimation of errors of gridded precipitation data which are area-averaged from conventional in-situ observations. - Intercomparison of gridded precipitation datasets resulting from different observation techniques and models. - Development of an operational method for the combination of gridded precipitation datasets resulting from different observation techniques (in colaboration with other contributors to the International Global Precipitation Climatology Project (GPCP)). - Investigation of the distribution of raingauge stations in relation to orography. - Preparation of digital global orography data for application with precipitation analysis. - Examination of methods and results of precipitation analysis based orography data. Products of GPCC and more information can be taken from Internet under http://www.dwd.de/research/gpcc. (orig.) [Deutsch] Das Weltzentrum fuer Niederschlagsklimatologie (WZN, bzw. Global Precipitation Climatology Centre, GPCC) wurde geschaffen, um der Klimaforschung monatliche globale Niederschlagsanalysen auf der Basis von Beobachtungsdaten zu liefern. Als ein deutscher Beitrag zum

  12. An Integrated Method of Multiradar Quantitative Precipitation Estimation Based on Cloud Classification and Dynamic Error Analysis

    Directory of Open Access Journals (Sweden)

    Yong Huang

    2017-01-01

    Full Text Available Relationships between radar reflectivity factor and rainfall are different in various precipitation cloud systems. In this study, the cloud systems are firstly classified into five categories with radar and satellite data to improve radar quantitative precipitation estimation (QPE algorithm. Secondly, the errors of multiradar QPE algorithms are assumed to be different in convective and stratiform clouds. The QPE data are then derived with methods of Z-R, Kalman filter (KF, optimum interpolation (OI, Kalman filter plus optimum interpolation (KFOI, and average calibration (AC based on error analysis on the Huaihe River Basin. In the case of flood on the early of July 2007, the KFOI is applied to obtain the QPE product. Applications show that the KFOI can improve precision of estimating precipitation for multiple precipitation types.

  13. Simple and approximate estimations of future precipitation return values

    Science.gov (United States)

    Benestad, Rasmus E.; Parding, Kajsa M.; Mezghani, Abdelkader; Dyrrdal, Anita V.

    2017-07-01

    We present estimates of future 20-year return values for 24 h precipitation based on multi-model ensembles of temperature projections and a crude method to quantify how warmer conditions may influence precipitation intensity. Our results suggest an increase by as much as 40-50 % projected for 2100 for a number of locations in Europe, assuming the high Representative Concentration Pathway (RCP) 8.5 emission scenario. The new strategy was based on combining physical understandings with the limited information available, and it utilised the covariance between the mean seasonal variations in precipitation intensity and the North Atlantic saturation vapour pressure. Rather than estimating the expected values and interannual variability, we tried to estimate an upper bound for the response in the precipitation intensity based on the assumption that the seasonal variations in the precipitation intensity are caused by the seasonal variations in temperature. Return values were subsequently derived from the estimated precipitation intensity through a simple and approximate scheme that combined the 1-year 24 h precipitation return values and downscaled annual wet-day mean precipitation for a 20-year event. The latter was based on the 95th percentile of a multi-model ensemble spread of downscaled climate model results. We found geographical variations in the shape of the seasonal cycle of the wet-day mean precipitation which suggest that different rain-producing mechanisms dominate in different regions. These differences indicate that the simple method used here to estimate the response of precipitation intensity to temperature was more appropriate for convective precipitation than for orographic rainfall.

  14. Error Analysis of Satellite Precipitation-Driven Modeling of Flood Events in Complex Alpine Terrain

    Directory of Open Access Journals (Sweden)

    Yiwen Mei

    2016-03-01

    Full Text Available The error in satellite precipitation-driven complex terrain flood simulations is characterized in this study for eight different global satellite products and 128 flood events over the Eastern Italian Alps. The flood events are grouped according to two flood types: rain floods and flash floods. The satellite precipitation products and runoff simulations are evaluated based on systematic and random error metrics applied on the matched event pairs and basin-scale event properties (i.e., rainfall and runoff cumulative depth and time series shape. Overall, error characteristics exhibit dependency on the flood type. Generally, timing of the event precipitation mass center and dispersion of the time series derived from satellite precipitation exhibits good agreement with the reference; the cumulative depth is mostly underestimated. The study shows a dampening effect in both systematic and random error components of the satellite-driven hydrograph relative to the satellite-retrieved hyetograph. The systematic error in shape of the time series shows a significant dampening effect. The random error dampening effect is less pronounced for the flash flood events and the rain flood events with a high runoff coefficient. This event-based analysis of the satellite precipitation error propagation in flood modeling sheds light on the application of satellite precipitation in mountain flood hydrology.

  15. The validation service of the hydrological SAF geostationary and polar satellite precipitation products

    Science.gov (United States)

    Puca, S.; Porcu, F.; Rinollo, A.; Vulpiani, G.; Baguis, P.; Balabanova, S.; Campione, E.; Ertürk, A.; Gabellani, S.; Iwanski, R.; Jurašek, M.; Kaňák, J.; Kerényi, J.; Koshinchanov, G.; Kozinarova, G.; Krahe, P.; Lapeta, B.; Lábó, E.; Milani, L.; Okon, L'.; Öztopal, A.; Pagliara, P.; Pignone, F.; Rachimow, C.; Rebora, N.; Roulin, E.; Sönmez, I.; Toniazzo, A.; Biron, D.; Casella, D.; Cattani, E.; Dietrich, S.; Di Paola, F.; Laviola, S.; Levizzani, V.; Melfi, D.; Mugnai, A.; Panegrossi, G.; Petracca, M.; Sanò, P.; Zauli, F.; Rosci, P.; De Leonibus, L.; Agosta, E.; Gattari, F.

    2014-04-01

    The development phase (DP) of the EUMETSAT Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF) led to the design and implementation of several precipitation products, after 5 yr (2005-2010) of activity. Presently, five precipitation estimation algorithms based on data from passive microwave and infrared sensors, on board geostationary and sun-synchronous platforms, function in operational mode at the H-SAF hosting institute to provide near real-time precipitation products at different spatial and temporal resolutions. In order to evaluate the precipitation product accuracy, a validation activity has been established since the beginning of the project. A Precipitation Product Validation Group (PPVG) works in parallel with the development of the estimation algorithms with two aims: to provide the algorithm developers with indications to refine algorithms and products, and to evaluate the error structure to be associated with the operational products. In this paper, the framework of the PPVG is presented: (a) the characteristics of the ground reference data available to H-SAF (i.e. radar and rain gauge networks), (b) the agreed upon validation strategy settled among the eight European countries participating in the PPVG, and (c) the steps of the validation procedures. The quality of the reference data is discussed, and the efforts for its improvement are outlined, with special emphasis on the definition of a ground radar quality map and on the implementation of a suitable rain gauge interpolation algorithm. The work done during the H-SAF development phase has led the PPVG to converge into a common validation procedure among the members, taking advantage of the experience acquired by each one of them in the validation of H-SAF products. The methodology is presented here, indicating the main steps of the validation procedure (ground data quality control, spatial interpolation, up-scaling of radar data vs. satellite grid

  16. Evaluation of the latest satellite-gauge precipitation products and their hydrologic applications over the Huaihe River basin

    Science.gov (United States)

    Sun, Ruochen; Yuan, Huiling; Liu, Xiaoli; Jiang, Xiaoman

    2016-05-01

    Satellite-gauge quantitative precipitation estimate (QPE) products may reduce the errors in near real-time satellite precipitation estimates by combining rain gauge data, which provides great potential to hydrometeorological applications. This study aims to comprehensively evaluate four of the latest satellite-gauge QPEs, including NASA's Tropical Rainfall Measuring Mission (TRMM) 3B42V7 product, NOAA's Climate Prediction Center (CPC) MORPHing technique (CMORPH) bias-corrected product (CMORPH CRT), CMORPH satellite-gauge merged product (CMORPH BLD) and CMORPH satellite-gauge merged product developed at the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA) (CMORPH CMA). These four satellite-gauge QPEs are statistically evaluated over the Huaihe River basin during 2003-2012 and applied into the distributed Variable Infiltration Capacity (VIC) model to assess hydrologic utilities. Compared to the China Gauge-based Daily Precipitation Analysis (CGDPA) newly developed at CMA/NMIC, the four satellite-gauge QPEs generally depict the spatial distribution well, with the underestimation in the southern mountains and overestimation in the northern plain of the Huaihe River basin. Specifically, both TRMM and CMORPH CRT adopt simple gauge adjustment algorithms and exhibit relatively poor performance, with evidently deteriorated quality in winter. In contrast, the probability density function-optimal interpolation (PDF-OI) gauge adjustment procedure has been applied in CMORPH BLD and CMORPH CMA, resulting in higher quality and more stable performance. CMORPH CMA further benefits from a merged dense gauge observation network and outperforms the other QPEs with significant improvements in rainfall amount and spatial/temporal distributions. Due to the insufficient gauge observations in the merging process, CMORPH BLD features the similar error characteristics of CMORPH CRT with a positive bias of light precipitation and a negative

  17. Yesterday's Japan: A system of flood risk estimation over Japan with remote-sensing precipitation data

    Science.gov (United States)

    Kanae, S.; Seto, S.; Yoshimura, K.; Oki, T.

    2008-12-01

    A new river discharge prediction and hindcast system all over Japan in order to issue alerts of flood risks has been developed. It utilizes Japan Meteorological Agency"fs Meso-scale model outputs and remote-sensing precipitation data. A statistical approach that compromises the bias and uncertainty of models is proposed for interpreting the simulated river discharge as a flood risk. A 29-year simulation was implemented to estimate parameters of the Gumbel distribution for the probability of extreme discharge, and the estimated discharge probability index (DPI) showed good agreement with that estimated based on observations. Even more strikingly, high DPI in the simulation corresponded to actual flood damage records. This indicates that the real-time simulation of the DPI could potentially provide reasonable flood warnings. A method to overcome the lack of sufficiently long simulation data through the use of a pre-existing long-term simulation and to estimate statistical parameters is also proposed. A preliminary flood risk prediction that used operational weather forecast data for 2003 and 2004 gave results similar to those of the 29-year simulation for the Typhoon T0423 event on October 2004, demonstrating the transferability of the technique to real-time prediction. In addition, the usefulness of satellite precipitation data for the flood estimation is evaluated via hindcast. We conducted it using several precipitation satellite datasets. The GSMaP product can detect heavy precipitation events, but floods being not well simulated in many cases because of GSMAP"fs underestimation. The GSMaP product adjusted by using monthly and 1 degree rain gauge information can be used to detect flood events as well as hourly rain gauge observations. Another quantitative issue is also disscussed. When a remote-sensing based precipitation data is used as an input for hindcast, we are suffering from underestimation of precipitation amount. The effort for improvement will be shown

  18. Effects of the Forecasting Methods, Precipitation Character, and Satellite Resolution on the Predictability of Short-Term Quantitative Precipitation Nowcasting (QPN from a Geostationary Satellite.

    Directory of Open Access Journals (Sweden)

    Yu Liu

    Full Text Available The prediction of the short-term quantitative precipitation nowcasting (QPN from consecutive gestational satellite images has important implications for hydro-meteorological modeling and forecasting. However, the systematic analysis of the predictability of QPN is limited. The objective of this study is to evaluate effects of the forecasting model, precipitation character, and satellite resolution on the predictability of QPN using images of a Chinese geostationary meteorological satellite Fengyun-2F (FY-2F which covered all intensive observation since its launch despite of only a total of approximately 10 days. In the first step, three methods were compared to evaluate the performance of the QPN methods: a pixel-based QPN using the maximum correlation method (PMC; the Horn-Schunck optical-flow scheme (PHS; and the Pyramid Lucas-Kanade Optical Flow method (PPLK, which is newly proposed here. Subsequently, the effect of the precipitation systems was indicated by 2338 imageries of 8 precipitation periods. Then, the resolution dependence was demonstrated by analyzing the QPN with six spatial resolutions (0.1atial, 0.3a, 0.4atial rand 0.6. The results show that the PPLK improves the predictability of QPN with better performance than the other comparison methods. The predictability of the QPN is significantly determined by the precipitation system, and a coarse spatial resolution of the satellite reduces the predictability of QPN.

  19. Effects of the Forecasting Methods, Precipitation Character, and Satellite Resolution on the Predictability of Short-Term Quantitative Precipitation Nowcasting (QPN) from a Geostationary Satellite.

    Science.gov (United States)

    Liu, Yu; Xi, Du-Gang; Li, Zhao-Liang; Ji, Wei

    2015-01-01

    The prediction of the short-term quantitative precipitation nowcasting (QPN) from consecutive gestational satellite images has important implications for hydro-meteorological modeling and forecasting. However, the systematic analysis of the predictability of QPN is limited. The objective of this study is to evaluate effects of the forecasting model, precipitation character, and satellite resolution on the predictability of QPN using images of a Chinese geostationary meteorological satellite Fengyun-2F (FY-2F) which covered all intensive observation since its launch despite of only a total of approximately 10 days. In the first step, three methods were compared to evaluate the performance of the QPN methods: a pixel-based QPN using the maximum correlation method (PMC); the Horn-Schunck optical-flow scheme (PHS); and the Pyramid Lucas-Kanade Optical Flow method (PPLK), which is newly proposed here. Subsequently, the effect of the precipitation systems was indicated by 2338 imageries of 8 precipitation periods. Then, the resolution dependence was demonstrated by analyzing the QPN with six spatial resolutions (0.1atial, 0.3a, 0.4atial rand 0.6). The results show that the PPLK improves the predictability of QPN with better performance than the other comparison methods. The predictability of the QPN is significantly determined by the precipitation system, and a coarse spatial resolution of the satellite reduces the predictability of QPN.

  20. Comparison and evaluation of satellite- and reanalysis-based precipitation products for water resources management in the Brahmaputra River basin

    Science.gov (United States)

    Saleh Khan, Abu; Sohel Masud, Md.; Abdulla Hel Kafi, Md.; Sultana, Tashrifa; Lopez Lopez, Patricia

    2017-04-01

    . The best performance was achieved with TRMM-3B42 precipitation. Preliminary results also show that precipitation is better captured during monsoon season rather than in dry seasons with all the analysed precipitation products. Moreover, in the comparison at a sub-basin level, precipitation estimates are more accurate in those sub-basins located in the southern part of the Brahmaputra River basin. These results identify the added value of satellite-based and reanalysis derived precipitation products for improving available information and water resources management in the Brahmaputra River basin. Keywords: precipitation, earth observations, hydrological modeling, Brahmaputra River basin.

  1. Correcting satellite-based precipitation products from SMOS soil moisture data assimilation using two models of different complexity

    Science.gov (United States)

    Román-Cascón, Carlos; Pellarin, Thierry; Gibon, François

    2017-04-01

    Real-time precipitation information at the global scale is quite useful information for many applications. However, satellite-based precipitation products in real time are known to be biased from real values observed in situ. On the other hand, the information about precipitation contained in soil moisture data can be very useful to improve precipitation estimation, since the evolution of this variable is highly influenced by the amount of rainfall at a certain area after a rain event. In this context, the soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite is used to correct the precipitation provided by real-time satellite-based products such as CMORPH, TRMM-3B42RT or PERSIANN. In this work, we test an assimilation algorithm based on the data assimilation of SMOS measurements in two models of different complexity: a simple hydrological model (Antecedent Precipitation Index (API)) and a state-of-the-art complex land-surface model (Surface Externalisée (SURFEX)). We show how the assimilation technique, based on a particle filter method, leads to the improvement of correlation and root mean squared error (RMSE) of precipitation estimates, with slightly better results for the simpler (and less expensive computationally) API model. This methodology has been evaluated for six years in ten sites around the world with different features, showing the limitations of the methodology in regions affected by mountainous terrain or by high radio-frequency interferences (RFI), which notably affect the quality of the soil moisture retrievals from brightness temperatures by SMOS. The presented results are promising for a potential near-real time application at the global scale.

  2. 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.

  3. 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.

  4. A New Method for Near Real Time Precipitation Estimates Using a Derived Statistical Relationship between Precipitable Water Vapor and Precipitation

    Science.gov (United States)

    Roman, J.

    2015-12-01

    The IPCC 5th Assessment found that the predicted warming of 1oC would increase the risk of extreme events such as heat waves, droughts, and floods. Weather extremes, like floods, have shown the vulnerability and susceptibility society has to these extreme weather events, through impacts such as disruption of food production, water supply, health, and damage of infrastructure. This paper examines a new way of near-real time forecasting of precipitation. A 10-year statistical climatological relationship was derived between precipitable water vapor (PWV) and precipitation by using the NASA Atmospheric Infrared Sounder daily gridded PWV product and the NASA Tropical Rainfall Measuring Mission daily gridded precipitation total. Forecasting precipitation estimates in real time is dire for flood monitoring and disaster management. Near real time PWV observations from AIRS on Aqua are available through the Goddard Earth Sciences Data and Information Service Center. In addition, PWV observations are available through direct broadcast from the NASA Suomi-NPP ATMS/CrIS instrument, the operational follow on to AIRS. The derived climatological relationship can be applied to create precipitation estimates in near real time by utilizing the direct broadcasting capabilities currently available in the CONUS region. The application of this relationship will be characterized through case-studies by using near real-time NASA AIRS Science Team v6 PWV products and ground-based SuomiNet GPS to estimate the current precipitation potential; the max amount of precipitation that can occur based on the moisture availability. Furthermore, the potential contribution of using the direct broadcasting of the NUCAPS ATMS/CrIS PWV products will be demonstrated. The analysis will highlight the advantages of applying this relationship in near-real time for flash flood monitoring and risk management. Relevance to the NWS River Forecast Centers will be discussed.

  5. Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil

    Directory of Open Access Journals (Sweden)

    Justine Ringard

    2015-12-01

    Full Text Available Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis V7 (Version 7 and RT (real time, CMORPH (Climate Prediction Center (CPC MORPHing technique and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network, for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio. Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and

  6. Satellite derived precipitation and freshwater flux variability and its dependence on the North Atlantic Oscillation

    Science.gov (United States)

    Andersson, Axel; Bakan, Stephan; Graßl, Hartmut

    2010-08-01

    The variability of satellite retrieved precipitation and freshwater flux from the `Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data' (HOAPS) is assessed with special emphasis on the `North Atlantic Oscillation' (NAO). To cover also land areas, a novel combination of the satellite derived precipitation climatology with the rain gauge based `Full Data Reanalysis Product Version 4', of the `Global Precipitation Climatology Centre' (GPCC) is used. This yields unique high-resolution, quasi-global precipitation fields compiled from two independent data sources. Over the ocean, the response of the freshwater balance and the related parameters to the NAO is investigated for the first time by using a purely satellite based data set. A strong dependence of precipitation patterns to the state of the NAO is found. On synoptic scale this is in accordance with earlier findings by other satellite based and reanalysis products. Furthermore, the consistency of the combined HOAPS-3/GPCC data set allows also detailed regional analyses of precipitation patterns. The response of HOAPS-3 freshwater flux to the NAO is dominated by precipitation at mid and high latitudes, while for the subtropical regions the feedback of the evaporation is stronger.

  7. Flood forecasting in Niger-Benue basin using satellite and quantitative precipitation forecast data

    Science.gov (United States)

    Haile, Alemseged Tamiru; Tefera, Fekadu Teshome; Rientjes, Tom

    2016-10-01

    Availability of reliable, timely and accurate rainfall data is constraining the establishment of flood forecasting and early warning systems in many parts of Africa. We evaluated the potential of satellite and weather forecast data as input to a parsimonious flood forecasting model to provide information for flood early warning in the central part of Nigeria. We calibrated the HEC-HMS rainfall-runoff model using rainfall data from post real time Tropical Rainfall Measuring Mission (TRMM) Multi satellite Precipitation Analysis product (TMPA). Real time TMPA satellite rainfall estimates and European Centre for Medium-Range Weather Forecasts (ECMWF) rainfall products were tested for flood forecasting. The implication of removing the systematic errors of the satellite rainfall estimates (SREs) was explored. Performance of the rainfall-runoff model was assessed using visual inspection of simulated and observed hydrographs and a set of performance indicators. The forecast skill was assessed for 1-6 days lead time using categorical verification statistics such as Probability Of Detection (POD), Frequency Of Hit (FOH) and Frequency Of Miss (FOM). The model performance satisfactorily reproduced the pattern and volume of the observed stream flow hydrograph of Benue River. Overall, our results show that SREs and rainfall forecasts from weather models have great potential to serve as model inputs for real-time flood forecasting in data scarce areas. For these data to receive application in African transboundary basins, we suggest (i) removing their systematic error to further improve flood forecast skill; (ii) improving rainfall forecasts; and (iii) improving data sharing between riparian countries.

  8. Bias correction of satellite precipitation products for flood forecasting application at the Upper Mahanadi River Basin in Eastern India

    Science.gov (United States)

    Beria, H.; Nanda, T., Sr.; Chatterjee, C.

    2015-12-01

    High resolution satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM), Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF), etc., offer a promising alternative to flood forecasting in data scarce regions. At the current state-of-art, these products cannot be used in the raw form for flood forecasting, even at smaller lead times. In the current study, these precipitation products are bias corrected using statistical techniques, such as additive and multiplicative bias corrections, and wavelet multi-resolution analysis (MRA) with India Meteorological Department (IMD) gridded precipitation product,obtained from gauge-based rainfall estimates. Neural network based rainfall-runoff modeling using these bias corrected products provide encouraging results for flood forecasting upto 48 hours lead time. We will present various statistical and graphical interpretations of catchment response to high rainfall events using both the raw and bias corrected precipitation products at different lead times.

  9. 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...

  10. PM-GCD – a combined IR–MW satellite technique for frequent retrieval of heavy precipitation

    Directory of Open Access Journals (Sweden)

    D. Casella

    2012-01-01

    Full Text Available Precipitation retrievals based on measurements from microwave (MW radiometers onboard low-Earth-orbit (LEO satellites can reach high level of accuracy – especially regarding convective precipitation. At the present stage though, these observations cannot provide satisfactory coverage of the evolution of intense and rapid precipitating systems. As a result, the obtained precipitation retrievals are often of limited use for many important applications – especially in supporting authorities for flood alerts and weather warnings. To tackle this problem, over the past two decades several techniques have been developed combining accurate MW estimates with frequent infrared (IR observations from geosynchronous (GEO satellites, such as the European Meteosat Second Generation (MSG. In this framework, we have developed a new fast and simple precipitation retrieval technique which we call Passive Microwave – Global Convective Diagnostic, (PM-GCD. This method uses MW retrievals in conjunction with the Global Convective Diagnostic (GCD technique which discriminates deep convective clouds based on the difference between the MSG water vapor (6.2 μm and thermal-IR (10.8 μm channels. Specifically, MSG observations and the GCD technique are used to identify deep convective areas. These areas are then calibrated using MW precipitation estimates based on observations from the Advanced Microwave Sounding Unit (AMSU radiometers onboard operational NOAA and Eumetsat satellites, and then finally propagated in time with a simple tracking algorithm. In this paper, we describe the PM-GCD technique, analyzing its results for a case study that refers to a flood event that struck the island of Sicily in southern Italy on 1–2 October 2009.

  11. Forecasting Lake-Effect Precipitation in the Great Lakes Region Using NASA Enhanced-Satellite Data

    Science.gov (United States)

    Cipullo, Michelle; Molthan, Andrew; Shafer, Jackie; Case, Jonathan; Jedlovec, Gary

    2011-01-01

    Lake-effect precipitation is common in the Great Lakes region, particularly during the late fall and winter. The synoptic processes of lake-effect precipitation are well understood by operational forecasters, but individual forecast events still present a challenge. Locally run, high resolution models can assist the forecaster in identifying the onset and duration of precipitation, but model results are sensitive to initial conditions, particularly the assumed surface temperature of the Great Lakes. The NASA Short-term Prediction Research and Transition (SPoRT) Center has created a Great Lakes Surface Temperature (GLST) composite, which uses infrared estimates of water temperatures obtained from the MODIS instrument aboard the Aqua and Terra satellites, other coarser resolution infrared data when MODIS is not available, and ice cover maps produced by the NOAA Great Lakes Environmental Research Lab (GLERL). This product has been implemented into the Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS), used within forecast offices to run local, high resolution forecasts. The sensitivity of the model forecast to the GLST product was analyzed with a case study of the Lake Effect Storm Echinacea, which produced 10 to 12 inches of snowfall downwind of Lake Erie, and 8 to 18 inches downwind of Lake Ontario from 27-29 January 2010. This research compares a forecast using the default Great Lakes surface temperatures from the Real Time Global sea surface temperature (RTG SST), in the WRF-EMS model to the enhanced NASA SPoRT GLST product to study forecast impacts. Results from this case study show that the SPoRT GLST contained less ice cover over Lake Erie and generally cooler water temperatures over Lakes Erie and Ontario. Latent and sensible heat fluxes over Lake Ontario were decreased in the GLST product. The GLST product decreased the quantitative precipitation forecast (QPF), which can be correlated to the decrease in temperatures and heat

  12. Estimating Soil Moisture from Satellite Microwave Observations

    Science.gov (United States)

    Owe, M.; VandeGriend, A. A.; deJeu, R.; deVries, J.; Seyhan, E.

    1998-01-01

    Cooperative research in microwave remote sensing between the Hydrological Sciences Branch of the NASA Goddard Space Flight Center and the Earth Sciences Faculty of the Vrije Universiteit Amsterdam began with the Botswana Water and Energy Balance Experiment and has continued through a series of highly successful International Research Programs. The collaboration between these two research institutions has resulted in significant scientific achievements, most notably in the area of satellite-based microwave remote sensing of soil moisture. The Botswana Program was the first joint research initiative between these two institutions, and provided a unique data base which included historical data sets of Scanning Multifrequency Microwave Radiometer (SN4NM) data, climate information, and extensive soil moisture measurements over several large experimental sites in southeast Botswana. These data were the basis for the development of new approaches in physically-based inverse modelling of soil moisture from satellite microwave observations. Among the results from this study were quantitative estimates of vegetation transmission properties at microwave frequencies. A single polarization modelling approach which used horizontally polarized microwave observations combined with monthly composites of Normalized Difference Vegetation Index was developed, and yielded good results. After more precise field experimentation with a ground-based radiometer system, a dual-polarization approach was subsequently developed. This new approach realized significant improvements in soil moisture estimation by satellite. Results from the Botswana study were subsequently applied to a desertification monitoring study for the country of Spain within the framework of the European Community science research programs EFEDA and RESMEDES. A dual frequency approach with only microwave data was used for this application. The Microwave Polarization Difference Index (MPDI) was calculated from 37 GHz data

  13. Multi-satellite rainfall sampling error estimates – a comparative study

    Directory of Open Access Journals (Sweden)

    A. Loew

    2012-10-01

    Full Text Available This study focus is set on quantifying sampling related uncertainty in the satellite rainfall estimates. We conduct observing system simulation experiment to estimate sampling error for various constellations of Low-Earth orbiting and geostationary satellites. There are two types of microwave instruments currently available: cross track sounders and conical scanners. We evaluate the differences in sampling uncertainty for various satellite constellations that carry instruments of the common type as well as in combination with geostationary observations. A precise orbital model is used to simulate realistic satellite overpasses with orbital shifts taken into account. With this model we resampled rain gauge timeseries to simulate satellites rainfall estimates free of retrieval and calibration errors. We concentrate on two regions, Germany and Benin, areas with different precipitation regimes. Our results show that sampling uncertainty for all satellite constellations does not differ greatly depending on the area despite the differences in local precipitation patterns. Addition of 3 hourly geostationary observations provides equal performance improvement in Germany and Benin, reducing rainfall undersampling by 20–25% of the total rainfall amount. Authors do not find a significant difference in rainfall sampling between conical imager and cross-track sounders.

  14. Sampling error study for rainfall estimate by satellite using a stochastic model

    Science.gov (United States)

    Shin, Kyung-Sup; North, Gerald R.

    1988-01-01

    In a parameter study of satellite orbits, sampling errors of area-time averaged rain rate due to temporal sampling by satellites were estimated. The sampling characteristics were studied by accounting for the varying visiting intervals and varying fractions of averaging area on each visit as a function of the latitude of the grid box for a range of satellite orbital parameters. The sampling errors were estimated by a simple model based on the first-order Markov process of the time series of area averaged rain rates. For a satellite of nominal Tropical Rainfall Measuring Mission (Thiele, 1987) carrying an ideal scanning microwave radiometer for precipitation measurements, it is found that sampling error would be about 8 to 12 pct of estimated monthly mean rates over a grid box of 5 X 5 degrees. It is suggested that an observation system based on a low inclination satellite combined with a sunsynchronous satellite simultaneously might be the best candidate for making precipitation measurements from space.

  15. Comparing 20 years of precipitation estimates from different sources over the world ocean

    Science.gov (United States)

    Béranger, Karine; Barnier, Bernard; Gulev, Sergei; Crépon, Michel

    2006-06-01

    The paper compares ten different global precipitation data sets over the oceans and discusses their respective strengths and weaknesses in ocean regions where they are potentially important to the salinity and buoyancy budgets of surface waters. Data sets (acronyms of which are given in Section 2) are categorised according to their source of data, which are (1) in situ for Center for Climatic Research (Legates and Willmott, 1990; Archive of Precipitation Version 3.01, http://climate.geog.udel.edu/~climate ), Southampton Oceanography Centre (SOC) (Josey et al., J Clim 12:2856 2880, 1999) and University of Wisconsin-Milwaukee (UWM) (Da Silva et al. 1994); (2) satellite for Microwave Sounding Unit (MSU) (Spencer, J Clim 6:1301 1326, 1993), TOPEX (Quartly et al., J Geophys Res 104:31489 31516, 1999), and Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) (Bauer and Schluessel, J Geophys Res 98:20737 20759, 1993); (3) atmospheric forecast model re-analyses for European Centre for Medium-range Weather Forecast (ECMWF) (Gibson et al. 1997) and National Center for Environmental Prediction (NCEP) (Kalnay et al., Bull Am Meteorol Soc 77:437 471, 1996); and (4) composite for Global Precipitation Climatology Project (GPCP) (satellites and rain gauges, Huffman et al., Bull Am Meteorol Soc 78(1):5 20, 1997) and Climate Prediction Center Merged Analysis of Precipitation (CMAP) (satellites, rain gauges and atmospheric forecast model, Xie and Arkin, Bull Am Meteorol Soc 78(11):2539 2558, 1997). Although there is no absolute field of reference, composite data sets are often considered as the best estimates. First, a qualitative comparison is carried out, which provides for each data set, a description of the geographical distribution of the climatological mean precipitation field. A more careful comparison between data sets is undertaken over periods they have in common. First, six among the ten data sets (SOC, UWM, ECMWF, NCEP, MSU and CMAP) are compared over

  16. Vertical profiles of heating derived from IR-based precipitation estimates during FGGE SOP-1

    Science.gov (United States)

    Robertson, Franklin R.; Vincent, Dayton G.

    1988-01-01

    This paper examines a technique for retrieving from geostationary IR data the vertical profiles of heating and cooling due to moist diabatic processes. First, GOES IR imagery is used to estimate precipitation fields which are independent of fields inferred from residuals in heat budget analysis based on the FGGE level III-b data. Vertical distributions of the associated heating are then obtained using thermodynamic data from the level III-b analysis, one-dimensional cloud models, and the satellite-estimated precipitation. The technique was applied to infer heating in the South Pacific convergence zone during a portion of FGEE SOP-1, and the results were compared with heat-budget calculations made using the ECMWF analyses.

  17. Estimation of probable maximum precipitation at the Kielce Upland (Poland) using meteorological method

    Science.gov (United States)

    Suligowski, Roman

    2014-05-01

    Probable Maximum Precipitation based upon the physical mechanisms of precipitation formation at the Kielce Upland. This estimation stems from meteorological analysis of extremely high precipitation events, which occurred in the area between 1961 and 2007 causing serious flooding from rivers that drain the entire Kielce Upland. Meteorological situation has been assessed drawing on the synoptic maps, baric topography charts, satellite and radar images as well as the results of meteorological observations derived from surface weather observation stations. Most significant elements of this research include the comparison between distinctive synoptic situations over Europe and subsequent determination of typical rainfall generating mechanism. This allows the author to identify the source areas of air masses responsible for extremely high precipitation at the Kielce Upland. Analysis of the meteorological situations showed, that the source areas for humid air masses which cause the largest rainfalls at the Kielce Upland are the area of northern Adriatic Sea and the north-eastern coast of the Black Sea. Flood hazard at the Kielce Upland catchments was triggered by daily precipitation of over 60 mm. The highest representative dew point temperature in source areas of warm air masses (these responsible for high precipitation at the Kielce Upland) exceeded 20 degrees Celsius with a maximum of 24.9 degrees Celsius while precipitable water amounted to 80 mm. The value of precipitable water is also used for computation of factors featuring the system, namely the mass transformation factor and the system effectiveness factor. The mass transformation factor is computed based on precipitable water in the feeding mass and precipitable water in the source area. The system effectiveness factor (as the indicator of the maximum inflow velocity and the maximum velocity in the zone of front or ascending currents, forced by orography) is computed from the quotient of precipitable water in

  18. Evaluation of the Potential of NASA Multi-satellite Precipitation Analysis in Global Landslide Hazard Assessment

    Science.gov (United States)

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

    2007-01-01

    Landslides are one of the most widespread natural hazards on Earth, responsible for thousands of deaths and billions of dollars in property damage every year. In the U.S. alone landslides occur in every state, causing an estimated $2 billion in damage and 25- 50 deaths each year. Annual average loss of life from landslide hazards in Japan is 170. The situation is much worse in developing countries and remote mountainous regions due to lack of financial resources and inadequate disaster management ability. Recently, a landslide buried an entire village on the Philippines Island of Leyte on Feb 17,2006, with at least 1800 reported deaths and only 3 houses left standing of the original 300. Intense storms with high-intensity , long-duration rainfall have great potential to trigger rapidly moving landslides, resulting in casualties and property damage across the world. In recent years, through the availability of remotely sensed datasets, it has become possible to conduct global-scale landslide hazard assessment. This paper evaluates the potential of the real-time NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA) system to advance our understanding of and predictive ability for rainfall-triggered landslides. Early results show that the landslide occurrences are closely associated with the spatial patterns and temporal distribution of rainfall characteristics. Particularly, the number of landslide occurrences and the relative importance of rainfall in triggering landslides rely on the influence of rainfall attributes [e.g. rainfall climatology, antecedent rainfall accumulation, and intensity-duration of rainstorms). TMPA precipitation data are available in both real-time and post-real-time versions, which are useful to assess the location and timing of rainfall-triggered landslide hazards by monitoring landslide-prone areas while receiving heavy rainfall. For the purpose of identifying rainfall-triggered landslides, an empirical global rainfall intensity

  19. Developing Methodologies for Applying TRMM-Estimated Precipitation Data to Hydrological Modeling of a South TX Watershed - Initial Results

    Science.gov (United States)

    Tobin, K. J.; Bennett, M. E.

    2007-05-01

    Previous experience with hydrological modeling in South Texas, which is located along the Texas-Mexico border, suggests that NWS ground measurements are too widely scattered to provide reliable precipitation input for modeling. In addition, a significant fraction of the study region is located at the edge of the coverage envelopes of the NWS NEXRAD weather radars present in the region limiting the accuracy of these systems to provide reliable precipitation estimates. Therefore, we are exploring whether TRMM estimated-precipitation data (3B42), in some form, can be used to support hydrological modeling in the Middle Rio Grande and Nueces River Basin watersheds. We have begun our modeling efforts by focusing on the middle Nueces watershed (7770 sq km). To model this largely rural watershed we selected the Soil and Water Assessment Tool (SWAT). Three precipitation datasets were selected for our initial model runs that include: (1) nearest NWS cooperative hourly rain gauge data, (2) three hourly TRMM 3B42 estimated precipitation, and (3) combination TRMM 3B42/NWS rain gauge datasets in which ground measurements are used for three hourly periods lacking high quality satellite microwave precipitation estimates as determined from TRMM 3G68 data. Three dataset were aggregated into an average daily estimate of precipitation for each TRMM grid cell. Manual calibration of was completed achieving model results that yield realistic monthly and annual water balances with both gauge and satellite estimate precipitation datasets. In the future, we plan to use the newly developed automatic calibration routine for SWAT, which is based on the Shuffled Complex Evolution algorithm, to optimize modeled discharge results from this study.

  20. Groundwater Modelling For Recharge Estimation Using Satellite Based Evapotranspiration

    Science.gov (United States)

    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

  1. Assessing the performance of satellite-based precipitation products over the Mediterranean region

    Science.gov (United States)

    Xaver, Angelika; Dorigo, Wouter; Brocca, Luca; Ciabatta, Luca

    2017-04-01

    Detailed knowledge about the spatial and temporal patterns and quantities of precipitation is of high importance. This applies especially in the Mediterranean region, where water demand for agricultural, industrial and touristic needs is growing and climate projections foresee a decrease of precipitation amounts and an increase in variability. In this region, ground-based rain gauges are available only limited in number, particularly in northern Africa and the Middle East and lack to capture the high spatio-temporal character of precipitation over large areas. This has motivated the development of a large number of remote sensing products for monitoring rainfall. Satellite-based precipitation products are based on various observation principles and retrieval approaches, i.e. from thermal infra-red and microwaves. Although, many individual validation studies on the performance of these precipitation datasets exist, they mostly examine only one or a few of these rainfall products at the same time and are not targeted at the Mediterranean basin as a whole. Here, we present an extensive comparative study of seven different satellite-based precipitation products, namely CMORPH 30-minutes, CMORPH 3-hourly, GPCP, PERSIANN, SM2Rain CCI, TRMM TMPA 3B42, and TRMM TMPA 3B42RT, focusing on the whole Mediterranean region and on individual Mediterranean catchments. The time frame of investigation is restricted by the common availability of all precipitation products and covers the period 2000-2013. We assess the skill of the satellite products against gridded gauge-based data provided by GPCC and E-OBS. Apart from common characteristics like biases and temporal correlations we evaluate several sophisticated dataset properties that are of particular interest for Mediterranean hydrology, including the capability of the remotely sensed products to capture extreme events and trends. A clear seasonal dependency of the correlation results can be observed for the whole Mediterranean

  2. Satellite-rainfall estimation for identification of rainfall thresholds used for landslide/debris flow prediction

    Science.gov (United States)

    Maggioni, Viviana; Nikolopoulos, Efthymios I.; Marra, Francesco; Destro, Elisa; Borga, Marco

    2016-04-01

    Rainfall-induced landslides and debris flows pose a significant and widespread hazard, resulting in a large number of casualties and enormous economic damages worldwide. Rainfall thresholds are often used to identify the local or regional rainfall conditions that, when reached or exceeded, are likely to result in landslides or debris flows. Rain gauge data are the typical source of information for the definition of these rainfall thresholds. However, in-situ observations over mountainous areas, where these hazards mainly occur, are very sparse or inexistent. Therefore identification and use of gauge-based rainfall thresholds is impossible in many landslide prone areas over the globe. The vast advancements in satellite-based precipitation estimation over the last couple of decades have lead to the creation of a number of global precipitation datasets at various spatiotemporal resolutions. Although several investigations have shown that these datasets can be associated with considerable uncertainty, they provide the only source of precipitation information over many areas around the globe. Therefore it is important to assess their performance in the context of landslide/debris flow prediction and investigate how we can potentially benefit from the information they provide. In this work, we evaluate the performance of three widely used quasi-global satellite precipitation products (3B42v7, PERSIANN and CMORPH) for the identification of rainfall threshold for landslide/debris flow triggering. Products are available at 0.25deg/3h resolution. The study region is focused over the Upper Adige river basin, northern Italy where a detailed database of more than 400 identified debris flows (during period 2000-2015) and a raingauge network of 95 stations, is available. Rain-gauge based rainfall thresholds are compared against satellite-based thresholds to evaluate strengths and limitations in using satellite precipitation estimates for defining rainfall thresholds. Analysis of

  3. 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

  4. High-resolution satellite-gauge merged precipitation climatologies of the Tropical Andes

    Science.gov (United States)

    Manz, Bastian; Buytaert, Wouter; Zulkafli, Zed; Lavado, Waldo; Willems, Bram; Robles, Luis Alberto; Rodríguez-Sánchez, Juan-Pablo

    2016-02-01

    Satellite precipitation products are becoming increasingly useful to complement rain gauge networks in regions where these are too sparse to capture spatial precipitation patterns, such as in the Tropical Andes. The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (TPR) was active for 17 years (1998-2014) and has generated one of the longest single-sensor, high-resolution, and high-accuracy rainfall records. In this study, high-resolution (5 km) gridded mean monthly climatological precipitation is derived from the raw orbital TPR data (TRMM 2A25) and merged with 723 rain gauges using multiple satellite-gauge (S-G) merging approaches. The resulting precipitation products are evaluated by cross validation and catchment water balances (runoff ratios) for 50 catchments across the Tropical Andes. Results show that the TPR captures major synoptic and seasonal precipitation patterns and also accurately defines orographic gradients but underestimates absolute monthly rainfall rates. The S-G merged products presented in this study constitute an improved source of climatological rainfall data, outperforming the gridded TPR product as well as a rain gauge-only product based on ordinary Kriging. Among the S-G merging methods, performance of inverse distance interpolation of satellite-gauge residuals was similar to that of geostatistical methods, which were more sensitive to gauge network density. High uncertainty and low performance of the merged precipitation products predominantly affected regions with low and intermittent precipitation regimes (e.g., Peruvian Pacific coast) and is likely linked to the low TPR sampling frequency. All S-G merged products presented in this study are available in the public domain.

  5. Estimation of surface insolation using sun-synchronous satellite data

    Science.gov (United States)

    Darnell, Wayne L.; Staylor, W. Frank; Gupta, Shashi K.; Denn, Fred M.

    1988-01-01

    A technique is presented for estimating insolation at the earth's surface using only sun-synchronous satellite data. The technique was tested by comparing the insolation results from year-long satellite data sets with simultaneous ground-measured insolation taken at five continental United States sites. Monthly average insolation values derived from the satellite data showed a standard error of 4.2 W/sq m, or 2.7 percent of the average ground insolation value.

  6. An enhanced algorithm to estimate BDS satellite's differential code biases

    Science.gov (United States)

    Shi, Chuang; Fan, Lei; Li, Min; Liu, Zhizhao; Gu, Shengfeng; Zhong, Shiming; Song, Weiwei

    2016-02-01

    This paper proposes an enhanced algorithm to estimate the differential code biases (DCB) on three frequencies of the BeiDou Navigation Satellite System (BDS) satellites. By forming ionospheric observables derived from uncombined precise point positioning and geometry-free linear combination of phase-smoothed range, satellite DCBs are determined together with ionospheric delay that is modeled at each individual station. Specifically, the DCB and ionospheric delay are estimated in a weighted least-squares estimator by considering the precision of ionospheric observables, and a misclosure constraint for different types of satellite DCBs is introduced. This algorithm was tested by GNSS data collected in November and December 2013 from 29 stations of Multi-GNSS Experiment (MGEX) and BeiDou Experimental Tracking Stations. Results show that the proposed algorithm is able to precisely estimate BDS satellite DCBs, where the mean value of day-to-day scattering is about 0.19 ns and the RMS of the difference with respect to MGEX DCB products is about 0.24 ns. In order to make comparison, an existing algorithm based on IGG: Institute of Geodesy and Geophysics, China (IGGDCB), is also used to process the same dataset. Results show that, the DCB difference between results from the enhanced algorithm and the DCB products from Center for Orbit Determination in Europe (CODE) and MGEX is reduced in average by 46 % for GPS satellites and 14 % for BDS satellites, when compared with DCB difference between the results of IGGDCB algorithm and the DCB products from CODE and MGEX. In addition, we find the day-to-day scattering of BDS IGSO satellites is obviously lower than that of GEO and MEO satellites, and a significant bias exists in daily DCB values of GEO satellites comparing with MGEX DCB product. This proposed algorithm also provides a new approach to estimate the satellite DCBs of multiple GNSS systems.

  7. Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT

    Directory of Open Access Journals (Sweden)

    I. M. Lensky

    2008-03-01

    Full Text Available A methodology for representing much of the physical information content of the METEOSAT Second Generation (MSG geostationary satellite using red-green-blue (RGB composites of the computed physical values of the picture elements is presented. The physical values are the solar reflectance in the solar channels and brightness temperature in the thermal channels. The main RGB compositions are (1 "Day Natural Colors", presenting vegetation in green, bare surface in brown, sea surface in black, water clouds as white, ice as magenta; (2 "Day Microphysical", presenting cloud microstructure using the solar reflectance component of the 3.9 μm, visible and thermal IR channels; (3 "Night Microphysical", also presenting clouds microstructure using the brightness temperature differences between 10.8 and 3.9 μm; (4 "Day and Night", using only thermal channels for presenting surface and cloud properties, desert dust and volcanic emissions; (5 "Air Mass", presenting mid and upper tropospheric features using thermal water vapor and ozone channels. The scientific basis for these rendering schemes is provided, with examples for the applications. The expanding use of these rendering schemes requires their proper documentation and setting as standards, which is the main objective of this publication.

  8. Analysis of long-term precipitation pattern over Antarctica derived from satellite-borne radar

    Science.gov (United States)

    Milani, L.; Porcù, F.; Casella, D.; Dietrich, S.; Panegrossi, G.; Petracca, M.; Sanò, P.

    2015-01-01

    Mass accumulation is a key geophysical parameter in understanding the Antarctic climate and its role in the global system. The local mass variation is driven by a number of different mechanisms: the deposition of snow and ice crystals on the surface from the atmosphere is generally modified by strong surface winds and variations in temperature and humidity at the ground, making it difficult to measure directly the accumulation by a sparse network of ground based instruments. Moreover, the low cloud total water/ice content and the varying radiative properties of the ground pose problems in the retrieval of precipitation from passive space-borne sensors at all frequencies. Finally, numerical models, despite their high spatial and temporal resolution, show discordant results and are difficult to be validated using ground-based measurements. A significant improvement in the knowledge of the atmospheric contribution to the mass balance over Antarctica is possible by using active space-borne instruments, such as the Cloud Profiling Radar (CPR) on board the low earth orbit CloudSat satellite, launched in 2006 and still operating. The radar measures the vertical profile of reflectivity at 94 GHz (sensitive to small ice particles) providing narrow vertical cross-sections of clouds along the satellite track. The aim of this work is to show that, after accounting for the characteristics of precipitation and the effect of surface on reflectivity in Antarctica, the CPR can retrieve snowfall rates on a single event temporal scale. Furthermore, the CPR, despite its limited temporal and spatial sampling capabilities, also effectively observes the annual snowfall cycle in this region. Two years of CloudSat data over Antarctica are analyzed and converted in water equivalent snowfall rate. Two different approaches for precipitation estimates are considered in this work. The results are analyzed in terms of annual and monthly averages, as well as in terms of instantaneous values. The

  9. Analysis of long-term precipitation pattern over Antarctica derived from satellite-borne radar

    Directory of Open Access Journals (Sweden)

    L. Milani

    2015-01-01

    Full Text Available Mass accumulation is a key geophysical parameter in understanding the Antarctic climate and its role in the global system. The local mass variation is driven by a number of different mechanisms: the deposition of snow and ice crystals on the surface from the atmosphere is generally modified by strong surface winds and variations in temperature and humidity at the ground, making it difficult to measure directly the accumulation by a sparse network of ground based instruments. Moreover, the low cloud total water/ice content and the varying radiative properties of the ground pose problems in the retrieval of precipitation from passive space-borne sensors at all frequencies. Finally, numerical models, despite their high spatial and temporal resolution, show discordant results and are difficult to be validated using ground-based measurements. A significant improvement in the knowledge of the atmospheric contribution to the mass balance over Antarctica is possible by using active space-borne instruments, such as the Cloud Profiling Radar (CPR on board the low earth orbit CloudSat satellite, launched in 2006 and still operating. The radar measures the vertical profile of reflectivity at 94 GHz (sensitive to small ice particles providing narrow vertical cross-sections of clouds along the satellite track. The aim of this work is to show that, after accounting for the characteristics of precipitation and the effect of surface on reflectivity in Antarctica, the CPR can retrieve snowfall rates on a single event temporal scale. Furthermore, the CPR, despite its limited temporal and spatial sampling capabilities, also effectively observes the annual snowfall cycle in this region. Two years of CloudSat data over Antarctica are analyzed and converted in water equivalent snowfall rate. Two different approaches for precipitation estimates are considered in this work. The results are analyzed in terms of annual and monthly averages, as well as in terms of

  10. Early assessment of Integrated Multi-satellite Retrievals for Global Precipitation Measurement over China

    Science.gov (United States)

    Guo, Hao; Chen, Sheng; Bao, Anming; Behrangi, Ali; Hong, Yang; Ndayisaba, Felix; Hu, Junjun; Stepanian, Phillip M.

    2016-07-01

    Two post-real time precipitation products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) are systematically evaluated over China with China daily Precipitation Analysis Product (CPAP) as reference. The IMERG products include the gauge-corrected IMERG product (IMERG_Cal) and the version of IMERG without direct gauge correction (IMERG_Uncal). The post-research TRMM Multisatellite Precipitation Analysis version 7 (TMPA-3B42V7) is also evaluated concurrently with IMERG for better perspective. In order to be consistent with CPAP, the evaluation and comparison of selected products are performed at 0.25° and daily resolutions from 12 March 2014 through 28 February 2015. The results show that: Both IMERG and 3B42V7 show similar performances. Compared to IMERG_Uncal, IMERG_Cal shows significant improvement in overall and conditional bias and in the correlation coefficient. Both IMERG_Cal and IMERG_Uncal perform relatively poor in winter and over-detect slight precipitation events in northwestern China. As an early validation of the GPM-era IMERG products that inherit the TRMM-era global satellite precipitation products, these findings will provide useful feedbacks and insights for algorithm developers and data users over China and beyond.

  11. Combination of radar and daily precipitation data to estimate meaningful sub-daily point precipitation extremes

    Science.gov (United States)

    Bárdossy, András; Pegram, Geoffrey

    2017-01-01

    The use of radar measurements for the space time estimation of precipitation has for many decades been a central topic in hydro-meteorology. In this paper we are interested specifically in daily and sub-daily extreme values of precipitation at gauged or ungauged locations which are important for design. The purpose of the paper is to develop a methodology to combine daily precipitation observations and radar measurements to estimate sub-daily extremes at point locations. Radar data corrected using precipitation-reflectivity relationships lead to biased estimations of extremes. Different possibilities of correcting systematic errors using the daily observations are investigated. Observed gauged daily amounts are interpolated to unsampled points and subsequently disaggregated using the sub-daily values obtained by the radar. Different corrections based on the spatial variability and the subdaily entropy of scaled rainfall distributions are used to provide unbiased corrections of short duration extremes. Additionally a statistical procedure not based on a matching day by day correction is tested. In this last procedure as we are only interested in rare extremes, low to medium values of rainfall depth were neglected leaving a small number of L days of ranked daily maxima in each set per year, whose sum typically comprises about 50% of each annual rainfall total. The sum of these L day maxima is first iterpolated using a Kriging procedure. Subsequently this sum is disaggregated to daily values using a nearest neighbour procedure. The daily sums are then disaggregated by using the relative values of the biggest L radar based days. Of course, the timings of radar and gauge maxima can be different, so the method presented here uses radar for disaggregating daily gauge totals down to 15 min intervals in order to extract the maxima of sub-hourly through to daily rainfall. The methodologies were tested in South Africa, where an S-band radar operated relatively continuously at

  12. Comparison between CNA and energetic electron precipitation: simultaneous observation by Poker Flat Imaging Riometer and NOAA satellite

    Directory of Open Access Journals (Sweden)

    Y.-M. Tanaka

    2005-07-01

    Full Text Available The cosmic noise absorption (CNA is compared with the precipitating electron flux for 19 events observed in the morning sector, using the high-resolution data obtained during the conjugate observations with the imaging riometer at Poker Flat Research Range (PFRR; 65.11° N, 147.42° W, Alaska, and the low-altitude satellite, NOAA 12. We estimate the CNA, using the precipitating electron flux measured by NOAA 12, based on a theoretical model assuming an isotropic pitch angle distribution, and quantitatively compare them with the observed CNA. Focusing on the eight events with a range of variation larger than 0.4dB, three events show high correlation between the observed and estimated CNA (correlation coefficient (r0>0.7 and five events show low correlation (r0<0.5. The estimated CNA is often smaller than the observed CNA (72% of all data for 19 events, which appears to be the main reason for the low-correlation events. We examine the assumption of isotropic pitch angle distribution by using the trapped electron flux measured at 80° zenith angle. It is shown that the CNA estimated from the trapped electron flux, assuming an isotropic pitch angle distribution, is highly correlated with the observed CNA and is often overestimated (87% of all data. The underestimate (overestimate of CNA derived from the precipitating (trapped electron flux can be interpreted in terms of the anisotropic pitch angle distribution similar to the loss cone distribution. These results indicate that the CNA observed with the riometer may be quantitatively explained with a model based on energetic electron precipitation, provided that the pitch angle distribution and the loss cone angle of the electrons are taken into account.

    Keywords. Energetic particles, precipitating – Energetic particles, trapped – Ionosphere-magnetosphere interactions

  13. Assessment of North Atlantic Precipitation and Freshwater Flux from the HOAPS-3 satellite climatology

    Science.gov (United States)

    Andersson, A.; Klepp, C.; Bakan, S.; Schulz, J.

    2009-04-01

    To attain a better understanding and modeling of climate processes attaining a proper knowledge of global water cycle components is essential. For the assessment of the freshwater flux at the ocean surface on global scale, exchange processes at the air-sea interface play a key-role. With the ability to derive ocean latent heat flux and precipitation from satellite data with acceptable accuracy, and frequent global coverage, a climatological assessment of the crucial processes has become possible. The HOAPS-3 climatology (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) contains fields of precipitation, surface fluxes and atmospheric parameters over the global ice-free ocean between 1987 and 2005. Except for the NOAA Pathfinder SST, all basic state variables needed for the derivation of the fluxes are calculated from SSM/I passive microwave radiometer measurements. Multi-satellite averages, inter-sensor calibration, and an efficient sea ice detection procedure make HOAPS a suitable data set for climatological applications as well as for case studies. Gridded 0.5 degree monthly, pentad and twice daily data products are freely available from www.hoaps.org. For the precipitation parameter, quasi-global coverage is achieved by complementing HOAPS-3 over land areas using the rain gauge based "Full Data Reanalysis Product Version 4", which is provided by the Global Precipitation Climatology Centre (GPCC). North Atlantic intra-decadal precipitation variability is investigated using this combined data set. The mutual response of the two independent precipitation data sources to the North Atlantic Oscillation (NAO) reveals coherent patterns and a detailed view on the structural changes in precipitation during the high and low states of the NAO. A second focus will be put on the evaluation of HOAPS-3 ocean surface freshwater fluxes and their interaction with the NAO.

  14. Satellite cloud and precipitation property retrievals for climate monitoring and hydrological applications

    Science.gov (United States)

    Wolters, E. L. A.

    2012-03-01

    This thesis presents the retrieval, evaluation, and application of cloud physical property datasets (cloud phase, cloud particle effective radius, and precipitation occurrence and intensity) obtained from Spinning Enhanced Visible and Infrared Imager (SEVIRI) reflectance measurements using the Cloud Physical Properties (CPP) retrieval algorithm. In Chapter 3 it is shown that the CPP cloud-phase retrieval algorithm has sufficient accuracy (West Africa. During the afternoon, precipitation occurrence frequency over dry soils becomes significantly higher than over wet soils, whereas for precipitation intensity no significant difference is discerned. The study demonstrates that the combination of satellite-based soil moisture and precipitation observations can be helpful in improving the understanding of the land surface-precipitation interaction over tropical areas. The thesis concludes with a number of recommendations on future algorithm improvements and potential research applications. For both cloud phase and precipitation properties, extension of the algorithm to include nighttime observations would be desirable to enable detailed studies on the full diurnal cycle. Further, the SEVIRI High-Resolution Visible (HRV) channel could be incorporated to correct retrieved cloud physical properties for broken and inhomogeneous cloud cases. Finally, the accurate cloud phase and precipitation datasets combined with the high SEVIRI spatial and temporal sampling resolution enables possibilities for detailed research on climate monitoring, nowcasting applications, evaluation of cloud schemes in climate models, studies on land surface-precipitation interactions (with a special focus on the diurnal cycle), and assimilation of the datasets in weather and climate models

  15. Models for estimation of land remote sensing satellites operational efficiency

    Science.gov (United States)

    Kurenkov, Vladimir I.; Kucherov, Alexander S.

    2017-01-01

    The paper deals with the problem of estimation of land remote sensing satellites operational efficiency. Appropriate mathematical models have been developed. Some results obtained with the help of the software worked out in Delphi programming support environment are presented.

  16. Performance of TMPA satellite precipitation product over the Northern Great Plains

    Science.gov (United States)

    Kharel, G.; Kirilenko, A.; Zhang, X.

    2011-12-01

    Satellite derived precipitation can be used as supplement and/or replacement to ground data in various applications including modeling and weather forecasting based on its accuracy, reliability and validity. We analyzed Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) 3B42 v.6 Level 3 product (0.25° × 0.25°, 3-hour resolution) against the United States Historical Climatology Network (USHCN) ground data from 98 stations in the Northern Great Plains (NGP) over the period of seven years (2003 to 2009). NGP, comprised of Wyoming, Montana, North Dakota, Minnesota, South Dakota and Nebraska states of the US, is located between the latitudes 41° - 49° N and longitudes 94° - 113.5° E within the TMPA product latitude band (50° NS).The goal of this research was to investigate the performance of TMPA over the NGP region. Results showed that the TMPA daily data has poor rainfall detection ability (POD ~ 0.3), weak correlation with the meteorological data (ρ=0.46) and high root mean square deviation (RMSD = 4.9 mm/day). We also found noticeable seasonal differences in the daily TMPA product performance. It underperformed during cold season (November to March) with weaker correlation (0.25) and worse POD (~ 0.15), as compared to relatively modest correlation (0.47) and POD (~0.30) during warm season (April to October). Our analysis at monthly scale revealed significantly better performance of TMPA with higher correlation (0.82) and lower RMSD (0.72 mm/day). Based on our findings, the TMPA daily data might be a poor replacement to ground data, however, at a monthly scale, TMPA can be used to estimate spatial rainfall distribution in NGP and/or as an input to a stochastic daily weather generator.

  17. Application of satellite estimates of rainfall distribution to simulate the potential for malaria transmission in Africa

    Science.gov (United States)

    Yamana, T. K.; Eltahir, E. A.

    2009-12-01

    The Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS) is a mechanistic model developed to assess malaria risk in areas where the disease is water-limited. This model relies on precipitation inputs as its primary forcing. Until now, applications of the model have used ground-based precipitation observations. However, rain gauge networks in the areas most affected by malaria are often sparse. The increasing availability of satellite based rainfall estimates could greatly extend the range of the model. The minimum temporal resolution of precipitation data needed was determined to be one hour. The CPC Morphing technique (CMORPH ) distributed by NOAA fits this criteria, as it provides 30-minute estimates at 8km resolution. CMORPH data were compared to ground observations in four West African villages, and calibrated to reduce overestimation and false alarm biases. The calibrated CMORPH data were used to force HYDREMATS, resulting in outputs for mosquito populations, vectorial capacity and malaria transmission.

  18. Using R for analysing spatio-temporal datasets: a satellite-based precipitation case study

    Science.gov (United States)

    Zambrano-Bigiarini, Mauricio

    2017-04-01

    Increasing computer power and the availability of remote-sensing data measuring different environmental variables has led to unprecedented opportunities for Earth sciences in recent decades. However, dealing with hundred or thousands of files, usually in different vectorial and raster formats and measured with different temporal frequencies, impose high computation challenges to take full advantage of all the available data. R is a language and environment for statistical computing and graphics which includes several functions for data manipulation, calculation and graphical display, which are particularly well suited for Earth sciences. In this work I describe how R was used to exhaustively evaluate seven state-of-the-art satellite-based rainfall estimates (SRE) products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-adj, MSWEPv1.1 and PGFv3) over the complex topography and diverse climatic gradients of Chile. First, built-in functions were used to automatically download the satellite-images in different raster formats and spatial resolutions and to clip them into the Chilean spatial extent if necessary. Second, the raster package was used to read, plot, and conduct an exploratory data analysis in selected files of each SRE product, in order to detect unexpected problems (rotated spatial domains, order or variables in NetCDF files, etc). Third, raster was used along with the hydroTSM package to aggregate SRE files into different temporal scales (daily, monthly, seasonal, annual). Finally, the hydroTSM and hydroGOF packages were used to carry out a point-to-pixel comparison between precipitation time series measured at 366 stations and the corresponding grid cell of each SRE. The modified Kling-Gupta index of model performance was used to identify possible sources of systematic errors in each SRE, while five categorical indices (PC, POD, FAR, ETS, fBIAS) were used to assess the ability of each SRE to correctly identify different precipitation intensities

  19. Assimilation of radar quantitative precipitation estimations in the Canadian Precipitation Analysis (CaPA)

    Science.gov (United States)

    Fortin, Vincent; Roy, Guy; Donaldson, Norman; Mahidjiba, Ahmed

    2015-12-01

    The Canadian Precipitation Analysis (CaPA) is a data analysis system used operationally at the Canadian Meteorological Center (CMC) since April 2011 to produce gridded 6-h and 24-h precipitation accumulations in near real-time on a regular grid covering all of North America. The current resolution of the product is 10-km. Due to the low density of the observational network in most of Canada, the system relies on a background field provided by the Regional Deterministic Prediction System (RDPS) of Environment Canada, which is a short-term weather forecasting system for North America. For this reason, the North American configuration of CaPA is known as the Regional Deterministic Precipitation Analysis (RDPA). Early in the development of the CaPA system, weather radar reflectivity was identified as a very promising additional data source for the precipitation analysis, but necessary quality control procedures and bias-correction algorithms were lacking for the radar data. After three years of development and testing, a new version of CaPA-RDPA system was implemented in November 2014 at CMC. This version is able to assimilate radar quantitative precipitation estimates (QPEs) from all 31 operational Canadian weather radars. The radar QPE is used as an observation source and not as a background field, and is subject to a strict quality control procedure, like any other observation source. The November 2014 upgrade to CaPA-RDPA was implemented at the same time as an upgrade to the RDPS system, which brought minor changes to the skill and bias of CaPA-RDPA. This paper uses the frequency bias indicator (FBI), the equitable threat score (ETS) and the departure from the partial mean (DPM) in order to assess the improvements to CaPA-RDPA brought by the assimilation of radar QPE. Verification focuses on the 6-h accumulations, and is done against a network of 65 synoptic stations (approximately two stations per radar) that were withheld from the station data assimilated by Ca

  20. Continued development of a global precipitation dataset from satellite and ground-based gauges

    Science.gov (United States)

    Dietzsch, Felix; Andersson, Axel; Schröder, Marc; Ziese, Markus; Becker, Andreas

    2017-04-01

    The project framework MiKlip ("Mittelfristige Klimaprognosen") is focused on the development of an operational forecast system for decadal climate predictions. The objective of the "Daily Precipitation Analysis for the validation of Global medium-range Climate predictions Operationalized" (DAPAGLOCO) project, is the development and operationalization of a global precipitation dataset for forecast validation of the MPI-ESM experiments used in MiKlip. The dataset is a combination of rain gauge measurement data over land and satellite-based precipitation retrievals over ocean. Over land, gauge data from the Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst (DWD) are used. Over ocean, retrievals from the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) dataset are used as data source. The currently available dataset consists of 21 years of data (1988-2008) and is provided in different spatial resolutions of 1° and 2.5° on the global scale, and 0.5° for Europe. Rain rates over ocean are currently derived from satellite microwave imagers by using a neuronal network. For the future it is intended to switch this retrieval method to a 1D-Var method. The current state of the dataset is presented, an introduction to the future retrieval and its features is given and first results from evaluation and application are shown.

  1. Comparison and evaluation of satellite derived precipitation products for hydrological modeling of the Zambezi River Basin

    Directory of Open Access Journals (Sweden)

    T. Cohen Liechti

    2011-08-01

    Full Text Available In the framework of the African Dams ProjecT (ADAPT, an integrated water resource management study in the Zambezi Basin is currently under development. In view of the sparse gauging network for rainfall monitoring, the observations from spaceborne instrumentation currently produce the only available rainfall data for a large part of the basin.

    Three operational and acknowledged high resolution satellite derived estimates: the Tropical Rainfall Measuring Mission product 3B42 (TRMM 3B42, the Famine Early Warning System product 2.0 (FEWS RFE2.0 and the National Oceanic and Atmospheric Administration/Climate Prediction Centre (NOAA/CPC morphing technique (CMORPH are analyzed in terms of spatial and temporal repartition of the precipitations. They are compared to ground data for the wet seasons of the years 2003 to 2009 on a point to pixel basis at daily, 10-daily and monthly time steps and on a pixel to pixel basis for the wet seasons of the years 2003 to 2007 at monthly time steps.

    The general North-South gradient of precipitation is captured by all the analyzed products. Regarding the spatial heterogeneity, FEWS pixels are much more inter-correlated than TRMM and CMORPH pixels. For a rainfall homogeneity threshold criterion of 0.5 global mean correlation coefficient, the area of each subbasin should not exceed a circle of 2.5° latitude/longitude radius for FEWS and a circle of 0.75° latitude/longitude radius for TRMM and CMORPH considering rectangular mesh.

    In terms of reliability, the correspondence of all estimates with ground data increases with the time step chosen for the analysis. The volume ratio computation indicates that CMORPH is overestimating by nearly 1.5 times the rainfall. The statistics of TRMM and FEWS estimates show quite similar results.

    Due to the its lower inter-correlation and longer data set, the TRMM 3B42 product is chosen as input for the hydraulic-hydrologic model of the basin.

  2. Hydrological Utility and Uncertainty of Multi-Satellite Precipitation Products in the Mountainous Region of South Korea

    Directory of Open Access Journals (Sweden)

    Jong Pil Kim

    2016-07-01

    Full Text Available Satellite-derived precipitation can be a potential source of forcing data for assessing water availability and managing water supply in mountainous regions of East Asia. This study investigates the hydrological utility of satellite-derived precipitation and uncertainties attributed to error propagation of satellite products in hydrological modeling. To this end, four satellite precipitation products (tropical rainfall measuring mission (TRMM multi-satellite precipitation analysis (TMPA version 6 (TMPAv6 and version 7 (TMPAv7, the global satellite mapping of precipitation (GSMaP, and the climate prediction center (CPC morphing technique (CMORPH were integrated into a physically-based hydrologic model for the mountainous region of South Korea. The satellite precipitation products displayed different levels of accuracy when compared to the intra- and inter-annual variations of ground-gauged precipitation. As compared to the GSMaP and CMORPH products, superior performances were seen when the TMPA products were used within streamflow simulations. Significant dry (negative biases in the GSMaP and CMORPH products led to large underestimates of streamflow during wet-summer seasons. Although the TMPA products displayed a good level of performance for hydrologic modeling, there were some over/underestimates of precipitation by satellites during the winter season that were induced by snow accumulation and snowmelt processes. These differences resulted in streamflow simulation uncertainties during the winter and spring seasons. This study highlights the crucial need to understand hydrological uncertainties from satellite-derived precipitation for improved water resource management and planning in mountainous basins. Furthermore, it is suggested that a reliable snowfall detection algorithm is necessary for the new global precipitation measurement (GPM mission.

  3. Assessment and Comparison of TMPA Satellite Precipitation Products in Varying Climatic and Topographic Regimes in Morocco

    Directory of Open Access Journals (Sweden)

    Adam Milewski

    2015-05-01

    Full Text Available TRMM Multi-satellite Precipitation Analysis (TMPA satellite precipitation products have been utilized to quantify, forecast, or understand precipitation patterns, climate change, hydrologic models, and drought in numerous scientific investigations. The TMPA products recently went through a series of algorithm developments to enhance the accuracy and reliability of high-quality precipitation measurements, particularly in low rainfall environments and complex terrain. In this study, we evaluated four TMPA products (3B42: V6, V7temp, V7, RTV7 against 125 rain gauges in Northern Morocco to assess the accuracy of TMPA products in various regimes, examine the performance metrics of new algorithm developments, and assess the impact of the processing error in 2012. Results show that the research products outperform the real-time products in all environments within Morocco, and the newest algorithm development (3B42 V7 outperforms the previous version (V6, particularly in low rainfall and high-elevation environments. TMPA products continue to overestimate precipitation in arid environments and underestimate it in high-elevation areas. Lastly, the temporary processing error resulted in little bias except in arid environments. These results corroborate findings from previous studies, provide scientific data for the Middle East, highlight the difficulty of using TMPA products in varying conditions, and present preliminary research for future algorithm development for the GPM mission.

  4. The Evolution of El Nino-Precipitation Relationships from Satellites and Gauges

    Science.gov (United States)

    Curtis, Scott; Adler, Robert F.; Starr, David OC (Technical Monitor)

    2002-01-01

    This study uses a twenty-three year (1979-2001) satellite-gauge merged community data set to further describe the relationship between El Nino Southern Oscillation (ENSO) and precipitation. The globally complete precipitation fields reveal coherent bands of anomalies that extend from the tropics to the polar regions. Also, ENSO-precipitation relationships were analyzed during the six strongest El Ninos from 1979 to 2001. Seasons of evolution, Pre-onset, Onset, Peak, Decay, and Post-decay, were identified based on the strength of the El Nino. Then two simple and independent models, first order harmonic and linear, were fit to the monthly time series of normalized precipitation anomalies for each grid block. The sinusoidal model represents a three-phase evolution of precipitation, either dry-wet-dry or wet-dry-wet. This model is also highly correlated with the evolution of sea surface temperatures in the equatorial Pacific. The linear model represents a two-phase evolution of precipitation, either dry-wet or wet-dry. These models combine to account for over 50% of the precipitation variability for over half the globe during El Nino. Most regions, especially away from the Equator, favor the linear model. Areas that show the largest trend from dry to wet are southeastern Australia, eastern Indian Ocean, southern Japan, and off the coast of Peru. The northern tropical Pacific and Southeast Asia show the opposite trend.

  5. Development of Radar-Satellite Blended QPF (Quantitative Precipitation Forecast) Technique for heavy rainfall

    Science.gov (United States)

    Jang, Sangmin; Yoon, Sunkwon; Rhee, Jinyoung; Park, Kyungwon

    2016-04-01

    Due to the recent extreme weather and climate change, a frequency and size of localized heavy rainfall increases and it may bring various hazards including sediment-related disasters, flooding and inundation. To prevent and mitigate damage from such disasters, very short range forecasting and nowcasting of precipitation amounts are very important. Weather radar data very useful in monitoring and forecasting because weather radar has high resolution in spatial and temporal. Generally, extrapolation based on the motion vector is the best method of precipitation forecasting using radar rainfall data for a time frame within a few hours from the present. However, there is a need for improvement due to the radar rainfall being less accurate than rain-gauge on surface. To improve the radar rainfall and to take advantage of the COMS (Communication, Ocean and Meteorological Satellite) data, a technique to blend the different data types for very short range forecasting purposes was developed in the present study. The motion vector of precipitation systems are estimated using 1.5km CAPPI (Constant Altitude Plan Position Indicator) reflectivity by pattern matching method, which indicates the systems' direction and speed of movement and blended radar-COMS rain field is used for initial data. Since the original horizontal resolution of COMS is 4 km while that of radar is about 1 km, spatial downscaling technique is used to downscale the COMS data from 4 to 1 km pixels in order to match with the radar data. The accuracies of rainfall forecasting data were verified utilizing AWS (Automatic Weather System) observed data for an extreme rainfall occurred in the southern part of Korean Peninsula on 25 August 2014. The results of this study will be used as input data for an urban stream real-time flood early warning system and a prediction model of landslide. Acknowledgement This research was supported by a grant (13SCIPS04) from Smart Civil Infrastructure Research Program funded by

  6. Daily precipitation estimation through different microwave sensors: Verification study over Italy

    Science.gov (United States)

    Ciabatta, Luca; Marra, Anna Cinzia; Panegrossi, Giulia; Casella, Daniele; Sanò, Paolo; Dietrich, Stefano; Massari, Christian; Brocca, Luca

    2017-02-01

    The accurate estimation of rainfall from remote sensing is of paramount importance for many applications as, for instance, the mitigation of natural hazards like floods, droughts, and landslides. Traditionally, microwave observations in the frequency between 10 and 183 GHz are used for estimating rainfall based on the direct interaction of radiation with the hydrometeors within precipitating clouds in a so-called top-down approach. Recently, a bottom-up approach was proposed that uses satellite soil moisture products derived from microwave observations (nature. In this study, we perform a long-term (3 years) assessment of different satellite rainfall products exploiting the full range of microwave frequencies over Italy. Specifically, the integration of two top-down algorithms (CDRD, Cloud Dynamics and Radiation Database, and PNPR, Passive microwave Neural network Precipitation Retrieval) for estimating rainfall from conically and cross-track scanning radiometers, and one bottom-up algorithm (SM2RAIN) applied to the Advanced SCATterometer soil moisture product is carried out. The performances of the products, individually and merged together, are assessed at daily time scale. The integration of top-down and bottom-up approaches provides the highest performance both in terms of continuous and categorical scores (i.e., median correlation coefficient and root mean square error values equal to 0.71 and 6.62 mm, respectively). In such a combination, the limitations of the two approaches are compensated allowing a better estimation of ground accumulated rainfall through SM2RAIN while, overcoming the limitations of rainfall estimation for intense events during wet conditions through CDRD-PNPR product. The accuracy and the reliability of the merged product open new possibilities for their testing in hydrological applications, such as the monitoring and prediction of floods and droughts over large areas, including regions where ground-based measurements are sparse or not

  7. Rainfall-runoff modelling using different estimators of precipitation data in the Carpathian mountain catchments (South Poland)

    Science.gov (United States)

    Kasina, Michal; Ziemski, Michal; Niedbala, Jerzy; Malota, Agnieszka

    2013-04-01

    Precipitation observations are an essential element of flood forecasting systems. Rain gauges, radars, satellite sensors and forecasts from high resolution numerical weather prediction models are a part of precipitation monitoring networks. These networks collect rainfall data that are further provided to hydrological models to produce forecasts. The main goal of this work is to assess the usage of different precipitation data sources in rainfall-runoff modelling with reference to Flash Flood Early Warning System. STUDY AREA Research was carried out in the upper parts of the Sola and Raba river catchments. Both of the rivers begin their course in the southern part of the Western Beskids (Outer Eastern Carpathians; southern Poland). For the purpose of this study, both rivers are taken to comprise the catchments upstream of the gauging stations at Zywiec (Sola) and Stroza (Raba). The upper Sola river catchment encompasses an area of 785 sq. km with an altitude ranging from 342 to 1236 m above sea level, while the Raba river catchment occupies an area of 644 sq. km with an altitude ranging from 300 to 1266 m above sea level. The catchments are underlain mainly by flysch sediments. The average annual amount of precipitation for the Sola River catchment is between 750 and 1300 mm and for the Raba river catchment is in the range of 800-1000 mm. METHODS AND RESULTS This work assesses the sensitivity of a lumped hydrological model DHI's Nedbør-Afrstrømnings-Model (NAM) to different sources of rainfall estimates: rain gauges, radar and satellite as well as predicted precipitation amount from high resolution numerical weather prediction models (e.g. ALADIN). The main steps of validation procedure are: i) comparison of rain gauge data with other precipitation data sources, ii) calibration of the hydrological model (using historical, long time series of rain gauge data treated as "ground truth"), iii) validation using different precipitation data sources as an input, iii

  8. Improving high-resolution quantitative precipitation estimation via fusion of multiple radar-based precipitation products

    Science.gov (United States)

    Rafieeinasab, Arezoo; Norouzi, Amir; Seo, Dong-Jun; Nelson, Brian

    2015-12-01

    For monitoring and prediction of water-related hazards in urban areas such as flash flooding, high-resolution hydrologic and hydraulic modeling is necessary. Because of large sensitivity and scale dependence of rainfall-runoff models to errors in quantitative precipitation estimates (QPE), it is very important that the accuracy of QPE be improved in high-resolution hydrologic modeling to the greatest extent possible. With the availability of multiple radar-based precipitation products in many areas, one may now consider fusing them to produce more accurate high-resolution QPE for a wide spectrum of applications. In this work, we formulate and comparatively evaluate four relatively simple procedures for such fusion based on Fisher estimation and its conditional bias-penalized variant: Direct Estimation (DE), Bias Correction (BC), Reduced-Dimension Bias Correction (RBC) and Simple Estimation (SE). They are applied to fuse the Multisensor Precipitation Estimator (MPE) and radar-only Next Generation QPE (Q2) products at the 15-min 1-km resolution (Experiment 1), and the MPE and Collaborative Adaptive Sensing of the Atmosphere (CASA) QPE products at the 15-min 500-m resolution (Experiment 2). The resulting fused estimates are evaluated using the 15-min rain gauge observations from the City of Grand Prairie in the Dallas-Fort Worth Metroplex (DFW) in north Texas. The main criterion used for evaluation is that the fused QPE improves over the ingredient QPEs at their native spatial resolutions, and that, at the higher resolution, the fused QPE improves not only over the ingredient higher-resolution QPE but also over the ingredient lower-resolution QPE trivially disaggregated using the ingredient high-resolution QPE. All four procedures assume that the ingredient QPEs are unbiased, which is not likely to hold true in reality even if real-time bias correction is in operation. To test robustness under more realistic conditions, the fusion procedures were evaluated with and

  9. Q Conversion Factor Models for Estimating Precipitable Water Vapor for Turkey

    Science.gov (United States)

    Deniz, Ilke; Mekik, Cetin; Gurbuz, Gokhan

    2015-04-01

    precipitable water vapor is the conversion factor Q which is shown in Emardson and Derks' studies and also Jade and Vijayan's. Developing a regional model using either Tm-Ts equation or the conversion factor Q will provide a basis for GNSS Meteorology in Turkey which depends on the analysis of the radiosonde profile data. For this purpose, the radiosonde profiles from Istanbul, Ankara, Diyarbaki r, Samsun, Erzurum, Izmir, Isparta and Adana stations are analyzed with the radiosonde analysis algorithm in the context of the 'The Estimation of Atmospheric Water Vapour with GPS' Project which is funded by the Scientific and Technological Research Council of Turkey (TUBITAK). The Project is also in the COST Action ES1206: Advanced Global Navigation Satellite Systems tropospheric products for monitoring severe weather events and climate (GNSS4SWEC). In this study, regional models using the conversion factor Q are used for the determination of precipitable water vapor, and applied to the GNSS derived wet tropospheric zenith delays. Henceforth, the estimated precipitable water vapor and the precipitable water vapor obtained from the radiosonde station are compared. The average of the differences between RS and models for Istanbul and Ankara stations are obtained as 2.0±1.6 mm, 1.6±1.6 mm, respectively.

  10. Channel Estimation And Multiuser Detection In Asynchronous Satellite Communications

    CERN Document Server

    Chaouech, Helmi; 10.5121/ijwmn.2010.2411

    2010-01-01

    In this paper, we propose a new method of channel estimation for asynchronous additive white Gaussian noise channels in satellite communications. This method is based on signals correlation and multiuser interference cancellation which adopts a successive structure. Propagation delays and signals amplitudes are jointly estimated in order to be used for data detection at the receiver. As, a multiuser detector, a single stage successive interference cancellation (SIC) architecture is analyzed and integrated to the channel estimation technique and the whole system is evaluated. The satellite access method adopted is the direct sequence code division multiple access (DS CDMA) one. To evaluate the channel estimation and the detection technique, we have simulated a satellite uplink with an asynchronous multiuser access.

  11. An adaptive spatial model for precipitation data from multiple satellites over large regions

    KAUST Repository

    Chakraborty, Avishek

    2015-03-01

    Satellite measurements have of late become an important source of information for climate features such as precipitation due to their near-global coverage. In this article, we look at a precipitation dataset during a 3-hour window over tropical South America that has information from two satellites. We develop a flexible hierarchical model to combine instantaneous rainrate measurements from those satellites while accounting for their potential heterogeneity. Conceptually, we envision an underlying precipitation surface that influences the observed rain as well as absence of it. The surface is specified using a mean function centered at a set of knot locations, to capture the local patterns in the rainrate, combined with a residual Gaussian process to account for global correlation across sites. To improve over the commonly used pre-fixed knot choices, an efficient reversible jump scheme is used to allow the number of such knots as well as the order and support of associated polynomial terms to be chosen adaptively. To facilitate computation over a large region, a reduced rank approximation for the parent Gaussian process is employed.

  12. 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

  13. 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.

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

    Science.gov (United States)

    Zubieta, Ricardo; Getirana, Augusto; Carlo Espinoza, Jhan; Lavado-Casimiro, Waldo; Aragon, Luis

    2017-07-01

    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.

  15. Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

    Science.gov (United States)

    Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.

    2013-01-01

    The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case

  16. Evaluation of short-period rainfall estimates from Kalpana-1 satellite using MET software

    Indian Academy of Sciences (India)

    Soma Sen Roy; Subhendu Brata Saha; Hashmi Fatima; S K Roy Bhowmik; P K Kundu

    2012-10-01

    The INSAT Multispectral Rainfall Algorithm (IMSRA) technique for rainfall estimation, has recently been developed to meet the shortcomings of the Global Precipitation Index (GPI) technique of rainfall estimation from the data of geostationary satellites; especially for accurate short period rainfall estimates. This study evaluates the 3-hourly precipitation estimates by this technique as well as the rainfall estimates by the GPI technique using data of the Kalpana-1 satellite, over the Indian region for the south-west monsoon season of 2010 to understand their relative strengths and weaknesses in estimating short period rainfall. The gridded 3 hourly accumulated TRMM satellite (3B42 V6 product or TMPA product) and surface raingauge data for stations over the Indian region for the same period is used as the standard measure of rainfall estimates. The Method for Object-based Diagnostic Evaluation (MODE) utility of the METv3.0 software, has been used for the evaluation purpose. The results show that the new IMSRA technique is closer to the TMPA rainfall estimate, in terms of areal spread, geometric shape and location of rainfall areas, as compared to the GPI technique. The overlap of matching rainfall areas with respect to TMPA rainfall patches is also higher for the IMSRA estimates as compared to the GPI values. However, both satellite rainfall estimates are observed to be generally higher compared to the TMPA measurements. However, the values for the highest 10% of the rainfall rates in any rainfall patch, is generally higher for rainfall measured by the IMSRA technique, as compared to the estimates by the GPI technique. This may partly be due to the capping maximum limit of 3 mm/hr for rainfall measured by the GPI technique limits the total 3-hour accumulation to 9 mm even during heavy rainfall episodes. This is not so with IMSRA technique, which has no such limiting value. However, this general overestimation of the rainfall amount, measured by both techniques

  17. Developments and applications of the Global Satellite Mapping of Precipitation (GSMaP) for the Global Precipitation Measurement (GPM)

    Science.gov (United States)

    Kachi, Misako; Aonashi, Kazumasa; Kubota, Takuji; Shige, Shoichi; Ushio, Tomoo; Mega, Tomoaki; Yamamoto, Munehisa; Hamada, Atsushi; Seto, Shinta; Takayabu, Yukari N.; Oki, Riko

    2016-04-01

    The Global Satellite Mapping of Precipitation (GSMaP) is a global rainfall map based on a blended Microwave-Infrared product and has been developed in Japan for the Global Precipitation Measurement (GPM) mission. To fulfill gaps of passive microwave observations, we developed a method to interpolate observations between each microwave imager by utilizing information from the Infrared imagers on board the geostationary satellites, and achieved production of an hourly global rainfall map in 0.1-degree latitude/longitude grid. The latest GSMaP version 6 product was released in September 2014 to the public as one of Japanese GPM products after the launch of the GPM Core Observatory, which is Japan and U.S. joint mission and carrying both the Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI), in February 2014. In the next version (version 7), which is scheduled to be released in the summer 2016, we plan to apply databases produced from DPR instead of those from PR, and to introduce snow retrieval algorithm for the passive microwave instruments that have higher frequency channels. The GSMaP near-real-time version (GSMaP_NRT) product is available 4-hour after observation through the "JAXA Global Rainfall Watch" web site (http://sharaku.eorc.jaxa.jp/GSMaP) since 2008. To assure near-real-time data availability, the GSMaP_NRT system simplified part of the algorithm and its processing procedure. Therefore, the GSMaP_NRT product gives higher priority to data latency than accuracy. Since its data release, GSMaP_NRT data has been used by various users for various purposes, such as rainfall monitoring, flood alert and warning, drought monitoring, crop yield forecast, and agricultural insurance. There are, however, several requirements from users for GSMaP improvements not only for accuracy but also specification. Among those requests for data specification, the most popular ones are shortening of data latency time and higher horizontal resolution. To reduce

  18. THE STOCHASTIC ESTIMATION OF SATELLITE CLOCK CORRECTION INFORMATION IN WADGPS

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Using autocorrelation information of the pseudorange errors generated by se lective availability (SA) frequency dithering, we have constructed a simple first order stochas tic model for SA effects. This model has been used in a Kalman filter to account for the stochastic behavior of SA dithering in estimating satellite clock information in wide area dif ferential GPS. We have obtained fifteen percent improvement in the user positioning using the correlation information on the satellite clock information in a Kalman filter, when comparing the results obtained using a regular least square estimation.

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

    OpenAIRE

    Hyojin Lee; Kwangmin Kang

    2015-01-01

    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...

  20. Sampling errors in rainfall estimates by multiple satellites

    Science.gov (United States)

    North, Gerald R.; Shen, Samuel S. P.; Upson, Robert

    1993-01-01

    This paper examines the sampling characteristics of combining data collected by several low-orbiting satellites attempting to estimate the space-time average of rain rates. The several satellites can have different orbital and swath-width parameters. The satellite overpasses are allowed to make partial coverage snapshots of the grid box with each overpass. Such partial visits are considered in an approximate way, letting each intersection area fraction of the grid box by a particular satellite swath be a random variable with mean and variance parameters computed from exact orbit calculations. The derivation procedure is based upon the spectral minimum mean-square error formalism introduced by North and Nakamoto. By using a simple parametric form for the spacetime spectral density, simple formulas are derived for a large number of examples, including the combination of the Tropical Rainfall Measuring Mission with an operational sun-synchronous orbiter. The approximations and results are discussed and directions for future research are summarized.

  1. Utilizing Satellite Precipitation Products to Understand the Link Between Climate Variability and Malaria

    Science.gov (United States)

    Maggioni, V.; Mousam, A.; Delamater, P. L.; Cash, B. A.; Quispe, A.

    2015-12-01

    Malaria is a public health threat to people globally leading to 198 million cases and 584,000 deaths annually. Outbreaks of vector borne diseases such as malaria can be significantly impacted by climate variables such as precipitation. For example, an increase in rainfall has the potential to create pools of water that can serve as breeding locations for mosquitos. Peru is a country that is currently controlling malaria, but has not been able to completely eliminate the disease. Despite the various initiatives in order to control malaria - including regional efforts to improve surveillance, early detection, prompt treatment, and vector management - malaria cases in Peru have risen between 2011 and 2014. The purpose of this study is to test the hypothesis that climate variability plays a fundamental role in malaria occurrence over a 12-year period (2003-2014) in Peru. When analyzing climate variability, it is important to obtain high-quality, high-resolution data for a time series long enough to draw conclusion about how climate variables have been and are changing. Remote sensing is a powerful tool for measuring and monitoring climate variables continuously in time and space. A widely used satellite-based precipitation product, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), available globally since 1998, was used to obtain 3-hourly data with a spatial resolution of 0.25° x 0.25°. The precipitation data was linked to weekly (2003-2014) malaria cases collected by health centers and available at a district level all over Peru to investigate the relationship between precipitation and the seasonal and annual variations in malaria incidence. Further studies will incorporate additional climate variables such as temperature, humidity, soil moisture, and surface pressure from remote sensing data products and climate models. Ultimately, this research will help us to understand if climate variability impacts malaria incidence

  2. Correlating Global Precipitation Measurement satellite data with karst spring hydrographs for rapid catchment delineation

    Science.gov (United States)

    Longenecker, Jake; Bechtel, Timothy; Chen, Zhao; Goldscheider, Nico; Liesch, Tanja; Walter, Robert

    2017-05-01

    To protect karst spring water resources, catchments must be known. We have developed a method for correlating spring hydrographs with newly available, high-resolution, satellite-based Global Precipitation Measurement data to rapidly and remotely locate recharge areas. We verify the method using a synthetic comparison of ground-based rain gage data with the satellite precipitation data set. Application to karst springs is proven by correlating satellite data with hydrographs from well-known springs with published catchments in Europe and North America. Application to an unknown-catchment spring in Pennsylvania suggests distant recharge, requiring a flow path that crosses topographic divides, as well as multiple lithologies, physiographic provinces, and tectonic boundaries. Although surprising, this latter result is consistent with published geologic/geophysical, monitoring well, and stream gage data. We conclude that the method has considerable potential to improve the speed and accuracy of catchment identification and hydrodynamic characterization, with applications to water resource protection and groundwater exploration, among others.

  3. Evaluating the performance of real-time streamflow forecasting using multi-satellite precipitation products in the Upper Zambezi, Africa

    Science.gov (United States)

    Demaria, E. M.; Valdes, J. B.; Wi, S.; Serrat-Capdevila, A.; Valdés-Pineda, R.; Durcik, M.

    2016-12-01

    In under-instrumented basins around the world, accurate and timely forecasts of river streamflows have the potential of assisting water and natural resource managers in their management decisions. The Upper Zambezi river basin is the largest basin in southern Africa and its water resources are critical to sustainable economic growth and poverty reduction in eight riparian countries. We present a real-time streamflow forecast for the basin using a multi-model-multi-satellite approach that allows accounting for model and input uncertainties. Three distributed hydrologic models with different levels of complexity: VIC, HYMOD_DS, and HBV_DS are setup at a daily time step and a 0.25 degree spatial resolution for the basin. The hydrologic models are calibrated against daily observed streamflows at the Katima-Mulilo station using a Genetic Algorithm. Three real-time satellite products: Climate Prediction Center's morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM-3B42RT) are bias-corrected with daily CHIRPS estimates. Uncertainty bounds for predicted flows are estimated with the Inverse Variance Weighting method. Because concentration times in the basin range from a few days to more than a week, we include the use of precipitation forecasts from the Global Forecasting System (GFS) to predict daily streamflows in the basin with a 10-days lead time. The skill of GFS-predicted streamflows is evaluated and the usefulness of the forecasts for short term water allocations is presented.

  4. DOA estimation for attitude determination on communication satellites

    Directory of Open Access Journals (Sweden)

    Yang Bin

    2014-06-01

    Full Text Available In order to determine an appropriate attitude of three-axis stabilized communication satellites, this paper describes a novel attitude determination method using direction of arrival (DOA estimation of a ground signal source. It differs from optical measurement, magnetic field measurement, inertial measurement, and global positioning system (GPS attitude determination. The proposed method is characterized by taking the ground signal source as the attitude reference and acquiring attitude information from DOA estimation. Firstly, an attitude measurement equation with DOA estimation is derived in detail. Then, the error of the measurement equation is analyzed. Finally, an attitude determination algorithm is presented using a dynamic model, the attitude measurement equation, and measurement errors. A developing low Earth orbit (LEO satellite which tests mobile communication technology with smart antennas can be stabilized in three axes by corporately using a magnetometer, reaction wheels, and three-axis magnetorquer rods. Based on the communication satellite, simulation results demonstrate the effectiveness of the method. The method could be a backup of attitude determination to prevent a system failure on the satellite. Its precision depends on the number of snapshots and the input signal-to-noise ratio (SNR with DOA estimation.

  5. DOA estimation for attitude determination on communication satellites

    Institute of Scientific and Technical Information of China (English)

    Yang Bin; He Feng; Jin Jin; Xiong Huagang; Xu Guanghan

    2014-01-01

    In order to determine an appropriate attitude of three-axis stabilized communication satellites, this paper describes a novel attitude determination method using direction of arrival (DOA) estimation of a ground signal source. It differs from optical measurement, magnetic field measurement, inertial measurement, and global positioning system (GPS) attitude determination. The proposed method is characterized by taking the ground signal source as the attitude reference and acquiring attitude information from DOA estimation. Firstly, an attitude measurement equation with DOA estimation is derived in detail. Then, the error of the measurement equation is analyzed. Finally, an attitude determination algorithm is presented using a dynamic model, the attitude measurement equation, and measurement errors. A developing low Earth orbit (LEO) satellite which tests mobile communication technology with smart antennas can be stabilized in three axes by corporately using a magnetometer, reaction wheels, and three-axis magnetorquer rods. Based on the communication satellite, simulation results demonstrate the effectiveness of the method. The method could be a backup of attitude determination to prevent a system failure on the satellite. Its precision depends on the number of snapshots and the input signal-to-noise ratio (SNR) with DOA estimation.

  6. Estimated Satellite Cluster Elements in Near Circular Orbit

    Science.gov (United States)

    1988-12-01

    values of the covariance matriz P to see if the filter performs as well as it believes it is performing [4:page 3391. 1.1.. Thuth Model The truth...between satellites will bc affected. Since the measurements contain no informa- L tion on absolute downrange position, it is impossible to estimate

  7. Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data

    OpenAIRE

    Nam, Christine C. W.; Quaas, Johannes

    2015-01-01

    Observations from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat satellites are used to evaluate clouds and precipitation in the ECHAM5 general circulation model. Active lidar and radar instruments on board CALIPSO and CloudSat allow the vertical distribution of clouds and their optical properties to be studied on a global scale. To evaluate the clouds modeled by ECHAM5 with CALIPSO and CloudSat, the lidar and radar satellite simulators of the Cloud ...

  8. Estimation of satellite antenna phase center offsets for Galileo

    Science.gov (United States)

    Steigenberger, P.; Fritsche, M.; Dach, R.; Schmid, R.; Montenbruck, O.; Uhlemann, M.; Prange, L.

    2016-08-01

    Satellite antenna phase center offsets for the Galileo In-Orbit Validation (IOV) and Full Operational Capability (FOC) satellites are estimated by two different analysis centers based on tracking data of a global GNSS network. The mean x- and y-offsets could be determined with a precision of a few centimeters. However, daily estimates of the x-offsets of the IOV satellites show pronounced systematic effects with a peak-to-peak amplitude of up to 70 cm that depend on the orbit model and the elevation of the Sun above the orbital plane. For the IOV y-offsets, no dependence on the orbit model exists but the scatter strongly depends on the elevation of the Sun above the orbital plane. In general, these systematic effects are significantly smaller for the FOC satellites. The z-offsets of the two analysis centers agree within the 10-15 cm level, and the time series do not show systematic effects. The application of an averaged Galileo satellite antenna model obtained from the two solutions results in a reduction of orbit day boundary discontinuities by up to one third—even if an independent software package is used.

  9. Investigation of S3-2 satellite data for local time variation of energetic electron precipitation

    Science.gov (United States)

    Robbe, S.; Sheldon, W. R.; Benbrook, J. R.; Bering, E. A.; Vampola, A. L.

    1994-01-01

    Data on precipitating electrons from the S3-2 satellite were investigated for local time variation at four L = 4 stations in the southern hemisphere. The equatorial pitch angles of electrons mirroring at 100 km, assumed to be the edge of the bounce loss cone, are calculated for L = 4 using the International Geomagnetic Reference Field for the epoch of the S3-2 data, along with the variation in mirror altitude per degree of equatorial pitch angle. The largest obstacle to the investigation was uneven sampling in terms of local time for all of the stations. However, this situation was improved upon by the use of S3-2 measurements at the conjugate locations of the four stations which provided additional data on electrons in the southern hemisphere bounce loss cone. Evidence for an effect of the dawn-to-dusk geoelectric field was found at two of the stations, Halley Bay and Siple, in the form of a minimum in electron precipitation at dusk. However, the present study does not completely resolve the question of local time modulation of electron precipitation at L = 4 in the southern hemisphere. Furthermore, while the average precipitation was lowest at the Kerguelen site, as would be expected on the basis of drift loss cone (DLC) theories, the intensity at that site exceeds the level that is expected on the basis of these DLC theories.

  10. Local gravity disturbance estimation from multiple-high-single-low satellite-to-satellite tracking

    Science.gov (United States)

    Jekeli, Christopher

    1989-01-01

    The idea of satellite-to-satellite tracking in the high-low mode has received renewed attention in light of the uncertain future of NASA's proposed low-low mission, Geopotential Research Mission (GRM). The principal disadvantage with a high-low system is the increased time interval required to obtain global coverage since the intersatellite visibility is often obscured by Earth. The U.S. Air Force has begun to investigate high-low satellite-to-satellite tracking between the Global Positioning System (GPS) of satellites (high component) and NASA's Space Transportation System (STS), the shuttle (low component). Because the GPS satellites form, or will form, a constellation enabling continuous three-dimensional tracking of a low-altitude orbiter, there will be no data gaps due to lack of intervisibility. Furthermore, all three components of the gravitation vector are estimable at altitude, a given grid of which gives a stronger estimate of gravity on Earth's surface than a similar grid of line-of-sight gravitation components. The proposed Air Force mission is STAGE (Shuttle-GPS Tracking for Anomalous Gravitation Estimation) and is designed for local gravity field determinations since the shuttle will likely not achieve polar orbits. The motivation for STAGE was the feasibility to obtain reasonable accuracies with absolutely minimal cost. Instead of simulating drag-free orbits, STAGE uses direct measurements of the nongravitational forces obtained by an inertial package onboard the shuttle. The sort of accuracies that would be achievable from STAGE vis-a-vis other satellite tracking missions such as GRM and European Space Agency's POPSAT-GRM are analyzed.

  11. Evaluation of Satellite Precipitation Products with Rain Gauge Data at Different Scales: Implications for Hydrological Applications

    Directory of Open Access Journals (Sweden)

    Ruifang Guo

    2016-07-01

    Full Text Available Rain gauge and satellite-retrieved data have been widely used in basin-scale hydrological applications. While rain gauges provide accurate measurements that are generally unevenly distributed in space, satellites offer spatially regular observations and common error prone retrieval. Comparative evaluation of gauge-based and satellite-based data is necessary in hydrological studies, as precipitation is the most important input in basin-scale water balance. This study uses quality-controlled rain gauge data and prevailing satellite products (Tropical Rainfall Measuring Mission (TRMM 3B43, 3B42 and 3B42RT to examine the consistency and discrepancies between them at different scales. Rain gauges and TRMM products were available in the Poyang Lake Basin, China, from 1998 to 2007 (3B42RT: 2000–2007. Our results show that the performance of TRMM products generally increases with increasing spatial scale. At both the monthly and annual scales, the accuracy is highest for TRMM 3B43, with 3B42 second and 3B42RT third. TRMM products generally overestimate precipitation because of a high frequency and degree of overestimation in light and moderate rain cases. At the daily scale, the accuracy is relatively low between TRMM 3B42 and 3B42RT. Meanwhile, the performances of TRMM 3B42 and 3B42RT are highly variable in different seasons. At both the basin and pixel scales, TRMM 3B43 and 3B42 exhibit significant discrepancies from July to September, performing worst in September. For TRMM 3B42RT, all statistical indices fluctuate and are low throughout the year, performing worst in July at the pixel scale and January at the basin scale. Furthermore, the spatial distributions of the statistical indices of TRMM 3B43 and 3B42 performed well, while TRMM 3B42RT displayed a poor performance.

  12. Evaluating Cloud and Precipitation Processes in Numerical Models using Current and Potential Future Satellite Missions

    Science.gov (United States)

    van den Heever, S. C.; Tao, W. K.; Skofronick Jackson, G.; Tanelli, S.; L'Ecuyer, T. S.; Petersen, W. A.; Kummerow, C. D.

    2015-12-01

    Cloud, aerosol and precipitation processes play a fundamental role in the water and energy cycle. It is critical to accurately represent these microphysical processes in numerical models if we are to better predict cloud and precipitation properties on weather through climate timescales. Much has been learned about cloud properties and precipitation characteristics from NASA satellite missions such as TRMM, CloudSat, and more recently GPM. Furthermore, data from these missions have been successfully utilized in evaluating the microphysical schemes in cloud-resolving models (CRMs) and global models. However, there are still many uncertainties associated with these microphysics schemes. These uncertainties can be attributed, at least in part, to the fact that microphysical processes cannot be directly observed or measured, but instead have to be inferred from those cloud properties that can be measured. Evaluation of microphysical parameterizations are becoming increasingly important as enhanced computational capabilities are facilitating the use of more sophisticated schemes in CRMs, and as future global models are being run on what has traditionally been regarded as cloud-resolving scales using CRM microphysical schemes. In this talk we will demonstrate how TRMM, CloudSat and GPM data have been used to evaluate different aspects of current CRM microphysical schemes, providing examples of where these approaches have been successful. We will also highlight CRM microphysical processes that have not been well evaluated and suggest approaches for addressing such issues. Finally, we will introduce a potential NASA satellite mission, the Cloud and Precipitation Processes Mission (CAPPM), which would facilitate the development and evaluation of different microphysical-dynamical feedbacks in numerical models.

  13. Real-time, Quasi-Global, Multi-Satellite Precipitation Analysis Using TRMM and other Satellite Observations

    Science.gov (United States)

    Adler, Robert F.; Huffman, George; Curtis, Scott; Bolvin, David; Nelkin, Eric

    2003-01-01

    A TRMM-based 3-hr analyses that use TRMM observations to calibrate polar-orbit microwave observations from SSM/I (and other satellites) and geosynchronous IR observations and merges the various calibrated observations into a final, 3-hr resolution map is described. This TRMM standard product will be available for the entire TRMM period (January 1998-present) in 2003 as part of Version 6 of the TRMM products. A real-time version of this merged product is being produced and is available at 0.25" latitude-longitude resolution over the latitude range from 50 N-500S. Examples will be shown, including its use in monitoring flood conditions and in relating weather-scale patterns to climate-scale patterns. Plans to incorporate the TRMM data and 3-hourly analysis into the Global Precipitation Climatology Project (GPCP) products are outlined. The outcome in the near future should be an improved global analysis and climatology on monthly scales for the 23 year period and finer time scale analyses for more recent periods, including 3-hourly analyses over the globe. These technique developments are potential prototypes for analyses with the Global Precipitation Measurement (GPM) mission.

  14. 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.

  15. Towards stochastically downscaled precipitation in the Tropics based on a robust 1DD combined satellite product and a high resolution IR-based rain mask

    Science.gov (United States)

    Guilloteau, Clement; Roca, Rémy; Gosset, Marielle

    2015-04-01

    In the Tropics where the ground-based rain gauges network is very sparse, satellite rainfall estimates are becoming a compulsory source of information for various applications: hydrological modeling, water resources management or vegetation-monitoring. The tropical Tropical Amount of Precipitation with Estimate of Error (TAPEER) algorithm, developed within the framework of Megha-Tropiques satellite mission is a robust estimate of surface rainfall accumulations at the daily, one degree resolution. TAPEER validation in West Africa has proven its accuracy. Nevertheless applications that involve non-linear processes (such as surface runoff) require finer space / time resolution than one degree one day, or at least the statistical characterization of the sub-grid rainfall variability. TAPEER is based on a Universally Adjusted Global Precipitation Index (UAGPI) technique. The one degree, one day estimation relies on the combination of observations from microwave radiometers embarked on the 7 platforms forming the GPM constellation of low earth orbit satellites together with geostationary infra-red (GEO-IR) imagery. TAPEER provides as an intermediate product a high-resolution rain-mask based on the GEO-IR information (2.8 km, 15 min in Africa). The main question of this work is, how to use this high-resolution mask information as a constraint for downscaling ? This work first presents the multi-scale evaluation of TAPEER's rain detection mask against ground X-band polarimetric radar data and TRMM precipitation radar data in West Africa, through wavelet transform. Other algorithms (climate prediction center morphing technique CMORPH, global satellite mapping of precipitation GSMaP, multi-sensor precipitation estimate MPE) detection capabilities are also evaluated. Spatio-temporal wavelet filtering of the detection mask is then used to compute precipitation probability at the GEO-IR resolution. The wavelet tool is finally used to stochastically generate rain / no rain field

  16. Potential for Using Satellite Lidar for Seasonal Snow Depth Estimation

    Science.gov (United States)

    Jasinski, M. F.; Stoll, J.; Harding, D. J.; Fassnacht, S. R.; Carabajal, C. C.; Markus, T.

    2013-12-01

    This study evaluates the potential for estimating snow depth in complex mountainous terrain using high resolution satellite lidar. For over three decades, satellite remote sensing of snow depth and water equivalent has relied primarily on passive microwave sensors with an approximately 25 km footprint. While successfully employed in many global water balance analyses, their large footprints, necessary to capture the natural emission of the surface, are too coarse to define the spatial heterogeneity of mountain watershed-scale snow due to variable topography and vegetation. In this study, the capability of satellite lidar altimetry for estimating snow depth was evaluated primarily using surface elevations observed by the Geoscience Laser Altimeter Sensor (GLAS) flown on board the Ice, Cloud, and land Elevation Satellite from 2003-2009, with a footprint size of ~70m. The evaluation includes the analysis of GLAS waveforms at near-repeat locations during snow-off and snow on conditions, using several snow depth estimation approaches, focusing on the Uinta Mountains of NE Utah. Also presented is the concept for the ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS), currently set to launch in July 2016, and its potential capability for characterizing snow depth. The opportunity for partnering through NASA's Early Adopter Program using prototype aircraft observations also is presented.

  17. Statistical assessment and hydrological utility of the latest multi-satellite precipitation analysis IMERG in Ganjiang River basin

    Science.gov (United States)

    Li, Na; Tang, Guoqiang; Zhao, Ping; Hong, Yang; Gou, Yabin; Yang, Kai

    2017-01-01

    This study aims to statistically and hydrologically assess the hydrological utility of the latest Integrated Multi-satellitE Retrievals from Global Precipitation Measurement (IMERG) multi-satellite constellation over the mid-latitude Ganjiang River basin in China. The investigations are conducted at hourly and 0.1° resolutions throughout the rainy season from March 12 to September 30, 2014. Two high-quality quantitative precipitation estimation (QPE) datasets, i.e., a gauge-corrected radar mosaic QPE product (RQPE) and a highly dense network of 1200 rain gauges, are used as the reference. For the implementation of the study, first, we compare IMERG product and RQPE with rain gauge-interpolated data, respectively. The results indicate that both remote sensing products can estimate precipitation fairly well over the basin, while RQPE significantly outperforms IMERG product in almost all the studied cases. The correlation coefficients of RQPE (CC = 0.98 and CC = 0.67) are much higher than those of IMERG product (CC = 0.80 and CC = 0.33) at basin and grid scales, respectively. Then, the hydrological assessment is conducted with the Coupled Routing and Excess Storage (CREST) model under multiple parameterization scenarios, in which the model is calibrated using the rain gauge-interpolated data, RQPE, and IMERG products respectively. During the calibration period (from March 12 to May 31), the simulated streamflow based on rain gauge-interpolated data shows the highest Nash-Sutcliffe coefficient efficiency (NSCE) value (0.92), closely followed by the RQPE (NSCE = 0.84), while IMERG product performs barely acceptable (NSCE = 0.56). During the validation period (from June 1 to September 30), the three rainfall datasets are used to force the CREST model based on all the three calibrated parameter sets (i.e., nine combinations in total). RQPE outperforms rain gauge-interpolated data and IMERG product in all validation scenarios, possibly due to its advantageous capability

  18. Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

    Science.gov (United States)

    Veerakachen, Watcharee; Raksapatcharawong, Mongkol

    2015-09-01

    Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

  19. Satellite images analysis for shadow detection and building height estimation

    Science.gov (United States)

    Liasis, Gregoris; Stavrou, Stavros

    2016-09-01

    Satellite images can provide valuable information about the presented urban landscape scenes to remote sensing and telecommunication applications. Obtaining information from satellite images is difficult since all the objects and their surroundings are presented with feature complexity. The shadows cast by buildings in urban scenes can be processed and used for estimating building heights. Thus, a robust and accurate building shadow detection process is important. Region-based active contour models can be used for satellite image segmentation. However, spectral heterogeneity that usually exists in satellite images and the feature similarity representing the shadow and several non-shadow regions makes building shadow detection challenging. In this work, a new automated method for delineating building shadows is proposed. Initially, spectral and spatial features of the satellite image are utilized for designing a custom filter to enhance shadows and reduce intensity heterogeneity. An effective iterative procedure using intensity differences is developed for tuning and subsequently selecting the most appropriate filter settings, able to highlight the building shadows. The response of the filter is then used for automatically estimating the radiometric property of the shadows. The customized filter and the radiometric feature are utilized to form an optimized active contour model where the contours are biased to delineate shadow regions. Post-processing morphological operations are also developed and applied for removing misleading artefacts. Finally, building heights are approximated using shadow length and the predefined or estimated solar elevation angle. Qualitative and quantitative measures are used for evaluating the performance of the proposed method for both shadow detection and building height estimation.

  20. Assimilation of radar precipitation and satellite data into a NWP model using a physical initialisation scheme

    Science.gov (United States)

    Milan, M.; Schüttemeyer, D.; Venema, V.; Simmer, C.

    2009-04-01

    We implemented a PI (Physical Initialization) method in the non hydrostatic limited-area model COSMO (version 4.2) of the DWD (German Meteorological Service). The goal is the improvement of quantitative rain nowcasting with a high resolution NWP model. Input radar data is a DWD product: the national radar composite for 16 radars with a spatial resolution of one kilometer and a time resolution of 5 minutes. The conversion from reflectivity to rain rate is already made by DWD. This data is interpolated on the LM grid ( 2.8 × 2.8 km resolution) in order to calculate the analysed precipitation rate which depends on the observed precipitation and the model precipitation. The PIB (Physical Initialization Bonn) takes as input the radar based precipitation product and a cloud top height field retrieved from satellite observations, in our case we are using the SAFNWC products generated from Meteosat Second Generation data by DWD. During the assimilation window PIB adjusts the vertical wind, humidity, cloud water and cloud ice in order to force the model state towards the measurements. The most distinctive feature of the algorithm is the adjustment of the vertical wind profile in the framework of a simple precipitation scheme. The PIB assumes that the rain rate is proportional to the vertical humidity flux at cloud base and the vertical wind is adapted according to the conversion efficiency of saturated water vapor into rain water at the cloud base. This parameter is dynamically adjusted by the comparison between the model precipitation and the radar precipitation. The model is tested in convective cases over Germany, an identical twin experiment is used in order to demonstrate the consistency of PIB with the physics of the NWP model. In the tests which we have already performed this method has improved the forecast of the precipitation patterns, as well as the dynamics of the events. These improvements are found both during the assimilation window and for the first hours

  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. Assessment of Satellite-based Precipitation Products (TRMM) in Hydrologic Modeling: Case Studies from Northern Morocco

    Science.gov (United States)

    EL kadiri, R.; Milewski, A.; Durham, M.

    2012-12-01

    Precipitation is the most important forcing parameter in hydrological modeling, yet it is largely unknown in the arid Middle East. We assessed the magnitude, probability of detection, and false alarm rates of various rainfall satellite products (e.g., TRMM, RFE2.0) compared to in situ gauge data (~79 stations) across the Our Er Rbia, Sebou, and Melouya Watersheds in Northern Morocco. Precipitation over the area is relatively high with an average of ~400mm/year according to TRMM (1998-2008). The existing gauges indicate that the average annual precipitation across the Tadla and Coastal Plains region is 260mm/year and 390mm/year across the Atlas Mountains. Following the assessment of satellite products against in situ gauge data, we evaluated the effects (e.g., runoff and recharge amounts) of using satellite driven hydrologic models using SWAT. Specifically, we performed a four-fold exercise: (1) The first stage focused on the analysis of the rainfall products; (2) the second stage involved the construction of a rainfall-runoff model using gauge data; (3) the third stage entailed the calibration of the model against flow gauges and/or dams storage variability, and (4) model simulation using satellite based rainfall products using the calibrated parameters from the initial simulation. Results suggest the TRMM V7 has a much better correlation with the field data over the Oum Er Rbia watershed. The Correlation E (Nash-Suncliffe coefficient) has a positive value of 0.5, while the correlation coefficient of TRMM V6 vs. gauges data is a negative value of -0.25. This first order evaluation of the TRMM V7 shows the new algorithm has partially overcame the underestimation effect in the semi-arid environments. However, more research needs to be done to increase the usability of TRMM V7 in hydrologic models. Low correlations are most likely a result of the following: (1) snow at the high elevations in the Oum Er Rbia watershed, (2) the ocean effect on TRMM measurements along

  3. Velocity estimation of an airplane through a single satellite image

    Institute of Scientific and Technical Information of China (English)

    Zhuxin Zhao; Gongjian Wen; Bingwei Hui; Deren Li

    2012-01-01

    The motion information of a moving target can be recorded in a single image by a push-broom satellite. A push-broom satellite image is composed of many image lines sensed at different time instants. A method to estimate the velocity of a flying airplane from a single image based on the imagery model of the linear push-broom sensor is proposed. Some key points on the high-resolution image of the plane are chosen to determine the velocity (speed and direction). The performance of the method is tested and verified by experiments using a WorldView-1 image.%The motion information of a moving target can be recorded in a single image by a push-broom satellite.A push-broom satellite image is composed of many image lines sensed at different time instants.A method to estimate the velocity of a flying airplane from a single image based on the imagery model of the linear push-broom sensor is proposed.Some key points on the high-resolution image of the plane are chosen to determine the velocity (speed and direction).The performance of the method is tested and verified by experiments using a WorldView-1 image.

  4. A novel approach to validate satellite soil moisture retrievals using precipitation data

    Science.gov (United States)

    Karthikeyan, L.; Kumar, D. Nagesh

    2016-10-01

    A novel approach is proposed that attempts to validate passive microwave soil moisture retrievals using precipitation data (applied over India). It is based on the concept that the expectation of precipitation conditioned on soil moisture follows a sigmoidal convex-concave-shaped curve, the characteristic of which was recently shown to be represented by mutual information estimated between soil moisture and precipitation. On this basis, with an emphasis over distribution-free nonparametric computations, a new measure called Copula-Kernel Density Estimator based Mutual Information (CKDEMI) is introduced. The validation approach is generic in nature and utilizes CKDEMI in tandem with a couple of proposed bootstrap strategies, to check accuracy of any two soil moisture products (here Advanced Microwave Scanning Radiometer-EOS sensor's Vrije Universiteit Amsterdam-NASA (VUAN) and University of Montana (MONT) products) using precipitation (India Meteorological Department) data. The proposed technique yields a "best choice soil moisture product" map which contains locations where any one of the two/none of the two/both the products have produced accurate retrievals. The results indicated that in general, VUA-NASA product has performed well over University of Montana's product for India. The best choice soil moisture map is then integrated with land use land cover and elevation information using a novel probability density function-based procedure to gain insight on conditions under which each of the products has performed well. Finally, the impact of using a different precipitation (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) data set over the best choice soil moisture product map is also analyzed. The proposed methodology assists researchers and practitioners in selecting the appropriate soil moisture product for various assimilation strategies at both basin and continental scales.

  5. Daily Emission Estimates in China Constrained by Satellite Observations

    Science.gov (United States)

    Mijling, B.; van der A, R.

    2013-01-01

    Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. We present a new algorithm specifically designed to use daily satellite observations of column concentrations for fast updates of emission estimates of short-lived atmospheric constituents on a mesoscopic scale (~25Å~25 km2). The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates of East China, using the CHIMERE model on a 0.25 degree resolution together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments.

  6. Sampling errors in satellite estimates of tropical rain

    Science.gov (United States)

    Mcconnell, Alan; North, Gerald R.

    1987-01-01

    The GATE rainfall data set is used in a statistical study to estimate the sampling errors that might be expected for the type of snapshot sampling that a low earth-orbiting satellite makes. For averages over the entire 400-km square and for the duration of several weeks, strong evidence is found that sampling errors less than 10 percent can be expected in contributions from each of four rain rate categories which individually account for about one quarter of the total rain.

  7. Calibration of Ocean Forcing with satellite Flux Estimates (COFFEE)

    Science.gov (United States)

    Barron, Charlie; Jan, Dastugue; Jackie, May; Rowley, Clark; Smith, Scott; Spence, Peter; Gremes-Cordero, Silvia

    2016-04-01

    Predicting the evolution of ocean temperature in regional ocean models depends on estimates of surface heat fluxes and upper-ocean processes over the forecast period. Within the COFFEE project (Calibration of Ocean Forcing with satellite Flux Estimates, real-time satellite observations are used to estimate shortwave, longwave, sensible, and latent air-sea heat flux corrections to a background estimate from the prior day's regional or global model forecast. These satellite-corrected fluxes are used to prepare a corrected ocean hindcast and to estimate flux error covariances to project the heat flux corrections for a 3-5 day forecast. In this way, 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. While traditional assimilation of sea surface temperature (SST) observations re-centers ocean models at the start of each forecast cycle, COFFEE endeavors to appropriately partition and reduce among various 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 operational global or regional atmospheric forcing. Experiment cases combine different levels of flux calibration with assimilation alternatives. The cases 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

  8. Improvement in airsea flux estimates derived from satellite observations

    OpenAIRE

    Bentamy, Abderrahim; Grodsky, Semyon A.; Katsaros, Kristina; Mestas-nunez, Alberto M.; Blanke, Bruno; Desbiolles, Fabien

    2013-01-01

    A new method is developed to estimate daily turbulent airsea fluxes over the global ocean on a 0.25 degrees grid. The required surface wind speed (w(10)) and specific air humidity (q(10)) at 10m height are both estimated from remotely sensed measurements. w(10) is obtained from the SeaWinds scatterometer on board the QuikSCAT satellite. A new empirical model relating brightness temperatures (T-b) from the Special Sensor Microwave Imager (SSM/I) and q(10) is developed. It is an extension of th...

  9. A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China

    Directory of Open Access Journals (Sweden)

    Wenlong Jing

    2016-10-01

    Full Text Available Environmental monitoring of Earth from space has provided invaluable information for understanding land–atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation–land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day–night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI, the Digital Elevation Model (DEM, and geolocation (longitude, latitude. Four machine learning regression algorithms, the classification and regression tree (CART, the k-nearest neighbors (k-NN, the support vector machine (SVM, and random forests (RF, were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North China for the purpose of comparison of algorithm performance. The downscaled results were validated based on observations from meteorological stations and were also compared to a previous downscaling algorithm. According to the validation results, the RF-based model produced the results with the highest accuracy. It was followed by SVM, CART, and k-NN, but the accuracy of the downscaled results using SVM relied greatly on residual correction. The downscaled results were well correlated with the observations during the year, but the accuracies were relatively lower in July to September. Downscaling errors increase as monthly total precipitation increases, but the RF model was less affected by this proportional effect between errors and observation compared with the other algorithms. The variable importances of the land surface temperature (LST feature variables were higher than those of NDVI, which indicates the significance of considering the precipitation–land surface temperature

  10. MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data

    Science.gov (United States)

    Beck, Hylke E.; van Dijk, Albert I. J. M.; Levizzani, Vincenzo; Schellekens, Jaap; Miralles, Diego G.; Martens, Brecht; de Roo, Ad

    2017-01-01

    Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979-2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (http://www.gloh2o.org.

  11. 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.

  12. Assessment of GPM and TRMM Multi-Satellite Precipitation Products in Streamflow Simulations in a Data-Sparse Mountainous Watershed in Myanmar

    National Research Council Canada - National Science Library

    Fei Yuan; Limin Zhang; Khin Wah Wah Win; Liliang Ren; Chongxu Zhao; Yonghua Zhu; Shanhu Jiang; Yi Liu

    2017-01-01

    ...) final run and the TRMM Multi-satellite Precipitation Analysis 3B42V7 precipitation products, and their feasibility in streamflow simulations in the Chindwin River basin, Myanmar, from April 2014...

  13. 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...... the uncertainty of the weather radar rainfall input. The main findings of this work, is that the input uncertainty propagate through the urban drainage model with significant effects on the model result. The GLUE methodology is in general a usable way to explore this uncertainty although; the exact width...... of the prediction bands can be questioned, due to the subjective nature of the method. Moreover, the method also gives very useful information about the model and parameter behaviour....

  14. Disentangling the Relationships between Net Primary Production and Precipitation in Southern Africa Savannas Using Satellite Observations from 1982 to 2010

    Directory of Open Access Journals (Sweden)

    Jane Southworth

    2013-08-01

    Full Text Available To obtain a better understanding of the variability in net primary production (NPP in savannas is important for the study of the global carbon cycle and the management of this particular ecosystem. Using satellite and precipitation data sets, we investigated the variations in NPP in southern African savannas from 1982 to 2010, and disentangled the relationships between NPP and precipitation by land cover classes and mean annual precipitation (MAP gradients. Specifically, we evaluate the utility of the third generation Global Inventory Monitoring and Modeling System (GIMMS3g normalized difference vegetation index (NDVI dataset, in comparison with Moderate-resolution Imaging Spectroradiometer (MODIS derived NPP products, and find strong relationships between the overlapping data periods (2000–2010, such that we can apply our model to derive NPP estimates to the full 29-year NDVI time-series. Generally, the northern portion of the study area is characterized by high NPP and low variability, whereas the southern portion is characteristic of low NPP and high variability. During the period 1982 through 2010, NPP has reduced at a rate of −2.13 g∙C∙m−2∙yr−1 (p < 0.1, corresponding to a decrease of 6.7% over 29 years, and about half of bush and grassland savanna has experienced a decrease in NPP. There is a significant positive relationship between mean annual NPP and MAP in bush and grassland savannas, but no significant relationship is observed in tree savannas. The relationship between mean annual NPP and MAP varies with increases in MAP, characterized as a linear relationship that breaks down when MAP exceeding around 850–900 mm.

  15. Combining METEOSAT-10 satellite image data with GPS tropospheric path delays to estimate regional Integrated Water Vapor (IWV) distribution

    OpenAIRE

    2016-01-01

    Using GPS satellites signals, we can study different processes and coupling mechanisms that can help us understand the physical conditions in the upper atmosphere, which might lead or act as proxies for severe weather events such as extreme storms and flooding. GPS signals received by ground stations are multi-purpose and can also provide estimates of tropospheric zenith delays, which can be converted into mm-accuracy Precipitable Water Vapor (PWV) using collocated pressure and temperature me...

  16. Hail detection algorithm for the Global Precipitation Measuring mission core satellite sensors

    Science.gov (United States)

    Mroz, Kamil; Battaglia, Alessandro; Lang, Timothy J.; Tanelli, Simone; Cecil, Daniel J.; Tridon, Frederic

    2017-04-01

    By exploiting an abundant number of extreme storms observed simultaneously by the Global Precipitation Measurement (GPM) mission core satellite's suite of sensors and by the ground-based S-band Next-Generation Radar (NEXRAD) network over continental US, proxies for the identification of hail are developed based on the GPM core satellite observables. The full capabilities of the GPM observatory are tested by analyzing more than twenty observables and adopting the hydrometeor classification based on ground-based polarimetric measurements as truth. The proxies have been tested using the Critical Success Index (CSI) as a verification measure. The hail detection algorithm based on the mean Ku reflectivity in the mixed-phase layer performs the best, out of all considered proxies (CSI of 45%). Outside the Dual frequency Precipitation Radar (DPR) swath, the Polarization Corrected Temperature at 18.7 GHz shows the greatest potential for hail detection among all GMI channels (CSI of 26% at a threshold value of 261 K). When dual variable proxies are considered, the combination involving the mixed-phase reflectivity values at both Ku and Ka-bands outperforms all the other proxies, with a CSI of 49%. The best-performing radar-radiometer algorithm is based on the mixed-phase reflectivity at Ku-band and on the brightness temperature (TB) at 10.7 GHz (CSI of 46%). When only radiometric data are available, the algorithm based on the TBs at 36.6 and 166 GHz is the most efficient, with a CSI of 27.5%.

  17. GPS SATELLITE SIMULATOR SIGNAL ESTIMATION BASED ON ANN

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Multi-channel Global Positioning System (GPS) satellite signal simulator is used to provide realistic test signals for GPS receivers and navigation systems. In this paper, signals arriving the antenna of GPS receiver are analyzed from the viewpoint of simulator design. The estimation methods are focused of which several signal parameters are difficult to determine directly according to existing experiential models due to various error factors. Based on the theory of Artificial Neural Network (ANN), an approach is proposed to simulate signal propagation delay,carrier phase, power, and other parameters using ANN. The architecture of the hardware-in-the-loop test system is given. The ANN training and validation process is described. Experimental results demonstrate that the ANN designed can statistically simulate sample data in high fidelity.Therefore the computation of signal state based on this ANN can meet the design requirement,and can be directly applied to the development of multi-channel GPS satellite signal simulator.

  18. Fast Emission Estimates in China Constrained by Satellite Observations (Invited)

    Science.gov (United States)

    Mijling, B.; van der A, R.

    2013-12-01

    Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Unfortunately, bottom-up emission inventories, compiled from large quantities of statistical data, are easily outdated for an emerging economy such as China, where rapid economic growth changes emissions accordingly. Alternatively, top-down emission estimates from satellite observations of air constituents have important advantages of being spatial consistent, having high temporal resolution, and enabling emission updates shortly after the satellite data become available. Constraining emissions from concentration measurements is, however, computationally challenging. Within the GlobEmission project of the European Space Agency (ESA) a new algorithm has been developed, specifically designed for fast daily emission estimates of short-lived atmospheric species on a mesoscopic scale (0.25 × 0.25 degree) from satellite observations of column concentrations. The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates in East China, using the CHIMERE model together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. The observations are used to construct a monthly emission time series, which reveal important emission trends such as the emission reduction measures during the Beijing Olympic Games, and the impact and recovery from the global economic crisis. The algorithm is also able to detect emerging sources (e.g. new power plants) and improve emission information for areas where proxy data are not or badly known (e.g. shipping emissions). The new emission estimates result in a better

  19. Mediterranean hurricanes: large-scale environment and convective and precipitating areas from satellite microwave observations

    Directory of Open Access Journals (Sweden)

    C. Claud

    2010-10-01

    Full Text Available Subsynoptic scale vortices that have been likened to tropical cyclones or polar lows (medicanes are occasionally observed over the Mediterranean Sea. Generated over the sea, they are usually associated with strong winds and heavy precipitation and thus can be highly destructive in islands and costal areas. Only an accurate forecasting of such systems could mitigate these effects. However, at the moment, the predictability of these systems remains limited.

    Due to the scarcity of conventional observations, use is made of NOAA/MetOp satellite observations, for which advantage can be taken of the time coverage differences between the platforms that carry it, to give a very complete temporal description of the disturbances. A combination of AMSU-B (Advanced Microwave Sounding Unit-B/MHS (Microwave Humidity Sounder observations permit to investigate precipitation associated with these systems while coincident AMSU-A (Advanced Microwave Sounding Unit-A observations give insights into the larger synoptic-scale environment in which they occur.

    Three different cases (in terms of intensity, location, trajectory, duration, and periods of the year – May, September and December, respectively were investigated. Throughout these time periods, AMSU-A observations show that the persisting deep outflow of cold air over the sea together with an upper-level trough upstream constituted a favourable environment for the development of medicanes. AMSU-B/MHS based diagnostics show that convection and precipitation areas are large in the early stage of the low, but significantly reduced afterwards. Convection is maximum just after the upper-level trough, located upstream of cold mid-tropospheric air, reached its maximum intensity and acquired a cyclonic orientation.

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

    Science.gov (United States)

    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.

  1. Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique

    Science.gov (United States)

    Tote, Carolien; Patricio, Domingos; Boogaard, Hendrik; van der Wijngaart, Raymond; Tarnavsky, Elena; Funk, Christopher C.

    2015-01-01

    Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

  2. Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique

    Directory of Open Access Journals (Sweden)

    Carolien Toté

    2015-02-01

    Full Text Available Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT v2.0, Famine Early Warning System NETwork (FEWS NET Rainfall Estimate (RFE v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS are compared to independent gauge data (2001–2012. This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

  3. Bayesian estimation of animal movement from archival and satellite tags.

    Directory of Open Access Journals (Sweden)

    Michael D Sumner

    Full Text Available The reliable estimation of animal location, and its associated error is fundamental to animal ecology. There are many existing techniques for handling location error, but these are often ad hoc or are used in isolation from each other. In this study we present a Bayesian framework for determining location that uses all the data available, is flexible to all tagging techniques, and provides location estimates with built-in measures of uncertainty. Bayesian methods allow the contributions of multiple data sources to be decomposed into manageable components. We illustrate with two examples for two different location methods: satellite tracking and light level geo-location. We show that many of the problems with uncertainty involved are reduced and quantified by our approach. This approach can use any available information, such as existing knowledge of the animal's potential range, light levels or direct location estimates, auxiliary data, and movement models. The approach provides a substantial contribution to the handling uncertainty in archival tag and satellite tracking data using readily available tools.

  4. 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.

  5. PM-GCD - A combined IR-MW satellite technique for frequent retrieval of heavy precipitation: Application to the EU FLASH project

    Science.gov (United States)

    Casella, Daniele; Dietrich, Stefano; di Paola, Francesco; Formenton, Marco; Mugnai, Alberto; Sanò, Paolo

    2010-05-01

    Precipitation retrievals based on measurements from microwave (MW) radiometers onboard low-Earth-orbit (LEO) satellites can reach a high level of accuracy - and especially so, for deep convective precipitating systems. However, these observations do not provide a satisfactorily coverage of the rapid evolution of intense precipitating systems. As a result, the obtained precipitation retrievals are often of limited use for many important applications -- including support to authorities in activating flood alarms. To avoid this problem, several techniques have been developed that combine accurate MW estimates with frequent infrared (IR) observations from geosynchronous (GEO) satellites, such as the European Meteosat Second Generation (MSG). Within the European Union FP6 FLASH project, we have developed a new combined MW-IR technique for producing frequent precipitation retrievals from space (which we call PM-GCD technique). This technique uses passive-microwave (PM) retrievals in conjunction with the Global Convection Detection (GCD) technique that discriminates deep convective clouds within the GEO observations, based on the difference between the water vapor (6.2 μm ) and thermal-IR (10.8 μm ) channels. In essence, within the PM-GCD technique, deep convective areas are defined from MSG observations, then calibrated using MW-AMSU precipitation retrievals and finally propagated over time with a simple tracking algorithm. In this paper, we describe the PM-GCD technique and discuss the results of its application to a flood event that occurred on September 12-15, 2006 over the north-western Mediterranean coastal areas, and that has been selected for joint research by the EU FLASH and HYDRATE projects.

  6. Evaluation of TRMM Multi-satellite Precipitation Analysis (TMPA performance in the Central Andes region and its dependency on spatial and temporal resolution

    Directory of Open Access Journals (Sweden)

    M. L. M. Scheel

    2010-10-01

    Full Text Available Climate time series are of major importance for base line studies for climate change impact and adaptation projects. However, in mountain regions and in developing countries there exist significant gaps in ground based climate records in space and time. Specifically, in the Peruvian Andes spatially and temporally coherent precipitation information is a prerequisite for ongoing climate change adaptation projects in the fields of water resources, disasters and food security. The present work aims at evaluating the ability of Tropical Rainfall Measurement Mission (TRMM Multi-satellite Precipitation Analysis (TMPA to estimate precipitation rates at daily 0.25° × 0.25° scale in the Central Andes and the dependency of the estimate performance on changing spatial and temporal resolution. Comparison of the TMPA product with gauge measurements in the regions of Cuzco, Peru and La Paz, Bolivia were carried out and analysed statistically. Large biases are identified in both investigation areas in the estimation of daily precipitation amounts. The occurrence of strong precipitation events was well assessed, but their intensities were underestimated. TMPA estimates for La Paz show high false alarm ratio.

    The dependency of the TMPA estimate quality with changing resolution was analysed by comparisons of 1-, 7-, 15- and 30-day sums for Cuzco, Peru. The correlation of TMPA estimates with ground data increases strongly and almost linearly with temporal aggregation. The spatial aggregation to 0.5°, 0.75° and 1° grid box averaged precipitation and its comparison to gauge data of the same areas revealed no significant change in correlation coefficients and estimate performance.

    In order to profit from the TMPA combination product on a daily basis, a procedure to blend it with daily precipitation gauge measurements is proposed.

    Different sources of errors and uncertainties introduced by the sensors, sensor-specific algorithm aspects

  7. Precipitation estimates from MSG SEVIRI daytime, night-time and twilight data with random forests

    Science.gov (United States)

    Kühnlein, Meike; Appelhans, Tim; Thies, Boris; Nauss, Thomas

    2014-05-01

    We introduce a new rainfall retrieval technique based on MSG SEVIRI data which aims to retrieve rainfall rates in a continuous manner (day, twilight and night) at high temporal resolution. Due to the deficiencies of existing optical rainfall retrievals, the focus of this technique is on assigning rainfall rates to precipitating cloud areas in connection with extra-tropical cyclones in mid-latitudes including both convective and advective-stratiform precipitating cloud areas. The technique is realized in three steps: (i) Precipitating cloud areas are identified. (ii) The precipitating cloud areas are separated into convective and advective-stratiform precipitating areas. (iii) Rainfall rates are assigned to the convective and advective-stratiform precipitating areas, respectively. Therefore, considering the dominant precipitation processes of convective and advective-stratiform precipitation areas within extra-tropical cyclones, satellite-based information on the cloud top height, cloud top temperature, cloud phase and cloud water path are used to retrieve information about precipitation. The approach uses the ensemble classification and regression technique random forests to develop the prediction algorithms. Random forest models contain a combination of characteristics that make them well suited for its application in precipitation remote sensing. One of the key advantages is the ability to capture non-linear association of patterns between predictors and response which becomes important when dealing with complex non-linear events like precipitation. Using a machine learning approach differentiates the proposed technique from most state-of-the-art satellite-based rainfall retrievals which generally use conventional parametric approaches. To train and validate the model, the radar-based RADOLAN RW product from the German Weather Service (DWD) is used which provides area-wide gauge-adjusted hourly precipitation information. Beside the overall performance of the

  8. Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting

    Science.gov (United States)

    Wardah, T.; Abu Bakar, S. H.; Bardossy, A.; Maznorizan, M.

    2008-07-01

    SummaryFrequent flash-floods causing immense devastation in the Klang River Basin of Malaysia necessitate an improvement in the real-time forecasting systems being used. The use of meteorological satellite images in estimating rainfall has become an attractive option for improving the performance of flood forecasting-and-warning systems. In this study, a rainfall estimation algorithm using the infrared (IR) information from the Geostationary Meteorological Satellite-5 (GMS-5) is developed for potential input in a flood forecasting system. Data from the records of GMS-5 IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation neural network. The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud-top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three numerical weather prediction (NWP) products, namely the precipitable water content, relative humidity, and vertical wind, are also included as inputs. The algorithm is applied for the areal rainfall estimation in the upper Klang River Basin and compared with another technique that uses power-law regression between the cloud-top brightness-temperature and radar rain rate. Results from both techniques are validated against previously recorded Thiessen areal-averaged rainfall values with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the artificial neural network (ANN) technique, respectively. An extra lead time of around 2 h is gained when the satellite-based ANN rainfall estimation is coupled with a rainfall-runoff model to forecast a flash-flood event in the upper Klang River Basin.

  9. On the balance of precipitation and evaporation over global oceans in satellite based and reanalysis data sets

    Science.gov (United States)

    Bakan, S.; Andersson, A.; Fennig, K.; Klepp, C.; Klocke, D.; Schulz, J.

    2009-04-01

    Over the global oceans, precipitation should be smaller than evaporation and the balance should be compensated by the global runoff from land surfaces. But to which extent do satellite climatologies and reanalysis products reproduce this basic feature of the global water cycle? The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data set, HOAPS-3 (www.hoaps.org), contains fields of precipitation and evaporation over the global ocean and all basic state variables needed for the derivation of the fluxes. Except for the NOAA Pathfinder SST data set, all variables are derived from SSM/I satellite data over the ice free global ocean between 1987 and 2005. Special emphasis has been put into quality control and inter-satellite calibration in order to derive the data fields as homogeneous as possible. One of the major design goals of HOAPS was to provide a data set that is based exclusively on retrieval procedures which avoid any additional model or reanalysis input. On a global scale, the average evaporation since 1987 exceeds precipitation rate over the oceans in HOAPS-3 systematically, with almost negligible yearly cycle and small monthly variations. While the globally averaged precipitation time series does not exhibit any significant trend over the study period, evaporation shows a continuous increase during this time. Regionally, this increase concentrates in the subtropics and is, together with some reduction in precipitation, consistent with a strengthening of the Hadley circulation during the observation period. These results are compared with similar data fields of the same period from various satellite climatologies to insure the consistency of our results and to the NCEP and ERA40 as well as ERAInterim reanalysis products. Remarkable similarities and differences between the different information sources have been found and will be discussed in the presentation.

  10. nowCOAST's Map Service for NOAA Quantitative Precipitation Estimates (Time Enabled)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Map Information: This nowCOAST time-enabled map service provides maps depicting the NWS Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate mosaics...

  11. Evaluating Frontal Precipitation with a Spectral Microphysics Mesoscale Model and a Satellite Simulator as Compared to Radar and Radiometer Observations

    Science.gov (United States)

    Han, M.; Braun, S. A.; Matsui, T.; Iguchi, T.; Williams, C. R.

    2013-12-01

    The Advanced Microwave Scanning Radiometer for EOS (AMSR-E) onboard NASA Aqua satellite and a ground-based precipitation profiling radar sampled a frontal precipitation event in the US west coast on 30 to 31 December 2005. Simulations with bulk microphysics schemes in the Weather Research and Forecast (WRF) model have been evaluated with those remote sensing data. In the current study, we continue similar work to evaluate a spectral bin microphysics (SBM) scheme, HUCM, in the WRF model. The Goddard-Satellite Data Simulation Unit (G-SDSU) is used to simulate quantities observed by the radar and radiometer. With advanced representation of cloud and precipitation microphysics processes, the HUCM scheme predicts distributions of 7 hydrometeor species as storms evolve. In this study, the simulation with HUCM well captured the structure of the precipitation and its microphysics characteristics. In addition, it improved total precipitation ice mass simulation and corrected, to a certain extent, the large low bias of ice scattering signature in the bulk scheme simulations. However, the radar reflectivity simulations with the HUCM scheme were not improved as compared to the bulk schemes. We conducted investigations to understand how microphysical processes and properties, such as snow break up parameter and particle fall velocities would influence precipitation size distribution and spectrum of water paths, and further modify radar and/or radiometer simulations. Influence by ice nuclei is going to be examined as well.

  12. Estimating Ground-Level Particulate Matter (PM) Concentration using Satellite-derived Aerosol Optical Depth (AOD)

    Science.gov (United States)

    Park, Seohui; Im, Jungho

    2017-04-01

    Atmospheric aerosols are strongly associated with adverse human health effects. In particular, particulate matter less than 10 micrometers and 2.5 micrometers (i.e., PM10 and PM2.5, respectively) can cause cardiovascular and lung diseases such as asthma and chronic obstructive pulmonary disease (COPD). Air quality including PM has typically been monitored using station-based in-situ measurements over the world. However, in situ measurements do not provide spatial continuity over large areas. An alternative approach is to use satellite remote sensing as it provides data over vast areas at high temporal resolution. The literature shows that PM concentrations are related with Aerosol Optical Depth (AOD) that is derived from satellite observations, but it is still difficult to identify PM concentrations directly from AOD. Some studies used statistical approaches for estimating PM concentrations from AOD while some others combined numerical models and satellite-derived AOD. In this study, satellite-derived products were used to estimate ground PM concentrations based on machine learning over South Korea. Satellite-derived products include AOD from Geostationary Ocean Color Imager (GOCI), precipitation from Tropical Rainfall Measuring Mission (TRMM), soil moisture from AMSR-2, elevation from Shuttle Radar Topography Mission (SRTM), and land cover, land surface temperature and normalized difference vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS). PM concentrations data were collected from 318 stations. A statistical ordinary least squares (OLS) approach was also tested and compared with the machine learning approach (i.e., random forest). PM concentration was estimated during spring season (from March to May) in 2015 that typically shows high concentration of PM. The randomly selected 80% of data were used for model calibration and the remaining 20% were used for validation. The developed models were further tested for prediction of PM

  13. Validation of NASA-TRMM MPA Precipitation Estimates During Tropical Storms Using Gauge and Radar-Based Estimates

    Science.gov (United States)

    Henschke, A. E.; Habib, E.

    2008-05-01

    The purpose of this study is the validation of the 3B42 and 3B42-RT rainfall products from NASA's Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) during major tropical rainfall events throughout the state of Louisiana. The 3B42-RT product, a near real time dataset, and the 3B42 product, a gauge calibrated dataset, are available at .25° x .25°, 3-hourly resolution, covering the globe from 50°N latitude to 50°S latitude. In order to investigate the validity of the TMPA data, radar-based and rain gauge datasets were used as reference. The radar-based dataset, a product of the NWS Stage IV multi- sensor precipitation estimation (MPE) algorithm, is available at 1-hourly intervals on a 4km x 4km spatial scale. The rain gauge dataset was obtained on an hourly scale from a national gauge network maintained by the National Climatic Data Center (NCDC). During the study, six tropical storm periods between 2002 and 2005, ranging in length from three to five days, were examined (Hurricane Lili, October 2002; Tropical Storm Bill, June 2003; Hurricane Ivan, September 2004; Tropical Storm Matthew, October 2004; Hurricane Katrina, August 2005; and Hurricane Rita, September 2005). During the analyzed storms, the radar and rain gauge data were averaged spatially and temporally to match the resolution of the TMPA pixels. The number of pixels studied during each storm varied from three to six pixels, with a minimum requirement of three gauges per 3B42 pixel, depending on the gauge density at the landfall location of the storm. Evaluation of the 3B42/3B42-RT error was performed on a storm by storm basis as well as an overall accumulation of data from all six storms using error metrics including the relative mean difference, relative standard deviation, correlation coefficient, and probability of detection. Significant variability in the performance metrics were observed between the different analyzed storms. Enhanced performance in terms of

  14. A comparison of total precipitation values estimated from measurements and a 1D cloud model

    Directory of Open Access Journals (Sweden)

    Z. Aslan

    Full Text Available The purpose of this study is to establish a relation between observed total precipitation values and estimations from a one-dimensional diagnostic cloud model. Total precipitation values estimated from maximum liquid water content, maximum vertical velocity, cloud top height, and temperature excess are also used to provide an equation for the total precipitation prediction. Data for this study were collected in Istanbul during the autumns of 1987 and 1988. The statistical models are developed with multiple regression technique and then comparatively verified with independent data for 1990. The multiple regression coefficients are in the range of 75% to 80% in the statistical models. Results of the test showed that total precipitation values estimated from the above techniques are in good agreement, with correlation coefficient between 40% and 46% based on test data for 1990.

  15. Weather from 250 Miles Up: Visualizing Precipitation Satellite Data (and Other Weather Applications) Using CesiumJS

    Science.gov (United States)

    Lammers, Matt

    2017-01-01

    Geospatial weather visualization remains predominately a two-dimensional endeavor. Even popular advanced tools like the Nullschool Earth display 2-dimensional fields on a 3-dimensional globe. Yet much of the observational data and model output contains detailed three-dimensional fields. In 2014, NASA and JAXA (Japanese Space Agency) launched the Global Precipitation Measurement (GPM) satellite. Its two instruments, the Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) observe much of the Earth's atmosphere between 65 degrees North Latitude and 65 degrees South Latitude. As part of the analysis and visualization tools developed by the Precipitation Processing System (PPS) Group at NASA Goddard, a series of CesiumJS [Using Cesium Markup Language (CZML), JavaScript (JS) and JavaScript Object Notation (JSON)] -based globe viewers have been developed to improve data acquisition decision making and to enhance scientific investigation of the satellite data. Other demos have also been built to illustrate the capabilities of CesiumJS in presenting atmospheric data, including model forecasts of hurricanes, observed surface radar data, and gridded analyses of global precipitation. This talk will present these websites and the various workflows used to convert binary satellite and model data into a form easily integrated with CesiumJS.

  16. The impact of uncertain precipitation data on insurance loss estimates using a Flood Catastrophe Model

    Directory of Open Access Journals (Sweden)

    C. C. Sampson

    2014-01-01

    Full Text Available Catastrophe risk models used by the insurance industry are likely subject to significant uncertainty, but due to their proprietary nature and strict licensing conditions they are not available for experimentation. In addition, even if such experiments were conducted, these would not be repeatable by other researchers because commercial confidentiality issues prevent the details of proprietary catastrophe model structures from being described in public domain documents. However, such experimentation is urgently required to improve decision making in both insurance and re-insurance markets. In this paper we therefore construct our own catastrophe risk model for flooding in Dublin, Ireland in order to assess the impact of typical precipitation data uncertainty on loss predictions. As we consider only a city region rather than a whole territory and have access to detailed data and computing resources typically unavailable to industry modellers, our model is significantly more detailed than commercial products. The model consists of four components, a stochastic rainfall module, a hydrological and hydraulic flood hazard module, a vulnerability module and a financial loss module. Using these we undertake a series of simulations to test the impact of driving the stochastic event generator with four different rainfall data sets: ground gauge data, gauge corrected rainfall radar, meteorological re-analysis data (ERA-Interim and a satellite rainfall product (CMORPH. Catastrophe models are unusual because they use the upper three components of the modelling chain to generate a large synthetic database of unobserved and severe loss-driving events for which estimated losses are calculated. We find these loss estimates to be highly sensitive to uncertainties propagated from the driving observational datasets, suggesting that the range of uncertainty within catastrophe model structures may be greater than commonly believed.

  17. Estimating Cs-137 fallout inventories in Iceland from precipitation data

    Energy Technology Data Exchange (ETDEWEB)

    Palsson, S.E.; Sigurgeirsson, M.A.; Gudnason, K. [Icelandic Radiation Protection Inst., Reykjavik (Iceland); Arnalds, O. [Agricultural Research Inst., Reykjavik (Iceland); Howard, B.J.; Wright, S.M. [Centre for Ecology and Hydrology Merlewood, Cumbria, (United Kingdom); Palsdottir, I. [Iceland Meteorological Office, Reykjavik (Iceland)

    2002-12-01

    Iceland was identified in the Arctic Monitoring and Assessment Programme (AMAP) as one of the Arctic areas which received the most global fallout from atmospheric nuclear weapons tests, due to relatively high precipitation rates compared with much of the Arctic and sub arctic. Cs-137 in the Icelandic terrestrial ecosystem almost entirely originates from the nuclear weapons tests carried out in the atmosphere until the early sixties. Fallout was greatest in mid nineteen sixties. Additional fallout from the accident at the Chernobyl Nuclear Power Plant was relatively small. The study gave preliminary information on the spatial variation in {sup 137} Cs deposition in Iceland, especially in areas used for agriculture. The objectives of the study were (1) to measure the spatial variation of radiocaesium inventories in soils in Iceland and (2) to compare the results with different approaches to predicting {sup 137} Cs contents in soil. This quantification is a necessary first step in an evaluation of vulnerability to radiocaesium deposition in Iceland. It is anticipated that Icelandic soils could be highly vulnerable to radiocaesium due to their volcanic nature and consequent lack of illitic minerals, as has been suggested by initial chemical studies on the properties of soils in the Nordic countries. (ln)

  18. 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

  19. Comparison of the characteristic energy of precipitating electrons derived from ground-based and DMSP satellite data

    Directory of Open Access Journals (Sweden)

    M. Ashrafi

    2005-01-01

    Full Text Available Energy maps are important for ionosphere-magnetosphere coupling studies, because quantitative determination of field-aligned currents requires knowledge of the conductances and their spatial gradients. By combining imaging riometer absorption and all-sky auroral optical data it is possible to produce high temporal and spatial resolution maps of the Maxwellian characteristic energy of precipitating electrons within a 240240 common field of view. These data have been calibrated by inverting EISCAT electron density profiles into equivalent energy spectra. In this paper energy maps produced by ground-based instruments (optical and riometer are compared with DMSP satellite data during geomagnetic conjunctions. For the period 1995-2002, twelve satellite passes over the ground-based instruments' field of view for the cloud-free conditions have been considered. Four of the satellite conjunctions occurred during moderate geomagnetic, steady-state conditions and without any ion precipitation. In these cases with Maxwellian satellite spectra, there is 71% agreement between the characteristic energies derived from the satellite and the ground-based energy map method.

  20. Estimates of lightning NOx production from GOME satellite observations

    Directory of Open Access Journals (Sweden)

    K. F. Boersma

    2005-01-01

    Full Text Available Tropospheric NO2 column retrievals from the Global Ozone Monitoring Experiment (GOME satellite spectrometer are used to quantify the source strength and 3-D distribution of lightning produced nitrogen oxides (NOx=NO+NO2. A sharp increase of NO2 is observed at convective cloud tops with increasing cloud top height, consistent with a power-law behaviour with power 5±2. Convective production of clouds with the same cloud height are found to produce NO2 with a ratio 1.6/1 for continents compared to oceans. This relation between cloud properties and NO2 is used to construct a 10:30 local time global lightning NO2 production map for 1997. An extensive statistical comparison is conducted to investigate the capability of the TM3 chemistry transport model to reproduce observed patterns of lightning NO2 in time and space. This comparison uses the averaging kernel to relate modelled profiles of NO2 to observed NO2 columns. It exploits a masking scheme to minimise the interference of other NOx sources on the observed total columns. Simulations are performed with two lightning parameterizations, one relating convective preciptation (CP scheme to lightning flash distributions, and the other relating the fifth power of the cloud top height (H5 scheme to lightning distributions. The satellite-retrieved NO2 fields show significant correlations with the simulated lightning contribution to the NO2 concentrations for both parameterizations. Over tropical continents modelled lightning NO2 shows remarkable quantitative agreement with observations. Over the oceans however, the two model lightning parameterizations overestimate the retrieved NO2 attributed to lightning. Possible explanations for these overestimations are discussed. The ratio between satellite-retrieved NO2 and modelled lightning NO2 is used to rescale the original modelled lightning NOx production. Eight estimates of the lightning NOx production in 1997 are obtained from spatial and temporal

  1. Estimates of lightning NOx production from GOME satellite observations

    Directory of Open Access Journals (Sweden)

    H. M. Kelder

    2005-05-01

    Full Text Available Tropospheric NO2 column retrievals from the Global Ozone Monitoring Experiment (GOME satellite spectrometer are used to quantify the source strength and 3D distribution of lightning produced nitrogen oxides (NOx=NO2+NO2. A sharp increase of NO2 is observed at convective cloud tops with increasing cloud top height, consistent with a power-law behaviour with power 5±2. Convective production of clouds with the same cloud height are found to produce NO2 with a ratio 1.6/1 for continents compared to oceans. This relation between cloud properties and NO2 is used to construct a 10:30 local time global lightning NO2 production map for 1997. An extensive statistical comparison is conducted to investigate the capability of the TM3 chemistry transport model to reproduce observed patterns of lightning NO2 in time and space. This comparison uses the averaging kernel to relate modelled profiles of NO2 to observed NO2 columns. It exploits a masking scheme to minimise the interference of other NOx sources on the observed total columns. Simulations are performed with two lightning parametrisations, one relating convective preciptation (CP scheme to lightning flash distributions, and the other relating the fifth power of the cloud top height (H5 scheme to lightning distributions. The satellite-retrieved NO2 fields show significant correlations with the simulated lightning contribution to the NO2 concentrations for both parametrisations. Over tropical continents modelled lightning NO2 shows remarkable quantitative agreement with observations. Over the oceans however, the two model lightning parametrisations overestimate the retrieved NO2 attributed to lightning. Possible explanations for these overestimations are discussed. The ratio between satellite-retrieved NO2 and modelled lightning NO2 is used to rescale the original modelled lightning NOx production. Eight estimates of the lightning NOx production in 1997 are obtained from spatial and temporal correlation

  2. A hybrid Bayesian-SVD based method to detect false alarms in PERSIANN precipitation estimation product using related physical parameters

    Science.gov (United States)

    Ghajarnia, Navid; Arasteh, Peyman D.; Araghinejad, Shahab; Liaghat, Majid A.

    2016-07-01

    Incorrect estimation of rainfall occurrence, so called False Alarm (FA) is one of the major sources of bias error of satellite based precipitation estimation products and may even cause lots of problems during the bias reduction and calibration processes. In this paper, a hybrid statistical method is introduced to detect FA events of PERSIANN dataset over Urmia Lake basin in northwest of Iran. The main FA detection model is based on Bayesian theorem at which four predictor parameters including PERSIANN rainfall estimations, brightness temperature (Tb), precipitable water (PW) and near surface air temperature (Tair) is considered as its input dataset. In order to decrease the dimensions of input dataset by summarizing their most important modes of variability and correlations to the reference dataset, a technique named singular value decomposition (SVD) is used. The application of Bayesian-SVD method in FA detection of Urmia Lake basin resulted in a trade-off between FA detection and Hit events loss. The results show success of proposed method in detecting about 30% of FA events in return for loss of about 12% of Hit events while better capability of this method in cold seasons is observed.

  3. 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.

  4. Satellite Driven Estimation of Primary Productivity of Agroecosystems in India

    Science.gov (United States)

    Patel, N. R.; Dadhwal, V. K.; Agrawal, S.; Saha, S. K.

    2011-08-01

    Earth observation driven ecosystem modeling have played a major role in estimation of carbon budget components such as gross primary productivity (GPP) and net primary production (NPP) over terrestrial ecosystems, including agriculture. The present study therefore evaluate satellite-driven vegetation photosynthesis (VPM) model for GPP estimation over agro-ecosystems in India by using time series of the Normalized Difference Vegetation Index (NDVI) from SPOT-VEGETATION, cloud cover observation from MODIS, coarse-grid C3/C4 crop fraction and decadal grided databases of maximum and minimum temperatures. Parameterization of VPM parameters e.g. maximum light use efficiency (ɛ*) and Tscalar was done based on eddy-covariance measurements and literature survey. Incorporation of C3/C4 crop fraction is a modification to commonly used constant maximum LUE. Modeling results from VPM captured very well the geographical pattern of GPP and NPP over cropland in India. Well managed agro-ecosystems in Trans-Gangetic and upper Indo-Gangetic plains had the highest magnitude of GPP with peak GPP during kharif occurs in sugarcane-wheat system (western UP) and it occurs in rice-wheat system (Punjab) during Rabi season. Overall, croplands in these plains had more annual GPP (> 1000 g C m-2) and NPP (> 600 g C m-2) due to input-intensive cultivation. Desertic tracts of western Rajasthan showed the least GPP and NPP values. Country-level contribution of croplands to national GPP and NPP amounts to1.34 Pg C year-1 and 0.859 Pg C year-1, respectively. Modeled estimates of cropland NPP agrees well with ground-based estimates for north-western India (R2 = 0.63 and RMSE = 108 g C m-2). Future research will focus on evaluating the VPM model with medium resolution sensors such as AWiFS and MODIS for rice-wheat system and validating with eddy-covariance measurements.

  5. SATELLITE DRIVEN ESTIMATION OF PRIMARY PRODUCTIVITY OF AGROECOSYSTEMS IN INDIA

    Directory of Open Access Journals (Sweden)

    N. R. Patel

    2012-08-01

    Full Text Available Earth observation driven ecosystem modeling have played a major role in estimation of carbon budget components such as gross primary productivity (GPP and net primary production (NPP over terrestrial ecosystems, including agriculture. The present study therefore evaluate satellite-driven vegetation photosynthesis (VPM model for GPP estimation over agro-ecosystems in India by using time series of the Normalized Difference Vegetation Index (NDVI from SPOT-VEGETATION, cloud cover observation from MODIS, coarse-grid C3/C4 crop fraction and decadal grided databases of maximum and minimum temperatures. Parameterization of VPM parameters e.g. maximum light use efficiency (ε* and Tscalar was done based on eddy-covariance measurements and literature survey. Incorporation of C3/C4 crop fraction is a modification to commonly used constant maximum LUE. Modeling results from VPM captured very well the geographical pattern of GPP and NPP over cropland in India. Well managed agro-ecosystems in Trans-Gangetic and upper Indo-Gangetic plains had the highest magnitude of GPP with peak GPP during kharif occurs in sugarcane-wheat system (western UP and it occurs in rice-wheat system (Punjab during Rabi season. Overall, croplands in these plains had more annual GPP (> 1000 g C m-2 and NPP (> 600 g C m-2 due to input-intensive cultivation. Desertic tracts of western Rajasthan showed the least GPP and NPP values. Country-level contribution of croplands to national GPP and NPP amounts to1.34 Pg C year-1 and 0.859 Pg C year-1, respectively. Modeled estimates of cropland NPP agrees well with ground-based estimates for north-western India (R2 = 0.63 and RMSE = 108 g C m-2. Future research will focus on evaluating the VPM model with medium resolution sensors such as AWiFS and MODIS for rice-wheat system and validating with eddy-covariance measurements.

  6. 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

    Spaceborne synthetic aperture radars (SARs) operating at L-band and above are nowadays a well-established tool for Earth remote sensing; among the numerous civil applications we can indicate flood areas detection and monitoring, earthquakes analysis, digital elevation model production, land use monitoring and classification. Appealing characteristics of this kind of instruments is the high spatial resolution ensured in almost all-weather conditions and with a reasonable duty cycle and coverage. This result has achieved by the by the most recent generation of SAR missions, which moreover allow polarimetric observation of the target. Nevertheless, atmospheric clouds, in particular the precipitating ones, can significantly affect the signal backscattered from the ground surface (e.g. Ferrazzoli and Schiavon, 1997), on both amplitude and phase, with effects increasing with the operating frequency. In this respect, proofs are given by several recent works (e.g. Marzano et al., 2010, Baldini et al., 2014) using X-Band SAR data by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions. On the other hand, this sensitivity open interesting perspectives towards the SAR observation, and eventually quantification, of precipitations. In this respect, a proposal approach for X-SARs precipitation maps production and cloud masking arise from our work. Cloud masking allows detection of precipitation compromised areas. Respect precipitation maps, satellite X-SARs offer the unique possibility to ingest within flood forecasting model precipitation data at the catchment scale. This aspect is particularly innovative, even if work has been done the late years, and some aspects need to still address. Our developed processing framework allows, within the cloud masking stage, distinguishing flooded areas, precipitating clouds together with permanent water bodies, all appearing dark in the SAR image. The procedure is mainly based on image segmentation techniques and fuzzy logic (e.g. Pulvirenti et

  7. Changing precipitation extremes in a warming climate: A basis for design flood estimation

    Science.gov (United States)

    Wasko, Conrad; Sharma, Ashish

    2016-04-01

    The potential for increasing intensity of future rainfall events has significant implications for flooding and the design of infrastructure. However the questions of how precipitation will change in the future, how important these changes are to flooding, and how engineers incorporate these changes into hydrologic design remain as open questions. In the absence of reliable point based estimates of how precipitation will change, many studies investigate the historical relationship between rainfall intensity and temperature as a proxy for what may happen in a warmer climate. Much of the research to date has focussed on changing precipitation intensity, however, temporal and spatial patterns of precipitation are just as important. Here we link higher temperatures to changes in temporal and spatial patterns of extreme precipitation events. We show, using observed high quality precipitation records from Australia covering all major climatic zones, that storms are intensifying in both time and space resulting in a greater potential for flooding especially in urban locales around the world. Given that precipitation and antecedent conditions are changing, and, the impacts to flooding are significant, methods of incorporating these changes in catchment modelling are required. Continuous simulation offers a natural flexibility to incorporate the many correlated changes in precipitation that may occur in a future climate. An argument for such a framework using existing continuous simulation alternatives is articulated in concluding this presentation.

  8. Effects of systematic sampling on satellite estimates of deforestation rates

    Energy Technology Data Exchange (ETDEWEB)

    Steininger, M K; Godoy, F; Harper, G, E-mail: msteininger@conservation.or [Center for Applied Biodiversity Science-Conservation International, 2011 Crystal Drive Suite 500, Arlington, VA 22202 (United States)

    2009-09-15

    Options for satellite monitoring of deforestation rates over large areas include the use of sampling. Sampling may reduce the cost of monitoring but is also a source of error in estimates of areas and rates. A common sampling approach is systematic sampling, in which sample units of a constant size are distributed in some regular manner, such as a grid. The proposed approach for the 2010 Forest Resources Assessment (FRA) of the UN Food and Agriculture Organization (FAO) is a systematic sample of 10 km wide squares at every 1 deg. intersection of latitude and longitude. We assessed the outcome of this and other systematic samples for estimating deforestation at national, sub-national and continental levels. The study is based on digital data on deforestation patterns for the five Amazonian countries outside Brazil plus the Brazilian Amazon. We tested these schemes by varying sample-unit size and frequency. We calculated two estimates of sampling error. First we calculated the standard errors, based on the size, variance and covariance of the samples, and from this calculated the 95% confidence intervals (CI). Second, we calculated the actual errors, based on the difference between the sample-based estimates and the estimates from the full-coverage maps. At the continental level, the 1 deg., 10 km scheme had a CI of 21% and an actual error of 8%. At the national level, this scheme had CIs of 126% for Ecuador and up to 67% for other countries. At this level, increasing sampling density to every 0.25 deg. produced a CI of 32% for Ecuador and CIs of up to 25% for other countries, with only Brazil having a CI of less than 10%. Actual errors were within the limits of the CIs in all but two of the 56 cases. Actual errors were half or less of the CIs in all but eight of these cases. These results indicate that the FRA 2010 should have CIs of smaller than or close to 10% at the continental level. However, systematic sampling at the national level yields large CIs unless the

  9. Missing North Atlantic cyclonic precipitation in ECMWF numerical weather prediction and ERA-40 data detected through the satellite climatology HOAPS II

    Energy Technology Data Exchange (ETDEWEB)

    Klepp, C.P.; Bakan, S.; Grassl, H. [Max-Planck Inst. fuer Meteorologie and Meteorologisches Inst., Univ. Hamburg (Germany)

    2005-12-01

    Intense precipitation associated with wintertime North Atlantic cyclones occurs not only in connection with frontal zones but also, and often mainly, embedded in strong cold air outbreaks to the west of mature cold fronts. Coherent structures of cloud clusters organized in mesoscale postfrontal low-pressure systems are frequently found in satellite data. Such postfrontal lows (PFL) can develop into severe weather events within few hours and can even reach Europe causing intense convective rainfall and gale force winds. Despite predicting the major storm systems numerical weather prediction (NWP) additionally needs to account for PFLs due to their frequent occurrence connected with high impact weather. But while the major cyclone systems are mostly well predicted, the forecast of PFLs remains poor. Using North Atlantic weather observations from the 1997 fronts and Atlantic storm track experiment (FASTEX) along with the standard voluntary observing ship (VOS) data led to a high quality validation data set for this usually data sparse region. For individual case studies of FASTEX cyclones with mesoscale PFLs investigations were carried out using the well calibrated precipitation estimates from HOAPS (Hamburg Ocean Atmosphere Parameters and fluxes from satellite data) compared to the NWP model output of the ECMWF (European Centre for medium-range weather forecasts). Preceding studies showed that the HOAPS precipitation structure and intensities are in good agreement with the VOS observations for all observed precipitation types within the cyclones, including PFLs. To assure that the results found in the 1997 data are still valid in the more recent ECMWF model system, a PFL rainfall comparison is carried out using HOAPS and ERA-40 (ECMWF Re-Analysis) data for the winter of 2001 and 2002. The results indicate that the ECMWF model is mostly well reproducing precipitation structures and intensities associated with frontal systems as observed in the VOS and HOAPS data

  10. Evaluation of GPM-based Multi-satellite IMERG Precipitation Products Over the Lower Colorado River Basin, Texas

    Science.gov (United States)

    Omranian, S. E.; Sharif, H. O.

    2016-12-01

    This study evaluates the Global Precipitation Measurement (GPM) satellite products by analyzing extreme rainfall events over the Lower Colorado River Basin, Texas that resulted in unprecedented flash floods in May 2015. Records of a dense rain gauge network (241 stations) are compared with Integrated Multi-satellite Retrievals for GPM (IMERG) products. The spatial resolution of the GPM satellite product is 0.1º x 0.1º and the temporal resolution is 30 minutes. Reference gauge-based observations are distributed through the basin with total area of over 5,000 square kilometers at 15-minute time intervals. A preliminary assessment of GPM-based IMERG precipitation products shows reasonable correlation, especially when for periods of high amounts of rainfall. the results indicate that GPM satellite products can potentially be employed in hydrologic modeling, especially for large events. Moreover, since the IMERG products have semi-global coverage, it can be extremely useful in hydrological modeling and analysis studies over ungauged or poorly gauged regions.

  11. Flood modelling with global precipitation measurement (GPM) satellite rainfall data: a case study of Dehradun, Uttarakhand, India

    Science.gov (United States)

    Sai Krishna, V. V.; Dikshit, Anil Kumar; Pandey, Kamal

    2016-05-01

    Urban expansion, water bodies and climate change are inextricably linked with each other. The macro and micro level climate changes are leading to extreme precipitation events which have severe consequences on flooding in urban areas. Flood simulations shall be helpful in demarcation of flooded areas and effective flood planning and preparedness. The temporal availability of satellite rainfall data at varying spatial scale of 0.10 to 0.50 is helpful in near real time flood simulations. The present research aims at analysing stream flow and runoff to monitor flood condition using satellite rainfall data in a hydrologic model. The satellite rainfall data used in the research was NASA's Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), which is available at 30 minutes temporal resolution. Landsat data was used for mapping the water bodies in the study area. Land use land cover (LULC) data was prepared using Landsat 8 data with maximum likelihood technique that was provided as an input to the HEC-HMS hydrological model. The research was applied to one of the urbanized cities of India, viz. Dehradun, which is the capital of Uttarakhand State. The research helped in identifying the flood vulnerability at the basin level on the basis of the runoff and various socio economic parameters using multi criteria analysis.

  12. Precipitation effects on the selection of suitable non-variant targets intended for atmospheric correction of satellite remotely sensed imagery

    Science.gov (United States)

    Themistocleous, Kyriacos; Hadjimitsis, Diofantos G.; Retalis, Adrianos; Chrysoulakis, Nektarios; Michaelides, Silas

    2013-09-01

    One of the most well-established atmospheric correction methods of satellite imagery is the use of the empirical line method using non-variant targets. Non-variant targets serve as pseudo-invariant targets since their reflectance values are stable across time. A recent adaptation of the empirical line method incorporates the use of ground reflectance measurements of selected non-variant targets. Most of the users are not aware of the existing conditions of the pseudo-invariant targets; i.e., whether they are dry or wet. Any omission of such effects may cause erroneous results; therefore, remote sensing users must be aware of such effects. This study assessed the effects of precipitation on five types of commonly located surfaces, including asphalt, concrete and sand, intended as pseudo-invariant targets for atmospheric correction. Spectroradiometric measurements were taken in wet and dry conditions to obtain the spectral signatures of the targets, from January 2010 to May 2011 (46 campaigns). An atmospheric correction of eleven Landsat TM/ETM + satellite images using the empirical line method was conducted. To identify the effects of precipitation, a comparison was conducted of the atmospheric path radiance component for wet and dry conditions. It was found that precipitation conditions such as rainfall affected the reflectance values of the surfaces, especially sand. Therefore, precipitation conditions need to be considered when using non-variant targets in atmospheric correction methods.

  13. 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

  14. Estimation of Satellite PCO Offsets for BeiDou based on MGEX Net Solution

    Science.gov (United States)

    Yize, Zhang; Junping, Chen; Bin, Wu; Jiexian, Wang

    2015-04-01

    BeiDou Satellite Navigation System currently has a total 14 satellites including GEO/IGSO/MEO satellites and providing a regional PNT service. Due to a lack of publicly available antenna phase center offsets (PCO) for the BeiDou satellites, conventional values of (+0.6 m, 0.0 m, +1.1 m) are recommended for orbit and clock determination of the GEO/IGSO/MEO satellites, which needs to be further estimation and refinement. In this paper, we propose a multi-GNSS network solution for the estimation of BeiDou satellite PCO. More than 35 ground stations of International GNSS MGEX tracking network are used to determine the BeiDou satellite PCO. In this strategy, the GPS and BeiDou satellite orbits and clocks are derived from IGS final products, and GPS satellite PCO and PCV are fixed according to igs08.atx. The BeiDou satellites PCO are estimated together with the station clock, troposphere delay and LC combination ambiguity parameter. Result shows that the RMS of phase residuals for all stations is 1.8cm and is 1.6m for code residual, respectively. The estimated PCO is different for each satellite. Appling the new PCO for precise point positioning, we found that the positioning error improves from 6cm to 2cm in height.

  15. Estimation of Satellite Orientation from Space Surveillance Imagery Measured with an Adaptive Optics Telescope

    Science.gov (United States)

    1996-12-01

    SATELLITE ORIENTATION FROM SPACE SURVEILLANCE IMAGERY MEASURED WITH AN ADAPTIVE OPTICS TELESCOPE THESIS Gregory E. Wood Lieutenant, USAF AFIT/GSO/ENP...the official policy or position of the Department of Defense or the U. S. Government. AFIT/GSO/ENP/96D-02 ESTIMATION OF SATELLITE ORIENTATION FROM...surveillance operations. xii ESTIMATION OF SATELLITE ORIENTATION FROM SPACE SURVEILLANCE IMAGERY MEASURED WITH AN ADAPTIVE OPTICS TELESCOPE

  16. The scavenging of air pollutants by precipitation, and its estimation with the aid of weather radar

    Science.gov (United States)

    Jylha, Kirsti Tellervo

    2000-09-01

    Precipitation cleanses the air by capturing airborne pollutants and depositing them onto the ground. The efficiency of this process may be expressed by the fractional depletion rate of pollutant concentrations in the air, designated as the scavenging coefficient. It depends on the size distribution of the raindrops and snow crystals and is thereby related to quantities estimated by weather radar, namely, the radar reflectivity factor and the precipitation rate. On the other hand, there are no universal relationships between the scavenging coefficient and these two quantities; the relationships vary depending on the properties of the precipitation and pollutants. In the present thesis, a few estimates for them were derived theoretically and empirically, using in the latter case observations made in Finland either after the Chernobyl nuclear accident or during a wintertime case study near a coal-fired power plant. The greatest advantage in the use of weather radar in assessing precipitation scavenging arises from the fact that radar estimates the spatial distributions of precipitation in real time with a good spatial and temporal resolution. Radar software usually used to create displays of the precipitation rate can easily be modified to show distributions of the scavenging coefficient. Such images can provide valuable information about the areas where a substantial portion of the pollutants is deposited onto the ground or, alternatively, remains airborne. Based on the movement of the precipitation areas, it is also possible to make short-term forecasts of those areas most likely to be exposed to wet deposition. A network of radars may hence form an important part of a real-time monitoring and warning system that can be immediately effective in the event of an accidental releases of hazardous materials into the air.

  17. Application of satellite precipitation data to analyse and model arbovirus activity in the tropics

    Directory of Open Access Journals (Sweden)

    Corner Robert J

    2011-01-01

    Full Text Available Abstract Background Murray Valley encephalitis virus (MVEV is a mosquito-borne Flavivirus (Flaviviridae: Flavivirus which is closely related to Japanese encephalitis virus, West Nile virus and St. Louis encephalitis virus. MVEV is enzootic in northern Australia and Papua New Guinea and epizootic in other parts of Australia. Activity of MVEV in Western Australia (WA is monitored by detection of seroconversions in flocks of sentinel chickens at selected sample sites throughout WA. Rainfall is a major environmental factor influencing MVEV activity. Utilising data on rainfall and seroconversions, statistical relationships between MVEV occurrence and rainfall can be determined. These relationships can be used to predict MVEV activity which, in turn, provides the general public with important information about disease transmission risk. Since ground measurements of rainfall are sparse and irregularly distributed, especially in north WA where rainfall is spatially and temporally highly variable, alternative data sources such as remote sensing (RS data represent an attractive alternative to ground measurements. However, a number of competing alternatives are available and careful evaluation is essential to determine the most appropriate product for a given problem. Results The Tropical Rainfall Measurement Mission (TRMM Multi-satellite Precipitation Analysis (TMPA 3B42 product was chosen from a range of RS rainfall products to develop rainfall-based predictor variables and build logistic regression models for the prediction of MVEV activity in the Kimberley and Pilbara regions of WA. Two models employing monthly time-lagged rainfall variables showed the strongest discriminatory ability of 0.74 and 0.80 as measured by the Receiver Operating Characteristics area under the curve (ROC AUC. Conclusions TMPA data provide a state-of-the-art data source for the development of rainfall-based predictive models for Flavivirus activity in tropical WA. Compared to

  18. Predictive Uncertainty Estimation on a Precipitation and Temperature Reanalysis Ensemble for Shigar Basin, Central Karakoram

    Directory of Open Access Journals (Sweden)

    Paolo Reggiani

    2016-06-01

    Full Text Available The Upper Indus Basin (UIB and the Karakoram Range are the subject of ongoing hydro-glaciological studies to investigate possible glacier mass balance shifts due to climatic change. Because of the high altitude and remote location, the Karakoram Range is difficult to access and, therefore, remains scarcely monitored. In situ precipitation and temperature measurements are only available at valley locations. High-altitude observations exist only for very limited periods. Gridded precipitation and temperature data generated from the spatial interpolation of in situ observations are unreliable for this region because of the extreme topography. Besides satellite measurements, which offer spatial coverage, but underestimate precipitation in this area, atmospheric reanalyses remain one of the few alternatives. Here, we apply a proven approach to quantify the uncertainty associated with an ensemble of monthly precipitation and temperature reanalysis data for 1979–2009 in Shigar Basin, Central Karakoram. A Model-Conditional Processor (MCP of uncertainty is calibrated on precipitation and temperature in situ data measured in the proximity of the study region. An ensemble of independent reanalyses is processed to determine the predictive uncertainty of monthly observations. As to be expected, the informative gain achieved by post-processing temperature reanalyses is considerable, whereas significantly less gain is achieved for precipitation post-processing. The proposed approach indicates a systematic assessment procedure for predictive uncertainty through probabilistic weighting of multiple re-forecasts, which are bias-corrected on ground observations. The approach also supports an educated reconstruction of gap-filling for missing in situ observations.

  19. Estimating the vertical structure of intense Mediterranean precipitation using two X-band weather radar systems

    NARCIS (Netherlands)

    Berne, A.D.; Delrieu, G.; Andrieu, H.

    2005-01-01

    The present study aims at a preliminary approach of multiradar compositing applied to the estimation of the vertical structure of precipitation¿an important issue for radar rainfall measurement and prediction. During the HYDROMET Integrated Radar Experiment (HIRE¿98), the vertical profile of

  20. Estimation of regional intensity-duration-frequency curves for extreme precipitation

    DEFF Research Database (Denmark)

    Madsen, Henrik; Mikkelsen, Peter Steen; Rosbjerg, Dan;

    1998-01-01

    Regional estimation of extreme precipitation from a high resolution rain gauge network in Denmark is considered. The applied extreme value model is based on the partial duration series (PDS) approach in which all events above a certain threshold level are modelled. For a preliminary assessment...

  1. Recent trends of the tropical hydrological cycle inferred from Global Precipitation Climatology Project and International Satellite Cloud Climatology Project data

    Science.gov (United States)

    Zhou, Y. P.; Xu, Kuan-Man; Sud, Y. C.; Betts, A. K.

    2011-05-01

    Scores of modeling studies have shown that increasing greenhouse gases in the atmosphere impact the global hydrologic cycle; however, disagreements on regional scales are large, and thus the simulated trends of such impacts, even for regions as large as the tropics, remain uncertain. The present investigation attempts to examine such trends in the observations using satellite data products comprising Global Precipitation Climatology Project precipitation and International Satellite Cloud Climatology Project cloud and radiation. Specifically, evolving trends of the tropical hydrological cycle over the last 20-30 years were identified and analyzed. The results show (1) intensification of tropical precipitation in the rising regions of the Walker and Hadley circulations and weakening over the sinking regions of the associated overturning circulation; (2) poleward shift of the subtropical dry zones (up to 2° decade-1 in June-July-August (JJA) in the Northern Hemisphere and 0.3-0.7° decade-1 in June-July-August and September-October-November in the Southern Hemisphere) consistent with an overall broadening of the Hadley circulation; and (3) significant poleward migration (0.9-1.7° decade-1) of cloud boundaries of Hadley cell and plausible narrowing of the high cloudiness in the Intertropical Convergence Zone region in some seasons. These results support findings of some of the previous studies that showed strengthening of the tropical hydrological cycle and expansion of the Hadley cell that are potentially related to the recent global warming trends.

  2. Energy distribution of precipitating electrons estimated from optical and cosmic noise absorption measurements

    Directory of Open Access Journals (Sweden)

    H. Mori

    2004-04-01

    Full Text Available This study is a statistical analysis on energy distribution of precipitating electrons, based on CNA (cosmic noise absorption data obtained from the 256-element imaging riometer in Poker Flat, Alaska (65.11° N, 147.42° W, and optical data measured with an MSP (Meridian Scanning Photometer over 79 days during the winter periods from 1996 to 1998. On the assumption that energy distributions of precipitating electrons represent Maxwellian distributions, CNA is estimated based on the observation data of auroral 427.8-nm and 630.0-nm emissions, as well as the average atmospheric model, and compared with the actual observation data. Although the observation data have a broad distribution, they show systematically larger CNA than the model estimate. CNA determination using kappa or double Maxwellian distributions, instead of Maxwellian distributions, better explains the distribution of observed CNA data. Kappa distributions represent a typical energy distribution of electrons in the plasma sheet of the magnetosphere, the source region of precipitating electrons. Pure kappas are more likely during quiet times – and quiet times are more likely than active times. This result suggests that the energy distribution of precipitating electrons reflects the energy distribution of electrons in the plasma sheet.

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

  3. Sea ice-atmospheric interaction: Application of multispectral satellite data in polar surface energy flux estimates

    Science.gov (United States)

    Steffen, Konrad; Key, J.; Maslanik, J.; Schweiger, A.

    1993-01-01

    This is the third annual report on: Sea Ice-Atmosphere Interaction - Application of Multispectral Satellite Data in Polar Surface Energy Flux Estimates. The main emphasis during the past year was on: radiative flux estimates from satellite data; intercomparison of satellite and ground-based cloud amounts; radiative cloud forcing; calibration of the Advanced Very High Resolution Radiometer (AVHRR) visible channels and comparison of two satellite derived albedo data sets; and on flux modeling for leads. Major topics covered are arctic clouds and radiation; snow and ice albedo, and leads and modeling.

  4. Estimating water storage changes and sink terms in Volta Basin from satellite missions

    Directory of Open Access Journals (Sweden)

    Vagner G. FERREIRA

    2014-01-01

    Full Text Available The insufficiency of distributed in situ hydrological measurements is a major challenge for hydrological studies in many regions of the world. Satellite missions such as the Gravity Recovery and Climate Experiment (GRACE and the Tropical Rainfall Measurement Mission (TRMM can be used to improve our understanding of water resources beyond surface water in poorly gauged basins. In this study we combined GRACE and TRMM to investigate monthly estimates of evaporation plus runoff (sink terms using the water balance equation for the period from January 2005 to December 2010 within the Volta Basin. These estimates have been validated by comparison with time series of sink terms (evaporation plus surface and subsurface runoff from the Global Land Data Assimilation System (GLDAS. The results, for the period under consideration, show strong agreement between both time series, with a root mean square error (RMSE of 20.2 mm/month (0.67 mm/d and a correlation coefficient of 0.85. This illustrates the ability of GRACE to predict hydrological quantities, e.g. evaporation, in the Volta Basin. The water storage change data from GRACE and precipitation data from TRMM all show qualitative agreement, with evidence of basin saturation at approximately 73 mm in the equivalent water column at the annual and semi-annual time scales.

  5. 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

  6. Characterizing Seasonal Drought, Water Supply Pattern and Their Impact on Vegetation Growth Using Satellite Soil Moisture Data, GRACE Water Storage and Precipitation Observations

    Science.gov (United States)

    A, G.; Velicogna, I.; Kimball, J. S.; Du, J.; Kim, Y.; Njoku, E. G.; Colliander, A.

    2016-12-01

    We combine soil moisture (SM) data from AMSR-E, AMSR-2 and SMAP, terrestrial water storage (TWS) changes from GRACE and precipitation measurements from GPCP to delineate and characterize drought and water supply pattern and its impact on vegetation growth. GRACE TWS provides spatially continuous observations of total terrestrial water storage changes and regional drought extent, persistence and severity, while satellite derived soil moisture estimates provide enhanced delineation of plant-available soil moisture. Together these data provide complementary metrics quantifying available plant water supply and have important implications for water resource management. We use these data to investigate the supply changes from different water components in relation to satellite based vegetation productivity metrics from MODIS, before, during and following the major drought events observed in the continental US during the past 13 years. We observe consistent trends and significant correlations between monthly time series of TWS, SM, and vegetation productivity. In Texas and surrounding semi-arid areas, we find that the spatial pattern of the vegetation-moisture relation follows the gradient in mean annual precipitation. In Texas, GRACE TWS and surface SM show strong coupling and similar characteristic time scale in relatively normal years, while during the 2011 onward hydrological drought, GRACE TWS manifests a longer time scale than that of surface SM, implying stronger drought persistence in deeper water storage. In the Missouri watershed, we find a spatially varying vegetation-moisture relationship where in the drier northwestern portion of the basin, the inter-annual variability in summer vegetation productivity is closely associated with changes in carry-on GRACE TWS from spring, whereas in the moist southeastern portion of the basin, summer precipitation is the dominant controlling factor on vegetation growth.

  7. Femto-satellite Swarm State and Density Estimation Project

    Data.gov (United States)

    National Aeronautics and Space Administration — NASA is planning future missions involving fleets of small satellites in LEO and GEO that can exhibit autonomous collective behavior. Such a "swarm of...

  8. Gravity Anomalies and Estimated Topography Derived from Satellite Altimetry

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — In many areas of the global ocean, the depth of the seafloor is not well known because survey lines by ships are hundreds of kilometers apart. Satellites carrying...

  9. 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

  10. Geostationary Satellite Observation of Precipitable Water Vapor Using an Empirical Orthogonal Function (EOF based Reconstruction Technique over Eastern China

    Directory of Open Access Journals (Sweden)

    Man Sing Wong

    2015-05-01

    Full Text Available Water vapor, as one of the most important greenhouse gases, is crucial for both climate and atmospheric studies. Considering the high spatial and temporal variations of water vapor, a timely and accurate retrieval of precipitable water vapor (PWV is urgently needed, but has long been constrained by data availability. Our study derived the vertically integrated precipitable water vapor over eastern China using Multi-functional Transport Satellite (MTSAT data, which is in geostationary orbit with high temporal resolution. The missing pixels caused by cloud contamination were reconstructed using an Empirical Orthogonal Function (EOF decomposition method over both spatial and temporal dimensions. GPS meteorology data were used to validate the retrieval and the reconstructed results. The diurnal variation of PWV over eastern China was analyzed using harmonic analysis, which indicates that the reconstructed PWV data can depict the diurnal cycle of PWV caused by evapotranspiration and local thermal circulation.

  11. Satellite cloud and precipitation property retrievals for climate monitoring and hydrological applications

    NARCIS (Netherlands)

    Wolters, E.L.A.

    2012-01-01

    This thesis presents the retrieval, evaluation, and application of cloud physical property datasets (cloud phase, cloud particle effective radius, and precipitation occurrence and intensity) obtained from Spinning Enhanced Visible and Infrared Imager (SEVIRI) reflectance measurements using the Cloud

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

    Energy Technology Data Exchange (ETDEWEB)

    Mehran, Ali [Univ. of California, Irvine, CA (United States). Dept. of Civil and Environmental Engineering; AghaKouchak, Amir [Univ. of California, Irvine, CA (United States). Dept. of Civil and Environmental Engineering; Phillips, Thomas J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2014-02-25

    Numerous studies have emphasized that climate simulations are subject to various biases and uncertainties. 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 data 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 sub-continental regions (e.g., northern Eurasia, eastern Russia, central Australia) is problematical. Overall, the VHI of the multi-model ensemble mean and median also are superior to that of the individual CMIP5 models. However, at high quantiles of reference data (e.g., the 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, inter-model variations in bias over Australia and Amazonia are considerable. The Quantile Bias (QB) analyses indicate that CMIP5 simulations are even more biased at high quantiles of precipitation. Lastly, we found that a simple mean-field bias removal improves the overall B and VHI values, but does not make a significant improvement in these model performance metrics at high quantiles of precipitation.

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

    Science.gov (United States)

    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.

  14. Analyzing Spatial and Temporal Variation in Precipitation Estimates in a Coupled Model

    Science.gov (United States)

    Tomkins, C. D.; Springer, E. P.; Costigan, K. R.

    2001-12-01

    Integrated modeling efforts at the Los Alamos National Laboratory aim to simulate the hydrologic cycle and study the impacts of climate variability and land use changes on water resources and ecosystem function at the regional scale. The integrated model couples three existing models independently responsible for addressing the atmospheric, land surface, and ground water components: the Regional Atmospheric Model System (RAMS), the Los Alamos Distributed Hydrologic System (LADHS), and the Finite Element and Heat Mass (FEHM). The upper Rio Grande Basin, extending 92,000 km2 over northern New Mexico and southern Colorado, serves as the test site for this model. RAMS uses nested grids to simulate meteorological variables, with the smallest grid over the Rio Grande having 5-km horizontal grid spacing. As LADHS grid spacing is 100 m, a downscaling approach is needed to estimate meteorological variables from the 5km RAMS grid for input into LADHS. This study presents daily and cumulative precipitation predictions, in the month of October for water year 1993, and an approach to compare LADHS downscaled precipitation to RAMS-simulated precipitation. The downscaling algorithm is based on kriging, using topography as a covariate to distribute the precipitation and thereby incorporating the topographical resolution achieved at the 100m-grid resolution in LADHS. The results of the downscaling are analyzed in terms of the level of variance introduced into the model, mean simulated precipitation, and the correlation between the LADHS and RAMS estimates. Previous work presented a comparison of RAMS-simulated and observed precipitation recorded at COOP and SNOTEL sites. The effects of downscaling the RAMS precipitation were evaluated using Spearman and linear correlations and by examining the variance of both populations. The study focuses on determining how the downscaling changes the distribution of precipitation compared to the RAMS estimates. Spearman correlations computed for

  15. Comparison of the TRMM Precipitation Radar rainfall estimation with ground-based disdrometer and radar measurements in South Greece

    Science.gov (United States)

    Ioannidou, Melina P.; Kalogiros, John A.; Stavrakis, Adrian K.

    2016-11-01

    The performance of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) rainfall estimation algorithm is assessed, locally, in Crete island, south Greece, using data from a 2D-video disdrometer and a ground-based, X-band, polarimetric radar. A three-parameter, normalized Gamma drop size distribution is fitted to the disdrometer rain spectra; the latter are classified in stratiform and convective rain types characterized by different relations between distribution parameters. The method of moments estimates more accurately the distribution parameters than the best fit technique, which exhibits better agreement with and is more biased by the observed droplet distribution at large diameter values. Power laws between the radar reflectivity factor (Z) and the rainfall rate (R) are derived from the disdrometer data. A significant diversity of the prefactor and the exponent of the estimated power laws is observed, depending on the scattering model and the regression technique. The Z-R relationships derived from the disdrometer data are compared to those obtained from TRMM-PR data. Generally, the power laws estimated from the two datasets are different. Specifically, the greater prefactor found for the disdrometer data suggests an overestimation of rainfall rate by the TRMM-PR algorithm for light and moderate stratiform rain, which was the main rain type in the disdrometer dataset. Finally, contemporary data from the TRMM-PR and a ground-based, X-band, polarimetric radar are analyzed. Comparison of the corresponding surface rain rates for a rain event with convective characteristics indicates a large variability of R in a single TRMM-PR footprint, which typically comprises several hundreds of radar pixels. Thus, the coarse spatial resolution of TRMM-PR may lead to miss of significant high local peaks of convective rain. Also, it was found that the high temporal variability of convective rain may introduce significant errors in the estimation of bias of

  16. Contribution of MODIS Derived Snow Cover Satellite Data into Artificial Neural Network for Streamflow Estimation

    Science.gov (United States)

    Uysal, Gokcen; Arda Sorman, Ali; Sensoy, Aynur

    2014-05-01

    Contribution of snowmelt and correspondingly snow observations are highly important in mountainous basins for modelers who deal with conceptual, physical or soft computing models in terms of effective water resources management. Long term archived continuous data are needed for appropriate training and testing of data driven approaches like artificial neural networks (ANN). Data is scarce at the upper elevations due to the difficulty of installing sufficient automated SNOTEL stations; thus in literatures many attempts are made on the rainfall dominated basins for streamflow estimation studies. On the other hand, optical satellites can easily detect snow because of its high reflectance property. MODIS (Moderate Resolution Imaging Spectroradiometer) satellite that has two platforms (Terra and Aqua) provides daily and 8-daily snow images for different time periods since 2000, therefore snow cover data (SCA) may be useful as an input layer for ANN applications. In this study, a multi-layer perceptron (MLP) model is trained and tested with precipitation, temperature, radiation, previous day discharges as well as MODIS daily SCA data. The weights and biases are optimized with fastest and robust Levenberg-Marquardt backpropagation algorithm. MODIS snow cover images are removed from cloud coverage using certain filtering techniques. The Upper Euphrates River Basin in eastern part of Turkey (10 250 km2) is selected as the application area since it is fed by snowmelt approximately 2/3 of total annual volume during spring and early summer. Several input models and ANN structures are investigated to see the effect of the contributions using 10 years of data (2001-2010) for training and validation. The accuracy of the streamflow estimations is checked with statistical criteria (coefficient of determination, Nash-Sutcliffe model efficiency, root mean square error, mean absolute error) and the results seem to improve when SCA data is introduced. Furthermore, a forecast study is

  17. The GNSS polarimetric radio-occultation technique to sense precipitation events: a new concept to be tested aboard PAZ Low Earth Satellite

    Science.gov (United States)

    Tomás, Sergio; Oliveras, Santi; Cardellach, Estel; Rius, Antonio

    2013-04-01

    The Radio Occultation and Heavy Precipitation (ROHP) experiment, to be conducted aboard the Spanish PAZ satellite, consists of a radio occultation (RO) mission provided with dual-polarization capabilities. The research with polarimetric RO data has the goal of assessing the capabilities and limitations of this technique to infer profiles of heavy precipitation. The technique aims to provide vertical profiles of precipitation simultaneously to the vertical profiles of thermodynamic parameters (standard RO products) perfectly collocated both in space and time. If successful, the polarimetric RO will represent the first technique able to provide these complementary information on precipitation. This is a relevant input for studies on heavy and violent rainfall events, which being poorly represented by the current-generation of Numerical Weather Prediction and General Circulation Models appear to be difficult to forecast on all time-scales. The Low Earth Orbiter hosting this experiment, to be launched in 2013, will orbit at 500 km altitude in a near-Polar orbit. The Radio Occulation payload includes a RO GNSS receiver and a dual polarization (H/V) limb oriented antenna to capture the signals of setting GNSS transmitters. NOAA and UCAR participate in the ground-segment of the radiometric experiment to enable near-real time dissemination of the level-1 standard RO products. The space-based GNSS RO technique scans the atmosphere vertically at fine resolution (close to 300 meter in the troposphere) by precisely measure the delay between a GNSS transmitter and a GNSS receiver aboard a Low Earth Orbiter, when the former is setting below or rising above the Earth limb. The standard, thermodynamical, products are extracted from the excess delay induced by the atmosphere at different layers. This presentation will not focus on this well-established application, but a novel concept using polarimetry to also retrieve rain information. The precipitation-measurement principle is

  18. Satellite estimates of urban development for hydrological modelling

    DEFF Research Database (Denmark)

    Kaspersen, Per Skougaard; Drews, Martin

    We investigate the applicability of medium resolution Landsat satellite imagery for mapping temporal changes in urban land cover in European cities for direct use in urban flood models. The overarching aim is to provide accurate and costand resource-efficient quantification of temporal changes...

  19. Satellite air temperature estimation for monitoring the canopy layer heat island of Milan

    DEFF Research Database (Denmark)

    Pichierri, Manuele; Bonafoni, Stefania; Biondi, Riccardo

    2012-01-01

    2007 and 2010 were processed. Analysis of the canopy layer heat island (CLHI) maps during summer months reveals an average heat island effect of 3–4K during nighttime (with some peaks around 5K) and a weak CLHI intensity during daytime. In addition, the satellite maps reveal a well defined island shape......In this work, satellite maps of the urban heat island of Milan are produced using satellite-based infrared sensor data. For this aim, we developed suitable algorithms employing satellite brightness temperatures for the direct air temperature estimation 2 m above the surface (canopy layer), showing...

  20. Estimation of evaporation rates over the Arabian Sea from Satellite data

    Digital Repository Service at National Institute of Oceanography (India)

    Rao, M.V.; RameshBabu, V.; Rao, L.V.G.; Sastry, J.S.

    Utilizing both the SAMIR brightness temperatures of Bhaskara 2 and GOSSTCOMP charts of NOAA satellite series, the evaporation rates over the Arabian Sea for June 1982 are estimated through the bulk aerodynamic method. The spatial distribution...

  1. Estimated Depth Maps of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery (Draft)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Estimated shallow-water, depth maps were produced using rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations in the...

  2. Estimated Depth Maps of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Estimated shallow-water, depth maps were produced using rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations in the...

  3. Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall-runoff model

    Science.gov (United States)

    Roy, Tirthankar; Gupta, Hoshin V.; Serrat-Capdevila, Aleix; Valdes, Juan B.

    2017-02-01

    Daily, quasi-global (50° N-S and 180° W-E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making diagnostic structural improvements to hydrologic models whenever possible.

  4. Precipitation estimation in mountainous terrain using multivariate geostatistics. Part II: isohyetal maps

    Science.gov (United States)

    Hevesi, Joseph A.; Flint, Alan L.; Istok, Jonathan D.

    1992-01-01

    Values of average annual precipitation (AAP) may be important for hydrologic characterization of a potential high-level nuclear-waste repository site at Yucca Mountain, Nevada. Reliable measurements of AAP are sparse in the vicinity of Yucca Mountain, and estimates of AAP were needed for an isohyetal mapping over a 2600-square-mile watershed containing Yucca Mountain. Estimates were obtained with a multivariate geostatistical model developed using AAP and elevation data from a network of 42 precipitation stations in southern Nevada and southeastern California. An additional 1531 elevations were obtained to improve estimation accuracy. Isohyets representing estimates obtained using univariate geostatistics (kriging) defined a smooth and continuous surface. Isohyets representing estimates obtained using multivariate geostatistics (cokriging) defined an irregular surface that more accurately represented expected local orographic influences on AAP. Cokriging results included a maximum estimate within the study area of 335 mm at an elevation of 7400 ft, an average estimate of 157 mm for the study area, and an average estimate of 172 mm at eight locations in the vicinity of the potential repository site. Kriging estimates tended to be lower in comparison because the increased AAP expected for remote mountainous topography was not adequately represented by the available sample. Regression results between cokriging estimates and elevation were similar to regression results between measured AAP and elevation. The position of the cokriging 250-mm isohyet relative to the boundaries of pinyon pine and juniper woodlands provided indirect evidence of improved estimation accuracy because the cokriging result agreed well with investigations by others concerning the relationship between elevation, vegetation, and climate in the Great Basin. Calculated estimation variances were also mapped and compared to evaluate improvements in estimation accuracy. Cokriging estimation variances

  5. A Method for Estimating BeiDou Inter-frequency Satellite Clock Bias

    Directory of Open Access Journals (Sweden)

    LI Haojun

    2016-02-01

    Full Text Available A new method for estimating the BeiDou inter-frequency satellite clock bias is proposed, considering the shortage of the current methods. The constant and variable parts of the inter-frequency satellite clock bias are considered in the new method. The data from 10 observation stations are processed to validate the new method. The characterizations of the BeiDou inter-frequency satellite clock bias are also analyzed using the computed results. The results of the BeiDou inter-frequency satellite clock bias indicate that it is stable in the short term. The estimated BeiDou inter-frequency satellite clock bias results are molded. The model results show that the 10 parameters of model for each satellite can express the BeiDou inter-frequency satellite clock bias well and the accuracy reaches cm level. When the model parameters of the first day are used to compute the BeiDou inter-frequency satellite clock bias of the second day, the accuracy also reaches cm level. Based on the stability and modeling, a strategy for the BeiDou satellite clock service is presented to provide the reference of our BeiDou.

  6. Effects of assimilating precipitation zones derived from satellite and lightning data on numerical simulations of tropical-like Mediterranean storms

    Directory of Open Access Journals (Sweden)

    L. Fita

    2009-08-01

    Full Text Available The scarcity of meteorological observations in maritime areas is a well-known problem that can be an important limitation in the study of different phenomena. Tropical-like storms or medicanes developed over the Mediterranean sea are intense storms with some similarities to the tropical ones. Although they do not reach the hurricane intensity, their potential for damage is very high, due to the densely populated Mediterranean coastal regions. In this study, the two notable cases of medicane development which occurred in the western Mediterranean basin in September 1996 and October 2003, are considered. The capability of mesoscale numerical models to simulate general aspects of such a phenomena has been previously shown. With the aim of improving the numerical results, an adjustment of the humidity vertical profiles in MM5 simulations is performed by means of satellite derived precipitation. Convective and stratiform precipitation types obtained from satellite images are used to individually adjust the profiles. Lightning hits are employed to identify convective grid points. The adjustment of the vertical humidity profiles is carried out in the European Centre for Medium-Range Weather Forecasts (ECMWF analyses used as initial conditions for the simulations. Analyses nudging to ECMWF analyses and to the satellite-based humidity-corrected version of these analyses has also been applied using Four Dimensional Data Assimilation (FDDA. An additional adjustment is applied as observation nudging of satellite/lightning information at different time and spatial resolutions. Statistical parameters are proposed and tested as an objective way to intercompare satellite-derived and simulated trajectories. Simulations of medicanes exhibit a strong sensitivity to vertical humidity profiles. Trajectories of the storms are improved or worsened by using FDDA. A case dependence is obtained on the characteristics of the humidity-corrected medicanes. FDDA sensitivity

  7. Effects of assimilating precipitation zones derived from satellite and lightning data on numerical simulations of tropical-like Mediterranean storms

    Energy Technology Data Exchange (ETDEWEB)

    Fita, L.; Romero, R.; Luque, A.; Ramis, C. [Univ. de les Illes Balears, Palma de Mallorca (Spain). Grup de Meteorologia

    2009-07-01

    The scarcity of meteorological observations in maritime areas is a well-known problem that can be an important limitation in the study of different phenomena. Tropical-like storms or medicanes developed over the Mediterranean sea are intense storms with some similarities to the tropical ones. Although they do not reach the hurricane intensity, their potential for damage is very high, due to the densely populated Mediterranean coastal regions. In this study, the two notable cases of medicane development which occurred in the western Mediterranean basin in September 1996 and October 2003, are considered. The capability of mesoscale numerical models to simulate general aspects of such a phenomena has been previously shown. With the aim of improving the numerical results, an adjustment of the humidity vertical profiles in MM5 simulations is performed by means of satellite derived precipitation. Convective and stratiform precipitation types obtained from satellite images are used to individually adjust the profiles. Lightning hits are employed to identify convective grid points. The adjustment of the vertical humidity profiles is carried out in the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses used as initial conditions for the simulations. Analyses nudging to ECMWF analyses and to the satellite-based humidity-corrected version of these analyses has also been applied using Four Dimensional Data Assimilation (FDDA). An additional adjustment is applied as observation nudging of satellite/lightning information at different time and spatial resolutions. Statistical parameters are proposed and tested as an objective way to intercompare satellite-derived and simulated trajectories. Simulations of medicanes exhibit a strong sensitivity to vertical humidity profiles. Trajectories of the storms are improved or worsened by using FDDA. A case dependence is obtained on the characteristics of the humidity-corrected medicanes. FDDA sensitivity on temporal and

  8. Evapotranspiration estimation using a normalized difference vegetation index transformation of satellite data

    Science.gov (United States)

    Seevers, P.M.; Ottmann, R. W.

    1994-01-01

    Evapotranspiration of irrigated crops on two irrigation service areas along the lower Colorado River was estimated using a normalized difference vegetation index of satellite data. A procedure was developed which equated the index to crop coefficients. Evapotranspiration estimates for fields for three dates of thematic mapper data were highly correlated with ground estimates. Service area estimates using thematic mapper and Advanced Very High Resolution Radiometer data agreed well with estimates based on US Geological Survey gauging station data.

  9. The precipitation products generation chain for the EUMETSAT Hydrological Satellite Application Facility at C.N.M.C.A.

    Science.gov (United States)

    Biron, Daniele; Melfi, Davide; Zauli, Francesco

    2008-08-01

    The EUMETSAT Satellite Application Facility in support to Hydrology (H-SAF) focuses on development of new geophysical products on precipitation, soil moisture and snow parameters and the utilisation of these parameters in hydrological models, NWP models and water management. The development phase of the H-SAF started in September 2005 under the leadership of Italian Meteorological Service. The "Centro Nazionale di Meteorologia e Climatologia Aeronautica (C.N.M.C.A.)", the Italian National Weather Centre, that physically hosts the generation chain of precipitation products, carried on activities to reach the final target: development of algorithms, validation of results, implementation of operative procedure to supply the service and to monitor the service performances. The paper shows the architectural status of the H-SAF precipitation group and stress the component of operations. It is shown the full correspondence with the EUMETSAT approved H-SAF documents, in particular the Algorithm Theoretical Design Document (ATDD), where products characteristics are referenced. Are also reported the first results, produced during the first H-SAF Workshop, held in Rome in October 2007, of validation activities performed on version 1 products, and last results of products distribution to beta-users in preparation of distributing version 2.

  10. Contemporary ground-based and satellite precipitating system characterization for desertification studies in Southern Italy

    Directory of Open Access Journals (Sweden)

    M. Casazza

    2008-07-01

    Full Text Available During the research project RIADE (Ricerca Integrata per l'Applicazione di tecnologie e processi innovativi per la lotta alla DEsertificazione, devoted to the study on the potential risk of desertification in Southern Italy, a particular attention has been paid also to the analysis of precipitations from three surface stations (Licata, Sicily; Rotondella, Basilicata; Surigheddu, Sardinia in order to improve the knowledge derived from the most modern climatological studies related to this subject. The point of view adopted is to better define the precipitation microphysical properties (in particular, the Drop Size Distribution, DSD, and its moments, which are deeply related to the cloud system that generates the precipitation events. In particular we have used a newly introduced Convective Stratiform discrimination technique, that allowed us to observe a prevalence of events, concentrated along Winter (Wi season, of different microphysical nature. In fact the prevailing Stratiform nature is related to Licata station, while for Surigheddu and for Rotondella the nature is mainly Convective. This distinction is related to the presence of drops of bigger dimensions and more intense precipitations in the latter case, while, in the former case, a prevalence of smaller drops and a less intense precipitation is recorded. This confirms the distinctive belonging to three different climatic regions, as indicated in the study by Brunetti et al. (2006. Our findings are important in the framework of desertification studies, because the cause of desertification can be related either to fertile soils removal (in the case of Convective events or to lack of precipitated water (in the case of Stratiform events. We have also analysed a sub-set of ten events, with contemporary presence of data from VIS/IR channels of METEOSAT-7, SSM/I data from F13 and MODIS data from Terra platform. This has been done both to confirm the findings of PLUDIX data analysis (which

  11. Utilization of satellite-derived estimates of meteorological and land surface characteristics in the Land Surface Model for vast agricultural region territory

    Science.gov (United States)

    Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Volkova, Elena

    2015-04-01

    The method has been elaborated to evaluate the water and heat regime characteristics of the territory on a regional scale for the vegetation season based on a physical-mathematical model of water and heat exchange between vegetation covered land surface and atmosphere (LSM, Land Surface Model) appropriate for using satellite information on land surface and meteorological conditions. The developed model is intended for calculating soil water content, evapotranspiration (evaporation from bare soil and transpiration by vegetation), vertical water and heat fluxes as well as land surface and vegetation cover temperatures and vertical distributions of temperature and moisture in the active soil layer. Parameters of the model are soil and vegetation characteristics and input variables are meteorological characteristics. Their values have been obtained from ground-based observations at agricultural meteorological stations and satellite-based measurements by scanning radiometers AVHRR/NOAA, MODIS/EOS Terra and Aqua and SEVIRI (geostationary satellites Meteosat-9, -10). The AVHRR data have been used to build the estimates of three types of land surface temperature (LST): land skin temperature Tsg, air temperature at a level of vegetation cover Ta and efficient radiation temperature Tseff, emissivity E, normalized vegetation index NDVI, vegetation cover fraction B, leaf area index LAI, and precipitation. The set of estimates derived from MODIS data has comprised values of LST Tls, E, NDVI and LAI. The SEVIRI-based retrievals have included Tls, Ta, Е at daylight and nighttime, LAI (daily) and precipitation. The case study has been carried out for agricultural Central Black Earth region of the European Russia of 227,300 sq.km containing 7 regions of the Russian Federation for years 2009-2013 vegetation seasons. Estimates of described characteristics have been built with the help of the developed original and improved pre-existing methods and technologies of thematic processing

  12. A Precipitation Satellite Downscaling & Re-Calibration Routine for TRMM 3B42 and GPM Data Applied to the Tropical Andes

    Science.gov (United States)

    Manz, B.; Buytaert, W.; Tobón, C.; Villacis, M.; García, F.

    2014-12-01

    With the imminent release of GPM it is essential for the hydrological user community to improve the spatial resolution of satellite precipitation products (SPPs), also retrospectively of historical time-series. Despite the growing number of applications, to date SPPs have two major weaknesses. Firstly, geosynchronous infrared (IR) SPPs, relying exclusively on cloud elevation/ IR temperature, fail to replicate ground rainfall rates especially for convective rainfall. Secondly, composite SPPs like TRMM include microwave and active radar to overcome this, but the coarse spatial resolution (0.25°) from infrequent orbital sampling often fails to: a) characterize precipitation patterns (especially extremes) in complex topography regions, and b) allow for gauge comparisons with adequate spatial support. This is problematic for satellite-gauge merging and subsequent hydrological modelling applications. We therefore present a new re-calibration and downscaling routine that is applicable to 0.25°/ 3-hrly TRMM 3B42 and Level 3 GPM time-series to generate 1 km estimates. 16 years of instantaneous TRMM radar (TPR) images were evaluated against a unique dataset of over 100 10-min rain gauges from the tropical Andes (Colombia & Ecuador) to develop a spatially distributed error surface. Long-term statistics on occurrence frequency, convective/ stratiform fraction and extreme precipitation probability (Gamma & Generalized Pareto distributions) were computed from TPR at the 1 km scale as well as from TPR and 3B42 at the 0.25° scale. To downscale from 0.25° to 1 km a stochastic generator was used to restrict precipitation occurrence to a fraction of the 1 km pixels within the 0.25° gridcell at every time-step. Regression modelling established a relationship between probability distributions at the 0.25° scale and rainfall amounts were assigned to the retained 1 km pixels by quantile-matching to the gridcell. The approach inherently provides mass conservation of the downscaled

  13. Estimating the yaw-attitude of BDS IGSO and MEO satellites

    Science.gov (United States)

    Dai, Xiaolei; Ge, Maorong; Lou, Yidong; Shi, Chuang; Wickert, Jens; Schuh, Harald

    2015-10-01

    Precise knowledge and consistent modeling of the yaw-attitude of GNSS satellites are essential for high-precision data processing and applications. As the exact attitude control mechanism for the satellites of the BeiDou Satellite Navigation System (BDS) is not yet released, the reverse kinematic precise point positioning (PPP) method was applied in our study. However, we confirm that the recent precise orbit determination (POD) processing for GPS satellites could not provide suitable products for estimating BDS attitude using the reverse PPP because of the special attitude control switching between the nominal and the orbit-normal mode. In our study, we propose a modified processing schema for studying the attitude behavior of the BDS satellites. In this approach, the observations of the satellites during and after attitude switch are excluded in the POD processing, so that the estimates, which are needed in the reverse PPP, are not contaminated by the inaccurate initial attitude mode. The modified process is validated by experimental data sets and the attitude yaw-angles of the BDS IGSO and MEO satellites are estimated with an accuracy of better than . Furthermore, the results confirm that the switch is executed when the Sun elevation is about and the actual orientation is very close to its target one. Based on the estimated yaw-angles, a preliminary attitude switch model was established and reintroduced into the POD, yielding to a substantial improvement in the orbit overlap RMS.

  14. Estimating return periods for daily precipitation extreme events over the Brazilian Amazon

    Science.gov (United States)

    Santos, Eliane Barbosa; Lucio, Paulo Sérgio; Santos e Silva, Cláudio Moisés

    2016-11-01

    This paper aims to model the occurrence of daily precipitation extreme events and to estimate the return period of these events through the extreme value theory (generalized extreme value distribution (GEV) and the generalized Pareto distribution (GPD)). The GEV and GPD were applied in precipitation series of homogeneous regions of the Brazilian Amazon. The GEV and GPD goodness of fit were evaluated by quantile-quantile (Q-Q) plot and by the application of the Kolmogorov-Smirnov (KS) test, which compares the cumulated empirical distributions with the theoretical ones. The Q-Q plot suggests that the probability distributions of the studied series are appropriated, and these results were confirmed by the KS test, which demonstrates that the tested distributions have a good fit in all sub-regions of Amazon, thus adequate to study the daily precipitation extreme event. For all return levels studied, more intense precipitation extremes is expected to occur within the South sub-regions and the coastal area of the Brazilian Amazon. The results possibly will have some practical application in local extreme weather forecast.

  15. 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.

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

    Science.gov (United States)

    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 skill during June through September, when Heidke skill scores are between 0.2 and 0.4. We demonstrate that the aerosol (clear sky) component of the GOES-R ABI visibility retrieval can be used to augment measurements from the 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.

  17. Using High Resolution Satellite Precipitation fields to Assess the Impacts of Climate Change on the Santa Cruz and San Pedro River Basins

    Science.gov (United States)

    Robles-Morua, A.; Vivoni, E.; Rivera-Fernandez, E. R.; Dominguez, F.; Meixner, T.

    2013-05-01

    Hydrologic modeling using high spatiotemporal resolution satellite precipitation products in the southwestern United States and northwest Mexico is important given the sparse nature of available rain gauges. In addition, the bimodal distribution of annual precipitation also presents a challenge as differential climate impacts during the winter and summer seasons are not currently well understood. In this work, we focus on hydrological comparisons using rainfall forcing from a satellite-based product, downscaled GCM precipitation estimates and available ground observations. The simulations are being conducted in the Santa Cruz and San Pedro river basins along the Arizona-Sonora border at high spatiotemporal resolutions (~100 m and ~1 hour). We use a distributed hydrologic model, known as the TIN-based Real-time Integrated Basin Simulator (tRIBS), to generate simulated hydrological fields under historical (1991-2000) and climate change (2031-2040) scenarios obtained from an application of the Weather Research and Forecast (WRF) model. Using the distributed model, we transform the meteorological scenarios at 10-km, hourly resolution into predictions of the annual water budget, seasonal land surface fluxes and individual hydrographs of flood and recharge events. We compare the model outputs and rainfall fields of the WRF products against the forcing from the North American Land Data Assimilation System (NLDAS) and available ground observations from the National Climatic Data Center (NCDC) and Arizona Meteorological Network (AZMET). For this contribution, we selected two full years in the historical period and in the future scenario that represent wet and dry conditions for each decade. Given the size of the two basins, we rely on a high performance computing platform and a parallel domain discretization with higher resolutions maintained at experimental catchments in each river basin. Model simulations utilize best-available data across the Arizona-Sonora border on

  18. Comparison of satellite-based evapotranspiration estimates over the Tibetan Plateau

    Science.gov (United States)

    Peng, Jian; Loew, Alexander; Chen, Xuelong; Ma, Yaoming; Su, Zhongbo

    2016-08-01

    The Tibetan Plateau (TP) plays a major role in regional and global climate. The understanding of latent heat (LE) flux can help to better describe the complex mechanisms and interactions between land and atmosphere. Despite its importance, accurate estimation of evapotranspiration (ET) over the TP remains challenging. Satellite observations allow for ET estimation at high temporal and spatial scales. The purpose of this paper is to provide a detailed cross-comparison of existing ET products over the TP. Six available ET products based on different approaches are included for comparison. Results show that all products capture the seasonal variability well with minimum ET in the winter and maximum ET in the summer. Regarding the spatial pattern, the High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) ET demonstrator dataset is very similar to the LandFlux-EVAL dataset (a benchmark ET product from the Global Energy and Water Cycle Experiment), with decreasing ET from the south-east to north-west over the TP. Further comparison against the LandFlux-EVAL over different sub-regions that are decided by different intervals of normalised difference vegetation index (NDVI), precipitation, and elevation reveals that HOLAPS agrees best with LandFlux-EVAL having the highest correlation coefficient (R) and the lowest root mean square difference (RMSD). These results indicate the potential for the application of the HOLAPS demonstrator dataset in understanding the land-atmosphere-biosphere interactions over the TP. In order to provide more accurate ET over the TP, model calibration, high accuracy forcing dataset, appropriate in situ measurements as well as other hydrological data such as runoff measurements are still needed.

  19. Analysis on BDS Satellite Internal Multipath and Its Impact on Wide-lane FCB Estimation

    Directory of Open Access Journals (Sweden)

    RUAN Rengui

    2017-08-01

    Full Text Available To the issue of the satellite internal multipath (SIMP of BeiDou satellites, it proposed and emphasized that the SIMP model should be established as a function of the nadir angle with respect to the observed satellite rather than the elevation of the measurement, so that it can be used for receivers at various altitude. BDS data from global distributed stations operated by the International Monitoring and Assessment System (iGMAS and the Multi-GNSS Experiment (MGEX of the International GNSS Service (IGS are collected and a new SIMP model as a piece-wise linear function of the nadir angle is released for the IGSO-and MEO-satellite groups and for B1, B2 and B3 frequency band individually. The SIMP of GEO,IGSO and MEO satellites is further analyzed with B1/B2 dual-frequency data onboard the FengYun-3 C(FY3C satellite at an altitude of~830 km, and it showed that, for nadir angles smaller than 7°, the SIMP values for GEO is quite close to the IGSO's, especially for B2, which may suggest that the SIMP model for IGSO satellites possibly also works for GEO satellites. It also demonstrated that, when the nadir angle is smaller than 12°for the MEO and 7°for the IGSO, the estimated SIMP model with data from FY3C is considerable consistent with that estimated with data collected at ground stations. Experiments are carried out to investigate the impacts of the SIMP on wide-lane fractional cycle bias (FCB estimation for BDS satellites. The result indicates that, with the correction of the estimated SIMP, the repeatability of the FCB series is significantly improved by more than 60% for all satellites. Specifically, for the MEO and IGSO satellites, the repeatability is smaller than 0.05 cycle; the repeatability of 0.023 and 0.068 cycles achieved for GEO satellites C01 and C02 respectively with the estimated SIMP model for IGSO satellites.

  20. Patterns of Precipitation and Convection Occurrence over the Mediterranean Basin Derived from a Decade of Microwave Satellite Observations

    Directory of Open Access Journals (Sweden)

    Bahjat Alhammoud

    2014-05-01

    Full Text Available The Mediterranean region is characterized by its vulnerability to changes in the water cycle, with the impact of global warming on the water resources being one of the major concerns in social, economical and scientific ambits. Even if precipitation is the best-known term of the Mediterranean water budget, large uncertainties remain due to the lack of suitable offshore observational data. In this study, we use the data provided by the Advanced Microwave Sounding Unit-B (AMSU-B on board NOAA satellites to detect and analyze precipitating and convective events over the last decade at spatial resolution of 0.2° latitude × 0.2° longitude. AMSU-B observation shows that rain occurrence is widespread over the Mediterranean in wintertime while reduced in the eastern part of the basin in summer. Both precipitation and convection occurrences display a weak diurnal cycle over sea. In addition, convection occurrences, which are essentially located over land during summertime, shift to mostly over the sea during autumn with maxima in the Ionian sub-basin and the Adriatic Sea. Precipitation occurrence is also inferred over the sea from two other widely used climatological datasets, HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data and the European Centre for Medium-Range Weather Forecasts (ECMWF reanalysis interim (ERA-Interim. There is generally a rather fair agreement between these climatologies for describing the large-scale patterns such as the strong latitudinal gradient of rain and eastward rain signal propagation. Furthermore, the higher spatial resolution of AMSU-B measurements (16 km at nadir gives access to mesoscale details in the region (e.g., coastal areas. AMSU-B measurements show less rain occurrences than HOAPS during wintertime, thereby suggesting that some of the thresholds used in our method might be too stringent during this season. We also observed that convection occurrences in ERA-Interim are systematically

  1. 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

    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......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...... 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 timearea model and historic rain series...

  2. A Parameter Estimation Scheme for Multiscale Kalman Smoother (MKS) Algorithm Used in Precipitation Data Fusion

    Science.gov (United States)

    Wang, Shugong; Liang, Xu

    2013-01-01

    A new approach is presented in this paper to effectively obtain parameter estimations for the Multiscale Kalman Smoother (MKS) algorithm. This new approach has demonstrated promising potentials in deriving better data products based on data of different spatial scales and precisions. Our new approach employs a multi-objective (MO) parameter estimation scheme (called MO scheme hereafter), rather than using the conventional maximum likelihood scheme (called ML scheme) to estimate the MKS parameters. Unlike the ML scheme, the MO scheme is not simply built on strict statistical assumptions related to prediction errors and observation errors, rather, it directly associates the fused data of multiple scales with multiple objective functions in searching best parameter estimations for MKS through optimization. In the MO scheme, objective functions are defined to facilitate consistency among the fused data at multiscales and the input data at their original scales in terms of spatial patterns and magnitudes. The new approach is evaluated through a Monte Carlo experiment and a series of comparison analyses using synthetic precipitation data. Our results show that the MKS fused precipitation performs better using the MO scheme than that using the ML scheme. Particularly, improvements are significant compared to that using the ML scheme for the fused precipitation associated with fine spatial resolutions. This is mainly due to having more criteria and constraints involved in the MO scheme than those included in the ML scheme. The weakness of the original ML scheme that blindly puts more weights onto the data associated with finer resolutions is overcome in our new approach.

  3. 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.

  4. Predicting cement distribution in geothermal sandstone reservoirs based on estimates of precipitation temperatures

    Science.gov (United States)

    Olivarius, Mette; Weibel, Rikke; Whitehouse, Martin; Kristensen, Lars; Hjuler, Morten L.; Mathiesen, Anders; Boyce, Adrian J.; Nielsen, Lars H.

    2016-04-01

    Exploitation of geothermal sandstone reservoirs is challenged by pore-cementing minerals since they reduce the fluid flow through the sandstones. Geothermal exploration aims at finding sandstone bodies located at depths that are adequate for sufficiently warm water to be extracted, but without being too cemented for warm water production. The amount of cement is highly variable in the Danish geothermal reservoirs which mainly comprise the Bunter Sandstone, Skagerrak and Gassum formations. The present study involves bulk and in situ stable isotope analyses of calcite, dolomite, ankerite, siderite and quartz in order to estimate at what depth they were formed and enable prediction of where they can be found. The δ18O values measured in the carbonate minerals and quartz overgrowths are related to depth since they are a result of the temperatures of the pore fluid. Thus the values indicate the precipitation temperatures and they fit the relative diagenetic timing identified by petrographical observations. The sandstones deposited during arid climatic conditions contain calcite and dolomite cement that formed during early diagenesis. These carbonate minerals precipitated as a response to different processes, and precipitation of macro-quartz took over at deeper burial. Siderite was the first carbonate mineral that formed in the sandstones that were deposited in a humid climate. Calcite began precipitating at increased burial depth and ankerite formed during deep burial and replaced some of the other phases. Ankerite and quartz formed in the same temperature interval so constrains on the isotopic composition of the pore fluid can be achieved. Differences in δ13C values exist between the sandstones that were deposited in arid versus humid environments, which suggest that different kinds of processes were active. The estimated precipitation temperatures of the different cement types are used to predict which of them are present in geothermal sandstone reservoirs in

  5. TRMM Science Highlights and Status of Precipitation Estimates on Monthly and Finder Time Scales

    Science.gov (United States)

    Adler, Robert; Einaudi, Franco (Technical Monitor)

    2001-01-01

    The Tropical Rainfall Measuring Mission (TRMM) has completed three years in orbit. A summary of research highlights will be presented focusing on application of TRMM data to topics ranging from climate analysis, through improving forecasts, to microphysical research. Monthly surface rainfall estimates over the ocean based on different instruments on TRMM currently differ by 20%. The difference is not surprising considering the different type of observations available for the first time from TRMM with both the passive and active microwave sensors. Resolving this difference will strengthen the validity and utility of ocean rainfall estimates and is the topic of ongoing research utilizing various facets of the TRMM validation and field experiment programs. The TRMM rainfall estimates are intercompared among themselves and with other estimates, including those of the standard, monthly Global Precipitation Climatology Project (GPCP) analysis. The GPCP analysis agrees roughly in magnitude with the passive microwave-based TRMM estimates which is not surprising considering GPCP over-ocean estimates are based on passive microwave observations. A three year TRMM rainfall climatology is presented based on the TRMM merged product, including anomaly fields related to the changing ENSO situation during the mission. Results of merging TRMM, other passive microwave observations, and geosynchronous infrared rainfall estimates into a global, tropical 3-hour time resolution analysis will also be described.

  6. Statistical and Hydrological Evaluation of TRMM-Based Multi-Satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the Latest Satellite Precipitation Products 3B42V7 Ready for Use in Ungauged Basins?

    Science.gov (United States)

    Xue, Xianwu; Hong, Yang; Limaye, Ashutosh S.; Gourley, Jonathan; Huffman, George J.; Khan, Sadiq Ibrahim; Dorji, Chhimi; Chen, Sheng

    2013-01-01

    The objective of this study is to quantitatively evaluate the successive Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products and further to explore the improvements and error propagation of the latest 3B42V7 algorithm relative to its predecessor 3B42V6 using the Coupled Routing and Excess Storage (CREST) hydrologic model in the mountainous Wangchu Basin of Bhutan. First, the comparison to a decade-long (2001-2010) daily rain gauge dataset reveals that: 1) 3B42V7 generally improves upon 3B42V6s underestimation both for the whole basin (bias from -41.15 to -8.38) and for a 0.250.25 grid cell with high-density gauges (bias from -40.25 to 0.04), though with modest enhancement of correlation coefficients (CC) (from 0.36 to 0.40 for basin-wide and from 0.37 to 0.41 for grid); and 2) 3B42V7 also improves its occurrence frequency across the rain intensity spectrum. Using the CREST model that has been calibrated with rain gauge inputs, the 3B42V6-based simulation shows limited hydrologic prediction NSCE skill (0.23 in daily scale and 0.25 in monthly scale) while 3B42V7 performs fairly well (0.66 in daily scale and 0.77 in monthly scale), a comparable skill score with the gauge rainfall simulations. After recalibrating the model with the respective TMPA data, significant improvements are observed for 3B42V6 across all categories, but not as much enhancement for the already well-performing 3B42V7 except for a reduction in bias (from -26.98 to -4.81). In summary, the latest 3B42V7 algorithm reveals a significant upgrade from 3B42V6 both in precipitation accuracy (i.e., correcting the underestimation) thus improving its potential hydrological utility. Forcing the model with 3B42V7 rainfall yields comparable skill scores with in-situ gauges even without recalibration of the hydrological model by the satellite precipitation, a compensating approach often used but not favored by the hydrology community, particularly in ungauged basins.

  7. 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.

  8. On Some Aspects of Precipitation over Tropical Indian Ocean Using Satellite Data

    Digital Repository Service at National Institute of Oceanography (India)

    RameshKumar, M.R.; Sreejith, O.P.

    rainfall. The next most important error, due to fair weather bias, arises due to the lack of precipitation measurements caused by the lows, depressions and cyclones, which most of the ships try to avoid. This problem is especially true in the case... of the Bay of Bengal, which is frequented by lows, depressions and cyclones throughout the year. Major drawbacks to studies of the rainfall over a particular area or region are the coverage of the instruments and their accuracy in measuring the areal...

  9. The new approach of polarimetric attenuation correction for improving radar quantitative precipitation estimation(QPE)

    Science.gov (United States)

    Gu, Ji-Young; Suk, Mi-Kyung; Nam, Kyung-Yeub; Ko, Jeong-Seok; Ryzhkov, Alexander

    2016-04-01

    To obtain high-quality radar quantitative precipitation estimation data, reliable radar calibration and efficient attenuation correction are very important. Because microwave radiation at shorter wavelength experiences strong attenuation in precipitation, accounting for this attenuation is the essential work at shorter wavelength radar. In this study, the performance of different attenuation/differential attenuation correction schemes at C band is tested for two strong rain events which occurred in central Oklahoma. And also, a new attenuation correction scheme (combination of self-consistency and hot-spot concept methodology) that separates relative contributions of strong convective cells and the rest of the storm to the path-integrated total and differential attenuation is among the algorithms explored. A quantitative use of weather radar measurement such as rainfall estimation relies on the reliable attenuation correction. We examined the impact of attenuation correction on estimates of rainfall in heavy rain events by using cross-checking with S-band radar measurements which are much less affected by attenuation and compared the storm rain totals obtained from the corrected Z and KDP and rain gages in these cases. This new approach can be utilized at shorter wavelength radars efficiently. Therefore, it is very useful to Weather Radar Center of Korea Meteorological Administration preparing X-band research dual Pol radar network.

  10. Probabilistic correction of precipitation measurement errors using a Bayesian Model Average Approach applied for the estimation of glacier accumulation

    Science.gov (United States)

    Moya Quiroga, Vladimir; Mano, Akira; Asaoka, Yoshihiro; Udo, Keiko; Kure, Shuichi; Mendoza, Javier

    2013-04-01

    Precipitation is a major component of the water cycle that returns atmospheric water to the ground. Without precipitation there would be no water cycle, all the water would run down the rivers and into the seas, then the rivers would dry up with no fresh water from precipitation. Although precipitation measurement seems an easy and simple procedure, it is affected by several systematic errors which lead to underestimation of the actual precipitation. Hence, precipitation measurements should be corrected before their use. Different correction approaches were already suggested in order to correct precipitation measurements. Nevertheless, focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this presentation we propose a Bayesian model average (BMA) approach for correcting rain gauge measurement errors. In the present study we used meteorological data recorded every 10 minutes at the Condoriri station in the Bolivian Andes. Comparing rain gauge measurements with totalisators rain measurements it was possible to estimate the rain underestimation. First, different deterministic models were optimized for the correction of precipitation considering wind effect and precipitation intensities. Then, probabilistic BMA correction was performed. The corrected precipitation was then separated into rainfall and snowfall considering typical Andean temperature thresholds of -1°C and 3°C. Hence, precipitation was separated into rainfall, snowfall and mixed precipitation. Then, relating the total snowfall with the glacier ice density, it was possible to estimate the glacier accumulation. Results show a yearly glacier accumulation of 1200 mm/year. Besides, results confirm that in tropical glaciers winter is not accumulation period, but a low ablation one. Results show that neglecting such correction may induce an underestimation higher than 35 % of total precipitation. Besides, the uncertainty range may induce differences up

  11. Discharge estimation in arid areas with the help of optical satellite data

    Science.gov (United States)

    Mett, M.; Aufleger, M.

    2009-04-01

    The MENA region is facing severe water scarcity. Overexploitation of groundwater resources leads to an ongoing drawdown of the water tables, salinisation and desertification of vast areas. To make matters worse enormous birth-rates, economic growth and refugees from conflict areas let the need for water explode. In the context of climate change this situation will even worsen and armed conflicts are within the bounds of possibility. To ease water scarcity many innovative techniques like artificial groundwater recharge are being developed or already state of the art. But missing hydrological information (for instance discharge data) often prevents design and efficient operation of such measures. Especially in poor countries hydrological measuring devices like gage stations are often missing, in a bad status or professionals of the water sector are absent. This leads to the paradox situation that in many arid regions water resources are indeed available but they cannot be utilised because they are not known. Nowadays different approaches are being designed to obtain hydrological information from perennial river systems with the help of satellite techniques. Mostly they are based on hydraulic parameters like river dimensions, roughness and water levels which can be derived from satellite data. By using conventional flow formulas and additional field investigations the discharge can be estimated. Another methodology derived information about maximum flow depth and flow width from optical sensors of high resolution to calculate discharge of the rivers whilst the flood. Attempts to derive discharge information from structural components of the river and fluviomorphologic changes due to changing flow regimes are in the focus of recent research. One attempt used Synthetic Aperture Radar (SAR) data to estimate discharge in braided river systems. Other attempts used airborne SAR imagery to obtain information about sinuosity and total river width of perennial braided river

  12. Spaceborne GPS receiver antenna phase center offset and variation estimation for the Shiyan 3 satellite

    Directory of Open Access Journals (Sweden)

    Gu Defeng

    2016-10-01

    Full Text Available In determining the orbits of low Earth orbit (LEO satellites using spaceborne GPS, the errors caused by receiver antenna phase center offset (PCO and phase center variations (PCVs are gradually becoming a major limiting factor for continued improvements to accuracy. Shiyan 3, a small satellite mission for space technology experimentation and climate exploration, was developed by China and launched on November 5, 2008. The dual-frequency GPS receiver payload delivers 1 Hz data and provides the basis for precise orbit determination within the range of a few centimeters. The antenna PCO and PCV error characteristics and the principles influencing orbit determination are analyzed. The feasibility of PCO and PCV estimation and compensation in different directions is demonstrated through simulation and in-flight tests. The values of receiver antenna PCO and PCVs for Gravity Recovery and Climate Experiment (GRACE and Shiyan 3 satellites are estimated from one month of data. A large and stable antenna PCO error, reaching up to 10.34 cm in the z-direction, is found with the Shiyan 3 satellite. The PCVs on the Shiyan 3 satellite are estimated and reach up to 3.0 cm, which is slightly larger than that of GRACE satellites. Orbit validation clearly improved with independent k-band ranging (KBR and satellite laser ranging (SLR measurements. For GRACE satellites, the average root mean square (RMS of KBR residuals improved from 1.01 cm to 0.88 cm. For the Shiyan 3 satellite, the average RMS of SLR residuals improved from 4.95 cm to 4.06 cm.

  13. Spaceborne GPS receiver antenna phase center offset and variation estimation for the Shiyan 3 satellite

    Institute of Scientific and Technical Information of China (English)

    Gu Defeng; Lai Yuwang; Liu Junhong; Ju Bing; Tu Jia

    2016-01-01

    In determining the orbits of low Earth orbit (LEO) satellites using spaceborne GPS, the errors caused by receiver antenna phase center offset (PCO) and phase center variations (PCVs) are gradually becoming a major limiting factor for continued improvements to accuracy. Shiyan 3, a small satellite mission for space technology experimentation and climate exploration, was developed by China and launched on November 5, 2008. The dual-frequency GPS receiver payload delivers 1 Hz data and provides the basis for precise orbit determination within the range of a few centime-ters. The antenna PCO and PCV error characteristics and the principles influencing orbit determi-nation are analyzed. The feasibility of PCO and PCV estimation and compensation in different directions is demonstrated through simulation and in-flight tests. The values of receiver antenna PCO and PCVs for Gravity Recovery and Climate Experiment (GRACE) and Shiyan 3 satellites are estimated from one month of data. A large and stable antenna PCO error, reaching up to 10.34 cm in the z-direction, is found with the Shiyan 3 satellite. The PCVs on the Shiyan 3 satellite are estimated and reach up to 3.0 cm, which is slightly larger than that of GRACE satellites. Orbit validation clearly improved with independent k-band ranging (KBR) and satellite laser ranging (SLR) measurements. For GRACE satellites, the average root mean square (RMS) of KBR resid-uals improved from 1.01 cm to 0.88 cm. For the Shiyan 3 satellite, the average RMS of SLR resid-uals improved from 4.95 cm to 4.06 cm.

  14. Controlling the Chaos Using Fuzzy Estimation in a Gyrostat Satellite

    Science.gov (United States)

    Guran, Ardeshir

    In this paper, we present a study of the dynamical behavior in a Kelvin type gyrostat satellite. We firstly obtain the Hamiltonian equations of our model by using Cardan angles as generalized coordinates. Then, we make this Hamiltonian dimensionless and calculate motion equations for this dimensionless system. The study of the Poincare's sections of this system shows us that chaotic motion regimes are present for specific parameter values. The main goal of this work is the finding of stabilizing orbits by using a control technique, the fuzzy control of Poincare map method, so that it can be applied to stabilize special periodic orbits in this system. Finally, we expect that the technique can be useful for a better understanding of control theory and their applications in gyrostat problems.

  15. The Impacts of Satellite Remotely Sensed Winds and Total Precipitable Vapour in WRF Tropical Cyclone Track Forecasts

    Directory of Open Access Journals (Sweden)

    Diandong Ren

    2016-01-01

    Full Text Available This study assesses the impact assimilating the scatterometer near-surface wind observations and total precipitable water from the SSMI, into WRF on genesis and track forecasting of four tropical cyclones (TCs. These TCs are selected to be representative of different intensity categories and basins. Impact is via a series of data denial experiments that systematically exclude the remote sensed information. Compared with the control case, in which only the final analysis atmospheric variables are used to initialize and provide the lateral boundary conditions, the data assimilation runs performed consistently better, but with very different skill levels for the different TCs. Eliassen-Palm flux analyses are employed. It is confirmed that if a polar orbital satellite footprint passes over the TC’s critical genesis region, the forecast will profit most from assimilating the remotely sensed information. If the critical genesis region lies within an interorbital gap then, regardless of how strong the TC later becomes (e.g., Katrina 2005, the improvement from assimilating near-surface winds and total precipitable water in the model prediction is severely limited. This underpins the need for a synergy of data from different scatterometers/radiometers. Other approaches are suggested to improve the accuracy in the prediction of TC genesis and tracks.

  16. 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.

  17. Satellite-scale Estimates of the "b Parameter" Relating Vegetation Water Content and SMOS Optical Thickness

    Science.gov (United States)

    Patton, J. C.; Hornbuckle, B. K.

    2013-12-01

    Microwave radiation emitted by Earth's land surface is primarily determined by soil moisture and vegetation. One of the effects of vegetation on surface microwave emissions is often termed the "vegetation optical thickness" or "vegetation opacity" and is often abbreviated as tau. Retrievals of soil moisture from microwave radiometer measurements requires knowledge of tau. The Soil Moisture and Ocean Salinity (SMOS) satellite measures microwave radiation at multiple incidence angles, enabling the simultaneous retrieval of soil moisture and tau. Other soil moisture satellites, such as the upcoming Soil Moisture Active Passive (SMAP) satellite, only measure at single incidence angles and may need auxiliary sources of tau data in order to retrieve soil moisture. One proposed method for estimating tau for these satellites is by relating reflectance data, e.g. the normalized difference vegetation index, to vegetation water content (VWC), then relating VWC to tau. VWC and tau can be related through the b parameter, i.e. tau = b x VWC. Values of b for different land cover types have been estimated from tower (~1 m) and airplane (~10-100 m) data, but have not been measured at the satellite scale (~10 km). Estimating b at the satellite scale from measurements at smaller scales is difficult because the effective value of b in a satellite pixel may not be well represented by linear weighted average based on the fraction of each land cover type in the pixel. However, by relating county crop yields, estimated by the USDA National Agricultural Statistics Service, to measurements of SMOS tau, and by using certain allometric relationships, such as the ratio of water to dry matter and the harvest index of crops, we can estimate b at the satellite scale. We have used this method to estimate b for each Iowa county for the years 2010-2012. Initial results suggest that b may change year to year; our current estimates for b in Iowa range from 0.065 in 2010 to 0.100 in 2012. These

  18. Evaluation of TMPA 3B42 Precipitation Estimates during the Passage of Tropical Cyclones over New Caledonia

    Science.gov (United States)

    Deo, Anil; Walsh, Kevin J. E.; Peltier, Alexandre

    2017-08-01

    This study evaluates the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 version 7 (V7) estimates of tropical cyclone (TC) rainfall over New Caledonia using the island rain gauge observations as the ground-truth reference. Several statistical measures and techniques are utilised to characterise the difference and similarity between TMPA and the gauge observations. The results show that TMPA has skill in representing the observed rainfall during the passage of TCs. TMPA overestimates light rainfall events and underestimates moderate to higher rainfall events. The skill deteriorates with increasing elevation, as underestimation by TMPA is greater at higher altitudes. The ability of TMPA also varies with TC intensity and distance from the TC centre, whereby it is more skilful for less intense TCs (category 1-2) and near the TC centre than in the outer rainbands. The ability of TMPA varies from case to case but a better performance is shown for TCs with a higher average rainfall. Finally, case studies of TC Vania (2011), TC Innis (2009), and TC Erica (2003) show that TMPA has the ability to represent the spatial distribution of the observed rainfall, but it tends to underestimate the higher rainfall events.

  19. NEXRAD quantitative precipitation estimates, data acquisition, and processing for the DuPage County, Illinois, streamflow-simulation modeling system

    Science.gov (United States)

    Ortel, Terry W.; Spies, Ryan R.

    2015-11-19

    Next-Generation Radar (NEXRAD) has become an integral component in the estimation of precipitation (Kitzmiller and others, 2013). The high spatial and temporal resolution of NEXRAD has revolutionized the ability to estimate precipitation across vast regions, which is especially beneficial in areas without a dense rain-gage network. With the improved precipitation estimates, hydrologic models can produce reliable streamflow forecasts for areas across the United States. NEXRAD data from the National Weather Service (NWS) has been an invaluable tool used by the U.S. Geological Survey (USGS) for numerous projects and studies; NEXRAD data processing techniques similar to those discussed in this Fact Sheet have been developed within the USGS, including the NWS Quantitative Precipitation Estimates archive developed by Blodgett (2013).

  20. Estimating the exceedance probability of extreme rainfalls up to the probable maximum precipitation

    Science.gov (United States)

    Nathan, Rory; Jordan, Phillip; Scorah, Matthew; Lang, Simon; Kuczera, George; Schaefer, Melvin; Weinmann, Erwin

    2016-12-01

    If risk-based criteria are used in the design of high hazard structures (such as dam spillways and nuclear power stations), then it is necessary to estimate the annual exceedance probability (AEP) of extreme rainfalls up to and including the Probable Maximum Precipitation (PMP). This paper describes the development and application of two largely independent methods to estimate the frequencies of such extreme rainfalls. One method is based on stochastic storm transposition (SST), which combines the "arrival" and "transposition" probabilities of an extreme storm using the total probability theorem. The second method, based on "stochastic storm regression" (SSR), combines frequency curves of point rainfalls with regression estimates of local and transposed areal rainfalls; rainfall maxima are generated by stochastically sampling the independent variates, where the required exceedance probabilities are obtained using the total probability theorem. The methods are applied to two large catchments (with areas of 3550 km2 and 15,280 km2) located in inland southern Australia. Both methods were found to provide similar estimates of the frequency of extreme areal rainfalls for the two study catchments. The best estimates of the AEP of the PMP for the smaller and larger of the catchments were found to be 10-7 and 10-6, respectively, but the uncertainty of these estimates spans one to two orders of magnitude. Additionally, the SST method was applied to a range of locations within a meteorologically homogenous region to investigate the nature of the relationship between the AEP of PMP and catchment area.

  1. An adaptive Multiplicative Extened Kalman Filter for Attitude Estimation of Marine Satellite Tracking Antenna

    DEFF Research Database (Denmark)

    Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar

    2016-01-01

    Satellite tracking is a challenging task for marine applications due to the disturbance from ocean waves. An Attitude Heading and Reference System (AHRS) for measuring ship attitude, based on Microelectromechanical Systems (MEMS) sensors, is a key part for satellite tracking. In this paper......, an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...

  2. Estimation of Vegetation Aerodynamic Roughness of Natural Regions Using Frontal Area Density Determined from Satellite Imagery

    Science.gov (United States)

    Jasinski, Michael F.; Crago, Richard

    1994-01-01

    Parameterizations of the frontal area index and canopy area index of natural or randomly distributed plants are developed, and applied to the estimation of local aerodynamic roughness using satellite imagery. The formulas are expressed in terms of the subpixel fractional vegetation cover and one non-dimensional geometric parameter that characterizes the plant's shape. Geometrically similar plants and Poisson distributed plant centers are assumed. An appropriate averaging technique to extend satellite pixel-scale estimates to larger scales is provided. ne parameterization is applied to the estimation of aerodynamic roughness using satellite imagery for a 2.3 sq km coniferous portion of the Landes Forest near Lubbon, France, during the 1986 HAPEX-Mobilhy Experiment. The canopy area index is estimated first for each pixel in the scene based on previous estimates of fractional cover obtained using Landsat Thematic Mapper imagery. Next, the results are incorporated into Raupach's (1992, 1994) analytical formulas for momentum roughness and zero-plane displacement height. The estimates compare reasonably well to reference values determined from measurements taken during the experiment and to published literature values. The approach offers the potential for estimating regionally variable, vegetation aerodynamic roughness lengths over natural regions using satellite imagery when there exists only limited knowledge of the vegetated surface.

  3. Efficiency assessment of using satellite data for crop area estimation in Ukraine

    Science.gov (United States)

    Gallego, Francisco Javier; Kussul, Nataliia; Skakun, Sergii; Kravchenko, Oleksii; Shelestov, Andrii; Kussul, Olga

    2014-06-01

    The knowledge of the crop area is a key element for the estimation of the total crop production of a country and, therefore, the management of agricultural commodities markets. Satellite data and derived products can be effectively used for stratification purposes and a-posteriori correction of area estimates from ground observations. This paper presents the main results and conclusions of the study conducted in 2010 to explore feasibility and efficiency of crop area estimation in Ukraine assisted by optical satellite remote sensing images. The study was carried out on three oblasts in Ukraine with a total area of 78,500 km2. The efficiency of using images acquired by several satellite sensors (MODIS, Landsat-5/TM, AWiFS, LISS-III, and RapidEye) combined with a field survey on a stratified sample of square segments for crop area estimation in Ukraine is assessed. The main criteria used for efficiency analysis are as follows: (i) relative efficiency that shows how much time the error of area estimates can be reduced with satellite images, and (ii) cost-efficiency that shows how much time the costs of ground surveys for crop area estimation can be reduced with satellite images. These criteria are applied to each satellite image type separately, i.e., no integration of images acquired by different sensors is made, to select the optimal dataset. The study found that only MODIS and Landsat-5/TM reached cost-efficiency thresholds while AWiFS, LISS-III, and RapidEye images, due to its high price, were not cost-efficient for crop area estimation in Ukraine at oblast level.

  4. Temporal scaling analysis of irradiance estimated from daily satellite data and numerical modelling

    Science.gov (United States)

    Vindel, Jose M.; Navarro, Ana A.; Valenzuela, Rita X.; Ramírez, Lourdes

    2016-11-01

    The temporal variability of global irradiance estimated from daily satellite data and numerical models has been compared for different spans of time. According to the time scale considered, a different behaviour can be expected for each climate. Indeed, for all climates and at small scale, the persistence decreases as this scale increases, but the mediterranean climate, and its continental variety, shows higher persistence than oceanic climate. The probabilities of maintaining the values of irradiance after a certain period of time have been used as a first approximation to analyse the quality of each source, according to the climate. In addition, probability distributions corresponding to variations of clearness indices measured at several stations located in different climate zones have been compared with those obtained from satellite and modelling estimations. For this work, daily radiation data from the reanalysis carried out by the European Centre for Medium-Range Weather Forecasts and from the Satellite Application Facilities on climate monitoring have been used for mainland Spain. According to the results, the temporal series estimation of irradiance is more accurate when using satellite data, independent of the climate considered. In fact, the coefficients of determination corresponding to the locations studied are always above 0.92 in the case of satellite data, while this coefficient decreases to 0.69 for some cases of the numerical model. This conclusion is more evident in oceanic climates, where the most important errors can be observed. Indeed, in this case, the RRMSE derived from the CM-SAF estimations is 20.93%, while in the numerical model, it is 48.33%. Analysis of the probabilities corresponding to variations in the clearness indices also shows a better behaviour of the satellite-derived estimates for oceanic climate. For the standard mediterranean climate, the satellite also provides better results, though the numerical model improves

  5. Estimating the Retrievability of Temperature Profiles from Satellite Infrared Measurements

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A method is developed to assess retrievability, namely the retrieval potential for atmospheric temperature profiles, from satellite infrared measurements in clear-sky conditions. This technique is based upon generalized linear inverse theory and empirical orthogonal function analysis. Utilizing the NCEP global temperature reanalysis data in January and July from 1999 to 2003, the retrievabilities obtained with the Atmospheric Infrared Sounder (AIRS) and the High Resolution Infrared Radiation Sounder/3 (HIRS/3)sounding channel data are derived respectively for each standard pressure level on a global scale. As an incidental result of this study, the optimum truncation number in the method of generalized linear inverse is deduced too. The results show that the retrievabilities of temperature obtained with the two datasets are similar in spatial distribution and seasonal change characteristics. As for the vertical distribution, the retrievabilities are low in the upper and lower atmosphere, and high between 400 hPa and 850 hPa. For the geographical distribution, the retrievabilities are low in the low-latitude oceanic regions and in some regions in Antarctica, and relatively high in mid-high latitudes and continental regions. Compared with the HIRS/3 data, the retrievability obtained with the AIRS data can be improved by an amount between 0.15 and 0.40.

  6. 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.

  7. Comparison of spatial interpolation methods for the estimation of precipitation distribution in Distrito Federal, Brazil

    Science.gov (United States)

    Borges, Pablo de Amorim; Franke, Johannes; da Anunciação, Yumiko Marina Tanaka; Weiss, Holger; Bernhofer, Christian

    2016-01-01

    Available climatological information of Distrito Federal does not satisfy the requirements for detailed climate diagnosis, as they do not provide the necessary spatial resolution for water resources management purposes. Annual and seasonal climatology (1971-2000) of precipitation from 6 meteorological stations and 54 rain gauges from Central Brazil were used to test eight different spatial interpolation methods. Geographical factors (i.e., altitude, longitude and latitude) explain a large portion of precipitation in the region, and therefore, multivariate models were included. The performance of estimations was assessed through independent validation using mean square error, correlation coefficient and Nash-Sutcliffe efficiency criterion. Inverse distance weighting (IDW), ordinary kriging (OK) and the multivariate regression with interpolation of residuals by IDW (MRegIDW) and OK (MRegOK) have performed the lowest errors and the highest correlation and Nash-Sutcliffe efficiency criterion. In general, interpolation methods provide similar spatial distributions of rainfall wherever observation network is dense. However, the inclusion of geographical variables to the interpolation method should improve estimates in areas where the observation network density is low. Nevertheless, the assessment of uncertainties using a geostatistical method provides supplementary and qualitative information which should be considered when interpreting the spatial distribution of rainfall.

  8. Use of objective analysis to estimate winter temperature and precipitation at different stations over western Himalaya

    Indian Academy of Sciences (India)

    Jagdish Chandra Joshi; Ashwagosha Ganju

    2010-10-01

    Temperature and fresh snow are essential inputs in an avalanche forecasting model.Without these parameters,prediction of avalanche occurrence for a region would be very difficult.In the complex terrain of Himalaya,nonavailability of snow and meteorological data of the remote locations during snow storms in the winter is a common occurrence.In view of this persistent problem present study estimates maximum temperature,minimum temperature,ambient temperature and precipitation intensity on different regions of Indian western Himalaya by using similar parameters of the neighbouring regions.The location at which parameters are required and its neighbouring locations should all fall in the same snow climatic zone.Initial step to estimate the parameters at a location,is to shift the parameters of neighbouring regions at a reference height corresponding to the altitude of the location at which parameters are to be estimated.The parameters at this reference height are then spatially interpolated by using Barnes objective analysis.The parameters estimated on different locations are compared with the observed one and the Root Mean Square Errors (RMSE)of the observed and estimated values of the parameters are discussed for the winters of 2007 –2008.

  9. Capturing heterogeneity: The role of a study area's extent for estimating net precipitation

    Science.gov (United States)

    Zimmermann, Alexander; Voss, Sebastian; Metzger, Johanna Clara; Hildebrandt, Anke; Zimmermann, Beate

    2016-04-01

    Accurate and precise estimates of net precipitation are required for many hydrological applications. For instance, most interception models require high quality estimates of the canopy storage capacity and the free throughfall coefficient. Good estimates of these parameters, in turn, critically depend on the quality of throughfall estimates. Previous attempts to guide throughfall sampling focused on the selection of an appropriate sample size, support, and sampling design. Comparatively little attention has been given to the role of the extent, i.e. the size of the area under study. In this contribution we investigate the influence of the extent on the representativeness of mean throughfall estimates for simply structured and heterogeneous forest ecosystems. We based our investigation on stochastic simulations which we derived from large empirical throughfall datasets. Using the simulated throughfall fields, we conducted virtual sampling experiments using a number of typical extents. We ran these tests both for a range of event sizes and for accumulated data. Our findings suggest that the size of the study area should be carefully adapted to the required temporal resolution of the throughfall data (i.e. event-based versus long-term) and to the complexity of the system under study.

  10. The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO2 from nadir-viewing satellites by weighted convolution

    Science.gov (United States)

    Beirle, Steffen; Hörmann, Christoph; Jöckel, Patrick; Liu, Song; Penning de Vries, Marloes; Pozzer, Andrea; Sihler, Holger; Valks, Pieter; Wagner, Thomas

    2016-07-01

    The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1-0.2 × 1015 molecules cm-2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study.

  11. Wave Period and Coastal Bathymetry Estimations from Satellite Images

    Science.gov (United States)

    Danilo, Celine; Melgani, Farid

    2016-08-01

    We present an approach for wave period and coastal water depth estimation. The approach based on wave observations, is entirely independent of ancillary data and can theoretically be applied to SAR or optical images. In order to demonstrate its feasibility we apply our method to more than 50 Sentinel-1A images of the Hawaiian Islands, well-known for its long waves. Six wave buoys are available to compare our results with in-situ measurements. The results on Sentinel-1A images show that half of the images were unsuitable for applying the method (no swell or wavelength too small to be captured by the SAR). On the other half, 78% of the estimated wave periods are in accordance with buoy measurements. In addition, we present preliminary results of the estimation of the coastal water depth on a Landsat-8 image (with characteristics close to Sentinel-2A). With a squared correlation coefficient of 0.7 for ground truth measurement, this approach reveals promising results for monitoring coastal bathymetry.

  12. Geothermal Heat Flux Underneath Ice Sheets Estimated From Magnetic Satellite Data

    DEFF Research Database (Denmark)

    Fox Maule, Cathrine; Purucker, M.E.; Olsen, Nils

    The geothermal heat flux is an important factor in the dynamics of ice sheets, and it is one of the important parameters in the thermal budgets of subglacial lakes. We have used satellite magnetic data to estimate the geothermal heat flux underneath the ice sheets in Antarctica and Greenland....... By using satellite data, we are able to make heat flux maps covering the entire Antarctic continent and all of Greenland. We find that the heat flux varies from less than 50 to more than 150~mW/m2 underneath the ice sheets. To validate our results, we have compared our heat flux estimate with geologic...

  13. Precipitation estimation in mountainous terrain using multivariate geostatistics. Part I: structural analysis

    Science.gov (United States)

    Hevesi, Joseph A.; Istok, Jonathan D.; Flint, Alan L.

    1992-01-01

    Values of average annual precipitation (AAP) are desired for hydrologic studies within a watershed containing Yucca Mountain, Nevada, a potential site for a high-level nuclear-waste repository. Reliable values of AAP are not yet available for most areas within this watershed because of a sparsity of precipitation measurements and the need to obtain measurements over a sufficient length of time. To estimate AAP over the entire watershed, historical precipitation data and station elevations were obtained from a network of 62 stations in southern Nevada and southeastern California. Multivariate geostatistics (cokriging) was selected as an estimation method because of a significant (p = 0.05) correlation of r = .75 between the natural log of AAP and station elevation. A sample direct variogram for the transformed variable, TAAP = ln [(AAP) 1000], was fitted with an isotropic, spherical model defined by a small nugget value of 5000, a range of 190 000 ft, and a sill value equal to the sample variance of 163 151. Elevations for 1531 additional locations were obtained from topographic maps to improve the accuracy of cokriged estimates. A sample direct variogram for elevation was fitted with an isotropic model consisting of a nugget value of 5500 and three nested transition structures: a Gaussian structure with a range of 61 000 ft, a spherical structure with a range of 70 000 ft, and a quasi-stationary, linear structure. The use of an isotropic, stationary model for elevation was considered valid within a sliding-neighborhood radius of 120 000 ft. The problem of fitting a positive-definite, nonlinear model of coregionalization to an inconsistent sample cross variogram for TAAP and elevation was solved by a modified use of the Cauchy-Schwarz inequality. A selected cross-variogram model consisted of two nested structures: a Gaussian structure with a range of 61 000 ft and a spherical structure with a range of 190 000 ft. Cross validation was used for model selection and for

  14. 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...

  15. Pico satellite attitude estimation via Robust Unscented Kalman Filter in the presence of measurement faults.

    Science.gov (United States)

    Soken, Halil Ersin; Hajiyev, Chingiz

    2010-07-01

    In the normal operation conditions of a pico satellite, a conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into consideration with a small weight, and the estimations are corrected without affecting the characteristics of the accurate ones. Two different RUKF algorithms, one with single scale factor and one with multiple scale factors, are proposed and applied for the attitude estimation process of a pico satellite. The results of these algorithms are compared for different types of measurement faults in different estimation scenarios and recommendations about their applications are given.

  16. 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

  17. Estimation of vegetation cover resilience from satellite time series

    Directory of Open Access Journals (Sweden)

    T. Simoniello

    2008-07-01

    Full Text Available Resilience is a fundamental concept for understanding vegetation as a dynamic component of the climate system. It expresses the ability of ecosystems to tolerate disturbances and to recover their initial state. Recovery times are basic parameters of the vegetation's response to forcing and, therefore, are essential for describing realistic vegetation within dynamical models. Healthy vegetation tends to rapidly recover from shock and to persist in growth and expansion. On the contrary, climatic and anthropic stress can reduce resilience thus favouring persistent decrease in vegetation activity.

    In order to characterize resilience, we analyzed the time series 1982–2003 of 8 km GIMMS AVHRR-NDVI maps of the Italian territory. Persistence probability of negative and positive trends was estimated according to the vegetation cover class, altitude, and climate. Generally, mean recovery times from negative trends were shorter than those estimated for positive trends, as expected for vegetation of healthy status. Some signatures of inefficient resilience were found in high-level mountainous areas and in the Mediterranean sub-tropical ones. This analysis was refined by aggregating pixels according to phenology. This multitemporal clustering synthesized information on vegetation cover, climate, and orography rather well. The consequent persistence estimations confirmed and detailed hints obtained from the previous analyses. Under the same climatic regime, different vegetation resilience levels were found. In particular, within the Mediterranean sub-tropical climate, clustering was able to identify features with different persistence levels in areas that are liable to different levels of anthropic pressure. Moreover, it was capable of enhancing reduced vegetation resilience also in the southern areas under Warm Temperate sub-continental climate. The general consistency of the obtained results showed that, with the help of suited analysis

  18. A Novel Sampling Method for Satellite-Based Offshore Wind Resource Estimation

    DEFF Research Database (Denmark)

    Badger, Merete; Badger, Jake; Hasager, Charlotte Bay

    Synthetic aperture radar (SAR) measurements from satellites can be used to estimate the spatial wind speed variation offshore in great detail. The radar senses cm-scale roughness at the sea surface which can be translated to wind speed at the height 10 m using an empirical geophysical model......-based wind climatology have improved gradually as more data were collected. The satellite scenes have been treated as random samples and weighted equally in our previous analyses. Here we introduce a novel sampling strategy based on the wind class methodology that is normally applied in numerical modeling...... climatologically representative large-scale meteorological conditions for the region of interest. The wind classes are used to make the most representative selection of satellite images from the ENVISAT image catalogue. A minimum of one satellite image is chosen per wind class. The frequency of occurrence of each...

  19. 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

  20. Estimation of Leaf Area Index Using IRS Satellite Images

    Directory of Open Access Journals (Sweden)

    A Faridhosseini

    2012-12-01

    Full Text Available Estimation of vegetation cover attributes, such as the Leaf Area Index (LAI, is an important step in identifying the amount of water use for some plants. The goal of this study is to investigate the feasibility of using IRS LISS-III data to retrieve LAI. To get a LAI retrieval model based on reflectance and vegetation index, detailed field data were collected in the study area of eastern Iran. In this study, atmospheric corrected IRS LISS-III imagery was used to calculate Normalized Difference Vegetation Index (NDVI. Data of 50 samples of LAI were measured by Sun Scan System – SS1 in the study area. In situ measurements of LAI were related to widely use spectral vegetation indices (NDVI. The best model through analyzing the results was LAI = 19.305×NDVI+5.514 using the method of linear-regression analysis. The results showed that the correlation coefficient R2 was 0.534 and RMSE was 0.67. Thereby, suggesting that, when using remote sensing NDVI for LAI estimation, not only is the choice of NDVI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using multi- spectral imagery for large-scale mapping of vegetation biophysical variables.

  1. 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

  2. Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery

    Directory of Open Access Journals (Sweden)

    Nobuoto Nojima

    2010-09-01

    Full Text Available For a quick and stable estimation of earthquake damaged buildings worldwide, using Phased Array type L-band Synthetic Aperture Radar (PALSAR loaded on the Advanced Land Observing Satellite (ALOS satellite, a model combining the usage of satellite synthetic aperture radar (SAR imagery and Japan Meteorological Agency (JMA-scale seismic intensity is proposed. In order to expand the existing C-band SAR based damage estimation model into L-band SAR, this paper rebuilds a likelihood function for severe damage ratio, on the basis of dataset from Japanese Earth Resource Satellite-1 (JERS-1/SAR (L-band SAR images observed during the 1995 Kobe earthquake and its detailed ground truth data. The model which integrates the fragility functions of building damage in terms of seismic intensity and the proposed likelihood function is then applied to PALSAR images taken over the areas affected by the 2007 earthquake in Pisco, Peru. The accuracy of the proposed damage estimation model is examined by comparing the results of the analyses with field investigations and/or interpretation of high-resolution satellite images.

  3. Predicting urban stormwater runoff with quantitative precipitation estimates from commercial microwave links

    Science.gov (United States)

    Pastorek, Jaroslav; Fencl, Martin; Stránský, David; Rieckermann, Jörg; Bareš, Vojtěch

    2017-04-01

    Reliable and representative rainfall data are crucial for urban runoff modelling. However, traditional precipitation measurement devices often fail to provide sufficient information about the spatial variability of rainfall, especially when heavy storm events (determining design of urban stormwater systems) are considered. Commercial microwave links (CMLs), typically very dense in urban areas, allow for indirect precipitation detection with desired spatial and temporal resolution. Fencl et al. (2016) recognised the high bias in quantitative precipitation estimates (QPEs) from CMLs which significantly limits their usability and, in order to reduce the bias, suggested a novel method for adjusting the QPEs to existing rain gauge networks. Studies evaluating the potential of CMLs for rainfall detection so far focused primarily on direct comparison of the QPEs from CMLs to ground observations. In contrast, this investigation evaluates the suitability of these innovative rainfall data for stormwater runoff modelling on a case study of a small ungauged (in long-term perspective) urban catchment in Prague-Letňany, Czech Republic (Fencl et al., 2016). We compare the runoff measured at the outlet from the catchment with the outputs of a rainfall-runoff model operated using (i) CML data adjusted by distant rain gauges, (ii) rainfall data from the distant gauges alone and (iii) data from a single temporary rain gauge located directly in the catchment, as it is common practice in drainage engineering. Uncertainties of the simulated runoff are analysed using the Bayesian method for uncertainty evaluation incorporating a statistical bias description as formulated by Del Giudice et al. (2013). Our results show that adjusted CML data are able to yield reliable runoff modelling results, primarily for rainfall events with convective character. Performance statistics, most significantly the timing of maximal discharge, reach better (less uncertain) values with the adjusted CML data

  4. Vegetation Height Estimation Near Power transmission poles Via satellite Stereo Images using 3D Depth Estimation Algorithms

    Science.gov (United States)

    Qayyum, A.; Malik, A. S.; Saad, M. N. M.; Iqbal, M.; Abdullah, F.; Rahseed, W.; Abdullah, T. A. R. B. T.; Ramli, A. Q.

    2015-04-01

    Monitoring vegetation encroachment under overhead high voltage power line is a challenging problem for electricity distribution companies. Absence of proper monitoring could result in damage to the power lines and consequently cause blackout. This will affect electric power supply to industries, businesses, and daily life. Therefore, to avoid the blackouts, it is mandatory to monitor the vegetation/trees near power transmission lines. Unfortunately, the existing approaches are more time consuming and expensive. In this paper, we have proposed a novel approach to monitor the vegetation/trees near or under the power transmission poles using satellite stereo images, which were acquired using Pleiades satellites. The 3D depth of vegetation has been measured near power transmission lines using stereo algorithms. The area of interest scanned by Pleiades satellite sensors is 100 square kilometer. Our dataset covers power transmission poles in a state called Sabah in East Malaysia, encompassing a total of 52 poles in the area of 100 km. We have compared the results of Pleiades satellite stereo images using dynamic programming and Graph-Cut algorithms, consequently comparing satellites' imaging sensors and Depth-estimation Algorithms. Our results show that Graph-Cut Algorithm performs better than dynamic programming (DP) in terms of accuracy and speed.

  5. Characterization of satellite based proxies for estimating nucleation mode particles over South Africa

    Directory of Open Access Journals (Sweden)

    A.-M. Sundström

    2014-10-01

    Full Text Available In this work satellite observations from the NASA's A-Train constellation were used to derive the values of primary emission and regional nucleation proxies over South Africa to estimate the potential for new particle formation. As derived in Kulmala et al. (2011, the satellite based proxies consist of source terms (NO2, SO2 and UV-B radiation, and a sink term describing the pre-existing aerosols. The first goal of this work was to study in detail the use of satellite aerosol optical depth (AOD as a substitute to the in situ based condensation sink (CS. One of the major factors affecting the agreement of CS and AOD was the elevated aerosol layers that increased the value of column integrated AOD but not affected the in situ CS. However, when the AOD in the proxy sink was replaced by an estimate from linear bivariate fit between AOD and CS, the agreement with the actual nucleation mode number concentration improved somewhat. The second goal of the work was to estimate how well the satellite based proxies can predict the potential for new particle formation. For each proxy the highest potential for new particle formation were observed over the Highveld industrial area, where the emissions were high but the sink due to pre-existing aerosols was relatively low. Best agreement between the satellite and in situ based proxies were obtained for NO2/AOD and UV-B/AOD2, whereas proxies including SO2 in the source term had lower correlation. Even though the OMI SO2 boundary layer product showed reasonable spatial pattern and detected the major sources over the study area, some of the known minor point sources were not detected. When defining the satellite proxies only for days when new particle formation event was observed, it was seen that for all the satellite based proxies the event day medians were higher than the entire measurement period median.

  6. Potential-field estimation from satellite data using scalar and vector Slepian functions

    CERN Document Server

    Plattner, Alain

    2013-01-01

    In the last few decades a series of increasingly sophisticated satellite missions has brought us gravity and magnetometry data of ever improving quality. To make optimal use of this rich source of information on the structure of Earth and other celestial bodies, our computational algorithms should be well matched to the specific properties of the data. In particular, inversion methods require specialized adaptation if the data are only locally available, their quality varies spatially, or if we are interested in model recovery only for a specific spatial region. Here, we present two approaches to estimate potential fields on a spherical Earth, from gradient data collected at satellite altitude. Our context is that of the estimation of the gravitational or magnetic potential from vector-valued measurements. Both of our approaches utilize spherical Slepian functions to produce an approximation of local data at satellite altitude, which is subsequently transformed to the Earth's spherical reference surface. The ...

  7. Densified GPS Estimates of Integrated Precipitable Water Vapor Improve Weather Forecasting during the North American Monsoon

    Science.gov (United States)

    Moore, A. W.; Small, I.; Gutman, S. I.; Bock, Y.; Dumas, J.; Haase, J. S.; Laber, J. L.

    2013-12-01

    Continuous GPS (CGPS) stations for observing crustal motion in the western U.S. now number more than 1200, with over 500 of them operating in real time. Tropospheric wet delay from real-time processing of the GPS data, along with co-located or nearby surface and temperature measurements, are being operationally converted to Integrated Precipitable Water Vapor (IPW) for evaluation as a forecasting tool (Gutman, 2011). The available density of real-time GPS in southern California now allows us to explore usage of densified GPS IPW in operational weather forecasting during weather conditions involving moisture extremes. Under a NASA Advanced Information Systems Technology (AIST) project, 27 southern California stations have been added to the NOAA GPS-Met observing network providing 30-minute estimates of IPW for ingestion into operational NOAA weather models, as well as for direct use by National Weather Service forecasters in monitoring developing weather conditions. The densified network proved advantageous in the 2013 North American Monsoon season, allowing forecasters to visualize rapid moisture increases at intervals between model runs and radiosonde observations and assisting in flood watch/warning decisions. We discuss the observed relationship between IPW and onset of precipitation in monsoon events in southern California and possibilities for additional decision support tools for forecasters.

  8. Assessing satellite AOD based and WRF/CMAQ output PM2.5 estimators

    Science.gov (United States)

    Cordero, Lina; Wu, Yonghua; Gross, Barry M.; Moshary, Fred

    2013-05-01

    Fine particulate matter measurements (PM2.5) are essential for air quality monitoring and related public health; however, the shortage of reliable measurmennts constrains researchers to use other means for obtaining reliable estimates over large scales. In particular, model forecasters and satellite community use their respective products to develop ground particulate matter estimations but few experiments have explored how the remote sensing approaches compare to the high resolution models. . In this paper we focus on studying the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellites (GOES) regression based estimates in comparison to more direct bias corrected outputs from the Community Multiscale Air Quality (CMAQ) model, We use a two-year dataset (2005-2006) and apply urban, season and hour filters to illustrate the agreement between estimated and in-situ measured fine particulate matter from the New York State Department of Environmental Conservation (NYSDEC). We first begin by analyzing the correspondence between ground aerosol optical depth (AOD) measurements from an AERONET (AErosol RObotic NETwork) Cimel sun/sky radiometer with both satellite and model products in one urban location; we show that satellite readings perform better than model outputs, especially during the summer (RMODIS>=0.65, RCMAQ>=0.37). This is a clear symptom of the difficulty in the models to properly model realistic optical properties. We then turn to a direct assessment of PM2.5 presenting individual comparisons between ground PM2.5 measurements with satellite/model predictions and demonstrate the higher accuracy from model estimations (RurbanMODIS >= 0.74, RurbanCMAQ >= 0.77; Rnon-urbanMODIS >= 0.48, Rnon-urbanCMAQ >= 0.78). In general, we find that the bias corrected CMAQ estimates are superior to satellite based estimators except at very high resolution. Finally, we show that when using both model and

  9. Satellite clock corrections estimation to accomplish real time ppp: experiments for brazilian real time network

    Science.gov (United States)

    Marques, Haroldo; Monico, João; Aquino, Marcio; Melo, Weyller

    2014-05-01

    The real time PPP method requires the availability of real time precise orbits and satellites clocks corrections. Currently, it is possible to apply the solutions of clocks and orbits available by BKG within the context of IGS Pilot project or by using the operational predicted IGU ephemeris. The accuracy of the satellite position available in the IGU is enough for several applications requiring good quality. However, the satellites clocks corrections do not provide enough accuracy (3 ns ~ 0.9 m) to accomplish real time PPP with the same level of accuracy. Therefore, for real time PPP application it is necessary to further research and develop appropriated methodologies for estimating the satellite clock corrections in real time with better accuracy. Currently, it is possible to apply the real time solutions of clocks and orbits available by Federal Agency for Cartography and Geodesy (BKG) within the context of IGS Pilot project. The BKG corrections are disseminated by a new proposed format of the RTCM 3.x and can be applied in the broadcasted orbits and clocks. Some investigations have been proposed for the estimation of the satellite clock corrections using GNSS code and phase observable at the double difference level between satellites and epochs (MERVAT, DOUSA, 2007). Another possibility consists of applying a Kalman Filter in the PPP network mode (HAUSCHILD, 2010) and it is also possible the integration of both methods, using network PPP and observables at double difference level in specific time intervals (ZHANG; LI; GUO, 2010). For this work the methodology adopted consists in the estimation of the satellite clock corrections based on the data adjustment in the PPP mode, but for a network of GNSS stations. The clock solution can be solved by using two types of observables: code smoothed by carrier phase or undifferenced code together with carrier phase. In the former, we estimate receiver clock error; satellite clock correction and troposphere, considering

  10. Estimation of clear-sky insolation using satellite and ground meteorological data

    Science.gov (United States)

    Staylor, W. F.; Darnell, W. L.; Gupta, S. K.

    1983-01-01

    Ground based pyranometer measurements were combined with meteorological data from the Tiros N satellite in order to estimate clear-sky insolations at five U.S. sites for five weeks during the spring of 1979. The estimates were used to develop a semi-empirical model of clear-sky insolation for the interpretation of input data from the Tiros Operational Vertical Sounder (TOVS). Using only satellite data, the estimated standard errors in the model were about 2 percent. The introduction of ground based data reduced errors to around 1 percent. It is shown that although the errors in the model were reduced by only 1 percent, TOVS data products are still adequate for estimating clear-sky insolation.

  11. 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.

  12. Water storage variations in the Poyang Lake Basin estimated from GRACE and satellite altimetry

    Institute of Scientific and Technical Information of China (English)

    Yang Zhou; Shuanggen Jin; Robert Tenzer; Jialiang Feng

    2016-01-01

    The Gravity Recovery and Climate Experiment (GRACE) satellite mission provides a unique opportunity to quantitatively study terrestrial water storage (TWS) variations. In this paper, the terrestrial water storage variations in the Poyang Lake Basin are recovered from the GRACE gravity data from January 2003 to March 2014 and compared with the Global Land Data Assimilation System (GLDAS) hydrological models and satellite altimetry. Further-more, the impact of soil moisture content from GLDAS and rainfall from the Tropical Rainfall Measuring Mission (TRMM) on TWS variations are investigated. Our results indi-cate that the TWS variations from GRACE, GLDAS and satellite altimetry have a general consistency. The TWS trends in the Poyang Lake Basin determined from GRACE, GLDAS and satellite altimetry are increasing at 0.0141 km3/a, 0.0328 km3/a and 0.0238 km3/a, respectively during the investigated time period. The TWS is governed mainly by the soil moisture content and dominated primarily by the precipitation but also modulated by the flood season of the Yangtze River as well as the lake and river exchange water.

  13. Estimation of land remote sensing satellites productivity based on the simulation technique

    Science.gov (United States)

    Kurenkov, Vladimir I.; Kucherov, Alexander S.; Yakischik, Artem A.

    2017-01-01

    The problem of estimating land remote sensing satellites productivity is considered. Here, productivity is treated as a number of separate survey objects taken in a definite time. Appropriate mathematical models have been developed. Some results obtained with the help of the software worked out in Delphi programming support environment are presented.

  14. Satellite estimates of wide-range suspended sediment concentrations in Changjiang (Yangtze) estuary using MERIS data

    NARCIS (Netherlands)

    Shen, F.; Verhoef, W.; Zhou, Y.; Salama, M.S.; Liu, X.

    2010-01-01

    The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l-1. Suspended sediment plays important roles in the estuarine and coastal system and environment. Previous algorithms for satellite estimates of sus

  15. Satellite remote sensing for estimating leaf area index, FPAR and primary production. A literature review

    Energy Technology Data Exchange (ETDEWEB)

    Boresjoe Bronge, Laine [SwedPower AB, Stockholm (Sweden)

    2004-03-01

    Land vegetation is a critical component of several biogeochemical cycles that have become the focus of concerted international research effort. Most ecosystem productivity models, carbon budget models, and global models of climate, hydrology and biogeochemistry require vegetation parameters to calculate land surface photosynthesis, evapotranspiration and net primary production. Therefore, accurate estimates of vegetation parameters are increasingly important in the carbon cycle, the energy balance and in environmental impact assessment studies. The possibility of quantitatively estimating vegetation parameters of importance in this context using satellite data has been explored by numerous papers dealing with the subject. This report gives a summary of the present status and applicability of satellite remote sensing for estimating vegetation productivity by using vegetation index for calculating leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR). Some possible approaches for use of satellite data for estimating LAI, FPAR and net primary production (NPP) on a local scale are suggested. Recommendations for continued work in the Forsmark and Oskarshamn investigation areas, where vegetation data and NDVI-images based on satellite data have been produced, are also given.

  16. An adaptive Multiplicative Extened Kalman Filter for Attitude Estimation of Marine Satellite Tracking Antenna

    DEFF Research Database (Denmark)

    Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar

    2016-01-01

    , an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...

  17. Stochastic estimation of dynamically changing object orientation parameters using satellite measurements

    OpenAIRE

    Lukasevich, V. I.; Kramarov, S. O.; Sokolov, Sergey V.

    2015-01-01

    It is solved a problem of a posteriori estimation of dynamically modified parameters of angular movement of the object by satellite measurements. There are shown advantages of application of the methods of stochastic non-linear dynamic filtration before single-stage measurements. It is represented an example, showing efficiency of proposed approach.

  18. Combining satellite altimetry and gravimetry data to improve Antarctic mass balance and gia estimates

    NARCIS (Netherlands)

    Gunter, B.C.; Didova, O.; Riva, R.E.M.; van den Broeke, M.R.; Ligtenberg, S.R.M.; Lenaerts, J.T.M.; King, M.; Urban, T.

    2012-01-01

    This study explores an approach that simultaneously estimates Antarctic mass balance and glacial isostatic adjustment (GIA) through the combination of satellite gravity and altimetry data sets. The results improve upon previous efforts by incorporating reprocessed data sets over a longer period of t

  19. Satellite estimates of wide-range suspended sediment concentrations in Changjiang (Yangtze) estuary using MERIS data

    NARCIS (Netherlands)

    Shen, F.; Verhoef, W.; Zhou, Y.; Salama, M.S.; Liu, X.

    2010-01-01

    The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l-1. Suspended sediment plays important roles in the estuarine and coastal system and environment. Previous algorithms for satellite estimates of

  20. Combining satellite altimetry and gravimetry data to improve Antarctic mass balance and gia estimates

    NARCIS (Netherlands)

    Gunter, B.C.; Didova, O.; Riva, R.E.M.; van den Broeke, M.R.; Ligtenberg, S.R.M.; Lenaerts, J.T.M.; King, M.; Urban, T.

    2012-01-01

    This study explores an approach that simultaneously estimates Antarctic mass balance and glacial isostatic adjustment (GIA) through the combination of satellite gravity and altimetry data sets. The results improve upon previous efforts by incorporating reprocessed data sets over a longer period of

  1. Assessment of Quantitative Precipitation Forecasts from Operational NWP Models (Invited)

    Science.gov (United States)

    Sapiano, M. R.

    2010-12-01

    Previous work has shown that satellite and numerical model estimates of precipitation have complimentary strengths, with satellites having greater skill at detecting convective precipitation events and model estimates having greater skill at detecting stratiform precipitation. This is due in part to the challenges associated with retrieving stratiform precipitation from satellites and the difficulty in resolving sub-grid scale processes in models. These complimentary strengths can be exploited to obtain new merged satellite/model datasets, and several such datasets have been constructed using reanalysis data. Whilst reanalysis data are stable in a climate sense, they also have relatively coarse resolution compared to the satellite estimates (many of which are now commonly available at quarter degree resolution) and they necessarily use fixed forecast systems that are not state-of-the-art. An alternative to reanalysis data is to use Operational Numerical Weather Prediction (NWP) model estimates, which routinely produce precipitation with higher resolution and using the most modern techniques. Such estimates have not been combined with satellite precipitation and their relative skill has not been sufficiently assessed beyond model validation. The aim of this work is to assess the information content of the models relative to satellite estimates with the goal of improving techniques for merging these data types. To that end, several operational NWP precipitation forecasts have been compared to satellite and in situ data and their relative skill in forecasting precipitation has been assessed. In particular, the relationship between precipitation forecast skill and other model variables will be explored to see if these other model variables can be used to estimate the skill of the model at a particular time. Such relationships would be provide a basis for determining weights and errors of any merged products.

  2. New service interface for River Forecasting Center derived quantitative precipitation estimates

    Science.gov (United States)

    Blodgett, David L.

    2013-01-01

    For more than a decade, the National Weather Service (NWS) River Forecast Centers (RFCs) have been estimating spatially distributed rainfall by applying quality-control procedures to radar-indicated rainfall estimates in the eastern United States and other best practices in the western United States to producea national Quantitative Precipitation Estimate (QPE) (National Weather Service, 2013). The availability of archives of QPE information for analytical purposes has been limited to manual requests for access to raw binary file formats that are difficult for scientists who are not in the climatic sciences to work with. The NWS provided the QPE archives to the U.S. Geological Survey (USGS), and the contents of the real-time feed from the RFCs are being saved by the USGS for incorporation into the archives. The USGS has applied time-series aggregation and added latitude-longitude coordinate variables to publish the RFC QPE data. Web services provide users with direct (index-based) data access, rendered visualizations of the data, and resampled raster representations of the source data in common geographic information formats.

  3. Retrospective Analog Year Analyses Using NASA Satellite Data to Improve USDA's World Agricultural Supply and Demand Estimates

    Science.gov (United States)

    Teng, William; Shannon, Harlan

    2011-01-01

    The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted, including maps, charts, and time series of recent weather, climate, and crop observations; numerical output from weather and crop models; and reports from the press, USDA attach s, and foreign governments. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. Because both the amount and timing of precipitation significantly affect crop yields, WAOB often uses precipitation time series to identify growing seasons with similar weather patterns and help estimate crop yields for the current growing season, based on observed yields in analog years. Historically, these analog years are visually identified; however, the qualitative nature of this method sometimes precludes the definitive identification of the best analog year. Thus, one goal of this study is to derive a more rigorous, statistical approach for identifying analog years, based on a modified coefficient of determination, termed the analog index (AI). A second goal is to compare the performance of AI for time series derived from surface-based observations vs. satellite-based measurements (NASA TRMM and other data).

  4. Quantifying uncertainty in modelled estimates of annual maximum precipitation: confidence intervals

    Science.gov (United States)

    Panagoulia, Dionysia; Economou, Polychronis; Caroni, Chrys

    2016-04-01

    The possible nonstationarity of the GEV distribution fitted to annual maximum precipitation under climate change is a topic of active investigation. Of particular significance is how best to construct confidence intervals for items of interest arising from stationary/nonstationary GEV models.We are usually not only interested in parameter estimates but also in quantiles of the GEV distribution and it might be expected that estimates of extreme upper quantiles are far from being normally distributed even for moderate sample sizes.Therefore, we consider constructing confidence intervals for all quantities of interest by bootstrap methods based on resampling techniques. To this end, we examined three bootstrapping approaches to constructing confidence intervals for parameters and quantiles: random-t resampling, fixed-t resampling and the parametric bootstrap. Each approach was used in combination with the normal approximation method, percentile method, basic bootstrap method and bias-corrected method for constructing confidence intervals. We found that all the confidence intervals for the stationary model parameters have similar coverage and mean length. Confidence intervals for the more extreme quantiles tend to become very wide for all bootstrap methods. For nonstationary GEV models with linear time dependence of location or log-linear time dependence of scale, confidence interval coverage probabilities are reasonably accurate for the parameters. For the extreme percentiles, the bias-corrected and accelerated method is best overall, and the fixed-t method also has good average coverage probabilities. Reference: Panagoulia D., Economou P. and Caroni C., Stationary and non-stationary GEV modeling of extreme precipitation over a mountainous area under climate change, Environmetrics, 25 (1), 29-43, 2014.

  5. Robust double gain unscented Kalman filter for small satellite attitude estimation

    Science.gov (United States)

    Cao, Lu; Yang, Weiwei; Li, Hengnian; Zhang, Zhidong; Shi, Jianjun

    2017-08-01

    Limited by the low precision of small satellite sensors, the estimation theories with high performance remains the most popular research topic for the attitude estimation. The Kalman filter (KF) and its extensions have been widely applied in the satellite attitude estimation and achieved plenty of achievements. However, most of the existing methods just take use of the current time-step's priori measurement residuals to complete the measurement update and state estimation, which always ignores the extraction and utilization of the previous time-step's posteriori measurement residuals. In addition, the uncertainty model errors always exist in the attitude dynamic system, which also put forward the higher performance requirements for the classical KF in attitude estimation problem. Therefore, the novel robust double gain unscented Kalman filter (RDG-UKF) is presented in this paper to satisfy the above requirements for the small satellite attitude estimation with the low precision sensors. It is assumed that the system state estimation errors can be exhibited in the measurement residual; therefore, the new method is to derive the second Kalman gain Kk2 for making full use of the previous time-step's measurement residual to improve the utilization efficiency of the measurement data. Moreover, the sequence orthogonal principle and unscented transform (UT) strategy are introduced to robust and enhance the performance of the novel Kalman Filter in order to reduce the influence of existing uncertainty model errors. Numerical simulations show that the proposed RDG-UKF is more effective and robustness in dealing with the model errors and low precision sensors for the attitude estimation of small satellite by comparing with the classical unscented Kalman Filter (UKF).

  6. Effect of satellite formations and imaging modes on global albedo estimation

    Science.gov (United States)

    Nag, Sreeja; Gatebe, Charles K.; Miller, David W.; de Weck, Olivier L.

    2016-05-01

    We confirm the applicability of using small satellite formation flight for multi-angular earth observation to retrieve global, narrow band, narrow field-of-view albedo. The value of formation flight is assessed using a coupled systems engineering and science evaluation model, driven by Model Based Systems Engineering and Observing System Simulation Experiments. Albedo errors are calculated against bi-directional reflectance data obtained from NASA airborne campaigns made by the Cloud Absorption Radiometer for the seven major surface types, binned using MODIS' land cover map - water, forest, cropland, grassland, snow, desert and cities. A full tradespace of architectures with three to eight satellites, maintainable orbits and imaging modes (collective payload pointing strategies) are assessed. For an arbitrary 4-sat formation, changing the reference, nadir-pointing satellite dynamically reduces the average albedo error to 0.003, from 0.006 found in the static referencecase. Tracking pre-selected waypoints with all the satellites reduces the average error further to 0.001, allows better polar imaging and continued operations even with a broken formation. An albedo error of 0.001 translates to 1.36 W/m2 or 0.4% in Earth's outgoing radiation error. Estimation errors are found to be independent of the satellites' altitude and inclination, if the nadir-looking is changed dynamically. The formation satellites are restricted to differ in only right ascension of planes and mean anomalies within slotted bounds. Three satellites in some specific formations show average albedo errors of less than 2% with respect to airborne, ground data and seven satellites in any slotted formation outperform the monolithic error of 3.6%. In fact, the maximum possible albedo error, purely based on angular sampling, of 12% for monoliths is outperformed by a five-satellite formation in any slotted arrangement and an eight satellite formation can bring that error down four fold to 3%. More than

  7. 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.

  8. Overview of the relativistic electron precipitations (REP) observed on LEO satellites and ISS by Bulgarian build instruments

    Science.gov (United States)

    Dachev, Tsvetan

    Relativistic electron precipitation (REP) are observed by the R3D B2/B3 and RD3-B3 instruments during the flights of the Foton M2/M3 and “BION-M” № 1 satellite in 2005, 2007 and 2013, and by the R3DE/R instruments at the EXPOSE-E facility of the European Columbus module and at the EXPOSE-R facility of the Russian Zvezda module of the International Space Station (ISS) in the period from February 2008 till August 2010. The obtained dose rates strongly depend by the external and internal shielding of the detectors in the instruments. The highest dose rate reaching more than 20 mGy h (-1) was observed outside the ISS Zvezda module during the REP in April 2010 being the second largest in GOES history with a >2 MeV electron fluence event. REP doses behind relatively thick shielding are too small but may play considerable role during extra vehicular activity (EVA) when the cosmonauts/astronauts body is shielded only by the space suit.

  9. Relevance of the correlation between precipitation and the 0 °C isothermal altitude for extreme flood estimation

    Science.gov (United States)

    Zeimetz, Fraenz; Schaefli, Bettina; Artigue, Guillaume; García Hernández, Javier; Schleiss, Anton J.

    2017-08-01

    Extreme floods are commonly estimated with the help of design storms and hydrological models. In this paper, we propose a new method to take into account the relationship between precipitation intensity (P) and air temperature (T) to account for potential snow accumulation and melt processes during the elaboration of design storms. The proposed method is based on a detailed analysis of this P-T relationship in the Swiss Alps. The region, no upper precipitation intensity limit is detectable for increasing temperature. However, a relationship between the highest measured temperature before a precipitation event and the duration of the subsequent event could be identified. An explanation for this relationship is proposed here based on the temperature gradient measured before the precipitation events. The relevance of these results is discussed for an example of Probable Maximum Precipitation-Probable Maximum Flood (PMP-PMF) estimation for the high mountainous Mattmark dam catchment in the Swiss Alps. The proposed method to associate a critical air temperature to a PMP is easily transposable to similar alpine settings where meteorological soundings as well as ground temperature and precipitation measurements are available. In the future, the analyses presented here might be further refined by distinguishing between precipitation event types (frontal versus orographic).

  10. Assessing the role of uncertain precipitation estimates on the robustness of hydrological model parameters under highly variable climate conditions

    Directory of Open Access Journals (Sweden)

    B. Bisselink

    2016-12-01

    New hydrological insights: Results indicate large discrepancies in terms of the linear correlation (r, bias (β and variability (γ between the observed and simulated streamflows when using different precipitation estimates as model input. The best model performance was obtained with products which ingest gauge data for bias correction. However, catchment behavior was difficult to be captured using a single parameter set and to obtain a single robust parameter set for each catchment, which indicate that transposing model parameters should be carried out with caution. Model parameters depend on the precipitation characteristics of the calibration period and should therefore only be used in target periods with similar precipitation characteristics (wet/dry.

  11. 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

  12. Numerical Research on Effects Upon Precipitation Forecast of Doppler-Radar Estimated Precipitation and Retrieved Wind Field Under Different Model Initial Schemes

    Institute of Scientific and Technical Information of China (English)

    WANG Yehong; ZHAO Yuchun; CUI Chunguang

    2007-01-01

    On the basis of the joint estimated 1-h precipitation from Changde, Jingzhou, and Yichang Doppler radars as well as Wuhan digital radar, and the retrieved wind fields from Yichang and Jingzhou Doppler radars, a series of numerical experiments with an advanced regional η-coordinate model (AREM) under different model initial schemes, i.e., Grapes-3DVAR, Barnes objective analysis, and Barnes-3DVAR, are carried out for a torrential rain process occurring along the Yangtze River in the 24-h period from 2000 BT 22 July 2002 to investigate the effects of the Doppler-radar estimated rainfall and retrieved winds on the rainfall forecast. The main results are as follows: (1) The simulations are obviously different under three initial schemes with the same data source (the radiosounding and T213L31 analysis). On the whole,Barnes-3DVAR, which combines the advantages of the Barnes objective analysis and the Grapes-3DVAR method, gives the best simulations: well-simulated rain band and clear mesoscale structures, as well as their location and intensity close to observations. (2) Both Barnes-3DVAR and Grapes-3DVAR schemes are able to assimilate the Doppler-radar estimated rainfall and retrieved winds, but differences in simulation results are very large, with Barnes-3DVAR's simulation much better than Grapes-3DVAR's. (3) Under Grapes3DVAR scheme, the simulation of 24-h rainfall is improved obviously when assimilating the Doppler-radar estimated precipitation into the model in compared with the control experiment; but it becomes a little worse when assimilating the Doppler-radar retrieved winds into the model, and it becomes worse obviously when assimilating the Doppler-radar estimated precipitation as well as retrieved winds into the model. However,the simulation is different under Barnes-3DVAR scheme. The simulation is improved to a certain degree no matter assimilating the estimated precipitation or retrieved winds, or both of them. The result is the best when assimilating both

  13. A Nano-satellite Mission to Study Charged Particle Precipitation from the Van Allen Radiation Belts caused due to Seismo-Electromagnetic Emissions

    CERN Document Server

    Sivadas, Nithin; Kannapan, Deepti; Yalamarthy, Ananth Saran; Dhiman, Ankit; Bhagoji, Arjun; Shankar, Athreya; Prasad, Nitin; Ramachandran, Harishankar; Koilpillai, R David

    2014-01-01

    In the past decade, several attempts have been made to study the effects of seismo-electromagnetic emissions - an earthquake precursor, on the ionosphere and the radiation belts. The IIT Madras nano-satellite (IITMSAT) mission is designed to make sensitive measurements of charged particle fluxes in a Low Earth Orbit to study the nature of charged particle precipitation from the Van Allen radiation belts caused due to such emissions. With the Space-based Proton Electron Energy Detector on-board a single nano-satellite, the mission will attempt to gather statistically significant data to verify possible correlations with seismo-electromagnetic emissions before major earthquakes.

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

    Energy Technology Data Exchange (ETDEWEB)

    Sengupta, M.; Gotseff, P.

    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. Modifications of the heliostat procedures for irradiance estimates from satellite images

    Energy Technology Data Exchange (ETDEWEB)

    Beyer, H.G.; Costanzo, Claudio; Heinemann, Detlev [Oldenburg Univ. (Germany). Fachbereich 8 - Physik

    1996-03-01

    Images taken by geostationary satellites may be used to estimate solar irradiance fluxes at the earth`s surface. The Heliostat method is a widely applied procedure for this task. It is based on the empirical correlation between a satellite derived cloud index and the irradiance at the ground. Modifications to this procedure that may reduce the temporal variability of the correlation are presented. The modified method may open the way to the use of a generic relation of cloud index and global irradiance. (author)

  16. Reusable Reentry Satellite (RRS) system design study: System cost estimates document

    Science.gov (United States)

    1991-01-01

    The Reusable Reentry Satellite (RRS) program was initiated to provide life science investigators relatively inexpensive, frequent access to space for extended periods of time with eventual satellite recovery on earth. The RRS will provide an on-orbit laboratory for research on biological and material processes, be launched from a number of expendable launch vehicles, and operate in Low-Altitude Earth Orbit (LEO) as a free-flying unmanned laboratory. SAIC's design will provide independent atmospheric reentry and soft landing in the continental U.S., orbit for a maximum of 60 days, and will sustain three flights per year for 10 years. The Reusable Reentry Vehicle (RRV) will be 3-axis stabilized with artificial gravity up to 1.5g's, be rugged and easily maintainable, and have a modular design to accommodate a satellite bus and separate modular payloads (e.g., rodent module, general biological module, ESA microgravity botany facility, general botany module). The purpose of this System Cost Estimate Document is to provide a Life Cycle Cost Estimate (LCCE) for a NASA RRS Program using SAIC's RRS design. The estimate includes development, procurement, and 10 years of operations and support (O&S) costs for NASA's RRS program. The estimate does not include costs for other agencies which may track or interface with the RRS program (e.g., Air Force tracking agencies or individual RRS experimenters involved with special payload modules (PM's)). The life cycle cost estimate extends over the 10 year operation and support period FY99-2008.

  17. Estimation of volcanic ash refractive index from satellite infrared sounder data

    Science.gov (United States)

    Ishimoto, H.; Masuda, K.

    2014-12-01

    The properties of volcanic ash clouds (cloud height, optical depth, and effective radius of the particles) are planned to estimate from the data of the next Japanese geostationary meteorological satellite, Himawari 8/9. The volcanic ash algorithms, such as those proposed by NOAA/NESDIS and by EUMETSAT, are based on the infrared absorption properties of the ash particles, and the refractive index of a typical volcanic rock (i.e. andesite) has been used in the forward radiative transfer calculations. Because of a variety of the absorption properties for real volcanic ash particles at infrared wavelengths (9-13 micron), a large retrieval error may occur if the refractive index of the observed ash particles was different from that assumed in the retrieval algorithm. Satellite infrared sounder provides spectral information for the volcanic ash clouds. If we can estimate the refractive index of the ash particles from the infrared sounder data, a dataset of the optical properties for similar rock type of the volcanic ash can be prepared for the ash retrieval algorithms of geostationary/polar-orbiting satellites in advance. Furthermore, the estimated refractive index can be used for a diagnostic and a correction of the ash particle model in the retrieval algorithm within a period of the volcanic activities. In this work, optimal estimation of the volcanic ash parameters was conducted through the radiative transfer calculations for the window channels of the atmospheric infrared sounder (AIRS). The estimated refractive indices are proposed for the volcanic ash particles of some eruption events.

  18. A probabilistic approach for estimating monthly catchment water balances from satellite and ground data

    Science.gov (United States)

    Schoups, Gerrit

    2017-04-01

    A probabilistic model is developed to estimate monthly basin-scale precipitation, evaporation, storage and river discharge from open-source data and water balance constraints. Both random and systematic deviations between observed and "true" water balance components are included in the model to account for measurement/processing errors and differences in scale. Model parameters comprise data standard deviations (random noise) and scaling factors (systematic bias). Water balance terms and parameters are estimated using Bayesian inference, yielding posterior distributions for all unknowns. The model is applied to MOPEX basins across the continental US using the following data sources: TRMM-3B43 (precipitation), SSEBop (evaporation), GRACE (storage), and USGS stream gauges (river discharge). Results provide optimal estimates and uncertainty of water balance components and data errors across a range of basin characteristics (size, wetness, etc).

  19. New estimates of tropical temperature and precipitation changes during the last 42ka

    Science.gov (United States)

    Grauel, A.; Hodell, D. A.; Bernasconi, S. M.; Correa-Metrio, A.

    2013-12-01

    The amount of cooling in the tropics during the last Ice Age has been a longstanding problem with large discrepancies between terrestrial and marine estimates. Here we present a reconstruction of temperature and precipitation changes over the last 42ka from a lake sediment core from Lake Petén Itzá, Guatemala, located at 17°N in lowland Central America. Previous studies of sediment cores from Lake Petén Itzá showed that alternating layers of clay- and gypsum-rich sediment reflect times of wetter and dryer conditions, respectively. The most arid conditions coincide with stadials, especially those associated with Heinrich events (HEs) when pollen assemblages are dominated by xeric-tolerant taxa. In contrast, interstadials and the last glacial maximum (LGM) are characterized by clay deposition and pollen indicative of temperate pine-oak forest, indicating more humid conditions in the lowland Neotropics. We compared three independent methods to reconstruct glacial temperatures: tandem measurements of δ18O in biogenic carbonate and gypsum hydration water, clumped isotope thermometry, and pollen-based temperature estimates using the Modern Analog Technique (MAT). The temperatures derived by the three methods generally agree during interstadials and some stadials (e.g., HE2 and 3), but diverge during other stadial events (e.g., HE1 and 4). For example, gypsum hydration and clumped isotope methods indicate a severe cooling of 6 to 10°C during HE1 and 4, whereas the pollen MAT suggests more moderate cooling of 3 to 6 °C. The reason for this divergence is likely that no modern analogs exist for the pollen assemblage during these cold, arid stadials when the MAT is not applicable. Although the temperature decrease is similar (6-10°C) for HE1 and 4, deuterium excess is distinctly different (-19 and -14, respectively), perhaps indicating a change in source and/or seasonality of precipitation. The δ18O and δD of the lake water indicate HE1 was the most arid

  20. The quantitative precipitation estimation system for Dallas-Fort Worth (DFW) urban remote sensing network

    Science.gov (United States)

    Chen, Haonan; Chandrasekar, V.

    2015-12-01

    The Dallas-Fort Worth (DFW) urban radar network consists of a combination of high resolution X band radars and a standard National Weather Service (NWS) Next-Generation Radar (NEXRAD) system operating at S band frequency. High spatiotemporal-resolution quantitative precipitation estimation (QPE) is one of the important applications of such a network. This paper presents a real-time QPE system developed by the Collaborative Adaptive Sensing of the Atmosphere (CASA) Engineering Research Center for the DFW urban region using both the high resolution X band radar network and the NWS S band radar observations. The specific dual-polarization radar rainfall algorithms at different frequencies (i.e., S- and X-band) and the fusion methodology combining observations at different temporal resolution are described. Radar and rain gauge observations from four rainfall events in 2013 that are characterized by different meteorological phenomena are used to compare the rainfall estimation products of the CASA DFW QPE system to conventional radar products from the national radar network provided by NWS. This high-resolution QPE system is used for urban flash flood mitigations when coupled with hydrological models.

  1. Precipitable Water Vapor Estimates in the Australian Region from Ground-Based GPS Observations

    Directory of Open Access Journals (Sweden)

    Suelynn Choy

    2015-01-01

    Full Text Available We present a comparison of atmospheric precipitable water vapor (PWV derived from ground-based global positioning system (GPS receiver with traditional radiosonde measurement and very long baseline interferometry (VLBI technique for a five-year period (2008–2012 using Australian GPS stations. These stations were selectively chosen to provide a representative regional distribution of sites while ensuring conventional meteorological observations were available. Good agreement of PWV estimates was found between GPS and VLBI comparison with a mean difference of less than 1 mm and standard deviation of 3.5 mm and a mean difference and standard deviation of 0.1 mm and 4.0 mm, respectively, between GPS and radiosonde measurements. Systematic errors have also been discovered during the course of this study, which highlights the benefit of using GPS as a supplementary atmospheric PWV sensor and calibration system. The selected eight GPS sites sample different climates across Australia covering an area of approximately 30° NS/EW. It has also shown that the magnitude and variation of PWV estimates depend on the amount of moisture in the atmosphere, which is a function of season, topography, and other regional climate conditions.

  2. Evaluation of radar-gauge merging methods for quantitative precipitation estimates

    Directory of Open Access Journals (Sweden)

    E. Goudenhoofdt

    2008-10-01

    Full Text Available Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to retrieve daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 3-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical methods give the best results for all network densities except for a very low density of 1 gauge per 500 km2 where a range-dependent adjustment complemented with a static local bias correction performs best.

  3. Observational estimates of detrainment and entrainment in non-precipitating shallow cumulus

    Science.gov (United States)

    Norgren, M. S.; Small, J. D.; Jonsson, H. H.; Chuang, P. Y.

    2016-01-01

    Vertical transport associated with cumulus clouds is important to the redistribution of gases, particles, and energy, with subsequent consequences for many aspects of the climate system. Previous studies have suggested that detrainment from clouds can be comparable to the updraft mass flux, and thus represents an important contribution to vertical transport. In this study, we describe a new method to deduce the amounts of gross detrainment and entrainment experienced by non-precipitating cumulus clouds using aircraft observations. The method utilizes equations for three conserved variables: cloud mass, total water, and moist static energy. Optimizing these three equations leads to estimates of the mass fractions of adiabatic mixed-layer air, entrained air and detrained air that the sampled cloud has experienced. The method is applied to six flights of the CIRPAS Twin Otter during the Gulf of Mexico Atmospheric Composition and Climate Study (GoMACCS) which took place in the Houston, Texas region during the summer of 2006 during which 176 small, non-precipitating cumuli were sampled. Using our novel method, we find that, on average, these clouds were comprised of 30 to 70 % mixed-layer air, with entrained air comprising most of the remainder. The mass fraction of detrained air was usually very small, less than 2 %, although values larger than 10 % were found in 15 % of clouds. Entrained and detrained air mass fractions both increased with altitude, consistent with some previous observational studies. The largest detrainment events were almost all associated with air that was at their level of neutral buoyancy, which has been hypothesized in previous modeling studies. This new method could be readily used with data from other previous aircraft campaigns to expand our understanding of detrainment for a variety of cloud systems.

  4. Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate

    Science.gov (United States)

    Bai, Yun; Zhang, Jiahua; Zhang, Sha; Koju, Upama Ashish; Yao, Fengmei; Igbawua, Tertsea

    2017-03-01

    Recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2=0.60 (RMSE = 18.72 W m-2) for all 27 sites and R2=0.62 (RMSE = 18.21 W m-2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 (RMSE = 20.74 W m-2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.

  5. Study and Tests of Improved Rain Estimates from the TRMM Precipitation Radar.

    Science.gov (United States)

    Ferreira, Franck; Amayenc, Paul; Oury, Stéphane; Testud, Jacques

    2001-11-01

    Rain rate R estimation from the 2A-25 profiling algorithm of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) is analyzed in two ways. Standard results from the operating version-5 algorithm are compared with those from the previous version 4. Also, various adjustments of the involved rain relationships in version 4 are explored, which leads to the proposal of two alternatives to the standard rain rate (Rstd-V4). The first one, (RN0), is based on N(0-scaled relations exploiting the concept of normalized -shaped drop size distributions; the second one, (RkR), relies on using constant R-k instead of constant R-Z relation as in the standard, where Z is reflectivity and k is attenuation coefficient. Error analysis points out a lower sensitivity of the alternative estimates to errors in radar calibration, or initial relations, than the standard. Results from a set of PR data, over ocean and land, show that the version-4 alternatives, and version-5 standard (Rstd-V5), produce more rain than the version-4 standard, which may correct for some reported underestimation. These approaches are tested via point-to-point comparisons of 3D PR-derived Z and R fields (versions 4 and 5) with `reference' fields derived from airborne dual-beam radar on board a National Oceanic and Atmospheric Administration P3-42 aircraft in Hurricanes Bonnie and Brett, for good cases of TRMM overpasses over the ocean. In the comparison domains, Bonnie is dominated by stratiform rain, and Brett includes convective and stratiform rain. In stratiform rain, the mean difference in Z, accounting for different frequencies and scanning geometries of both radars, lies within the uncertainty margin of residual errors in the radar calibrations. Also, the PR mean rain-rate estimates, RkR and Rstd-V5, agree fairly well with the P3 estimate, RP3, whereas Rstd-V4 and RN0 respectively underestimate and overestimate RP3. In convective rain (Brett case), the PR estimates of Z and R largely exceed

  6. ROBUST AND FAST FREQUENCY OFFSET ESTIMATION FOR OFDM BASED SATELLITE COMMUNICATION

    Institute of Scientific and Technical Information of China (English)

    Wei Li; Xu Youyun; Cai Yueming

    2009-01-01

    A pilot-aided Orthogonal Frequency Division Multiplexing (OFDM) frequency offset estimator designed for satellite communication system is proposed in the paper. The estimator focuses on the acquisition of the integer part of carrier frequency offset and the acquisition range is as large as the whole signal bandwidth. Making full use of the phase difference between received pilot and local referential pilot, a fast estimation is obtained. Compared with existing method, our method can also work well even in SNR as low as 0dB. Simulations verify the good performance of our method.

  7. Multi-satellite sensor study on precipitation-induced emission pulses of NOx from soils in semi-arid ecosystems

    Science.gov (United States)

    Zörner, Jan; Penning de Vries, Marloes; Beirle, Steffen; Sihler, Holger; Veres, Patrick R.; Williams, Jonathan; Wagner, Thomas

    2016-07-01

    We present a top-down approach to infer and quantify rain-induced emission pulses of NOx ( ≡ NO + NO2), stemming from biotic emissions of NO from soils, from satellite-borne measurements of NO2. This is achieved by synchronizing time series at single grid pixels according to the first day of rain after a dry spell of prescribed duration. The full track of the temporal evolution several weeks before and after a rain pulse is retained with daily resolution. These are needed for a sophisticated background correction, which accounts for seasonal variations in the time series and allows for improved quantification of rain-induced soil emissions. The method is applied globally and provides constraints on pulsed soil emissions of NOx in regions where the NOx budget is seasonally dominated by soil emissions. We find strong peaks of enhanced NO2 vertical column densities (VCDs) induced by the first intense precipitation after prolonged droughts in many semi-arid regions of the world, in particular in the Sahel. Detailed investigations show that the rain-induced NO2 pulse detected by the OMI (Ozone Monitoring Instrument), GOME-2 and SCIAMACHY satellite instruments could not be explained by other sources, such as biomass burning or lightning, or by retrieval artefacts (e.g. due to clouds). For the Sahel region, absolute enhancements of the NO2 VCDs on the first day of rain based on OMI measurements 2007-2010 are on average 4 × 1014  molec cm-2 and exceed 1 × 1015  molec cm-2 for individual grid cells. Assuming a NOx lifetime of 4 h, this corresponds to soil NOx emissions in the range of 6 up to 65 ng N m-2 s-1, which is in good agreement with literature values. Apart from the clear first-day peak, NO2 VCDs are moderately enhanced (2 × 1014  molec cm-2) compared to the background over the following 2 weeks, suggesting potential further emissions during that period of about 3.3 ng N m-2 s-1. The pulsed emissions contribute about 21-44 % to total

  8. Estimation of Phase Delay due to Precipitable Water for Dinsarbased Land Deformation Monitoring

    Science.gov (United States)

    Susaki, J.; Maeda, N.; Akatsuka, S.

    2017-09-01

    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.

  9. 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.

  10. Estimation of Drainage and Evapotranspiration from Time Series of Soil Moisture, Potential Evaporation, and Precipitation

    Science.gov (United States)

    Salvucci, G. D.; Gioioso, M.

    2003-12-01

    A previous study demonstrated that the dependence of soil water outflow on soil moisture can be estimated by averaging precipitation conditioned on soil moisture. The methodology is non parametric and relies only on the assumed stationarity of the soil moisture time series. Here we present a method for partitioning out the evapotranspiration component of total outflow. One goal is to structure the model with as few assumptions about model form as possible. for example we set evapotranspiration efficiency to increases monotonically with moisture and to be concave down, while the net drainage (capillary rise to or percolation from the root zone) is made to depend on moisture in a concave upward fashion. The functions used to represent these behavior are piecewise continuous polynomials or line segments. After generating a set of feasible partitions using a linear programming technique, we evaluate the relative likelihood of each by estimating the entropy of the time series of soil water storage that results from integrating the fluxes. We show that the entropy of the series is proportional to the likelihood that the increments that make it up come from a stationary process, and use this as a basis for model selection. We also estimate the growth of variance of the time series, and decompose this into an equilibrium process (that saturates with time due to a negative correlation among increments) and an error process which (for white noise model, measurement and sampling errors) leads to a random walk term. A unique feature of the method is that it does not fit model predictions to soil moisture, but instead evaluates the stationarity of the running series of soil water storage values implied by the partitioning. Because of this feature the method can be driven with indices of soil moisture (like brightness temperatures) rather than site-specific water contents.

  11. Combining Satellite and Ground Magnetic Measurements to Improve Estimates of Electromagnetic Induction Transfer Functions

    Science.gov (United States)

    Balasis, G.; Egbert, G. D.

    2005-12-01

    Electromagnetic (EM) induction studies using satellite and ground-based magnetic data may ultimately provide critical new constraints on the electrical conductivity of Earth's mantle. Unlike ground-based observatories, which leave large areas of the Earth (especially the ocean basins) unsampled, satellites have the potential for nearly complete global coverage. However, because the number of operating satellites is limited, spatially complex (especially non-zonal) external current sources are sampled relatively poorly by satellites at any fixed time. The comparatively much larger number of ground-based observatories provides more complete synoptic sampling of external source structure. By combining data from both satellites and observatories models of external sources can be improved, leading to more reliable global mapping of Earth conductivity. For example, estimates of EM induction transfer functions estimated from night-side CHAMP data have been previously shown to have biases which depend systematically on local time (LT). This pattern of biases suggests that a purely zonal model does not adequately describe magnetospheric sources. As a first step toward improved modeling of spatial complexity in sources, we have applied empirical orthogonal function (EOF) methods to exploratory analysis of night-side observatory data. After subtraction of the predictions of the CM4 comprehensive model, which includes a zonally symmetric storm-time correction based on Dst, we find significant non-axisymmetric, but large scale coherent variability in the mid-latitude night-side observatory residuals. Over the restricted range of local times (18:00-6:00) and latitudes (50°S to 50°N) considered, the dominant spatial mode of variability is reasonably approximated by a q21 quadrupole spherical harmonic. Temporal variability of this leading EOF mode is well correlated with Dst. Strategies for moving beyond this initial exploratory EOF analysis to combine observatory data with

  12. Multi-technique combination of space geodesy observations: Impact of the Jason-2 satellite on the GPS satellite orbits estimation

    Science.gov (United States)

    Zoulida, Myriam; Pollet, Arnaud; Coulot, David; Perosanz, Félix; Loyer, Sylvain; Biancale, Richard; Rebischung, Paul

    2016-10-01

    In order to improve the Precise Orbit Determination (POD) of the GPS constellation and the Jason-2 Low Earth Orbiter (LEO), we carry out a simultaneous estimation of GPS satellite orbits along with Jason-2 orbits, using GINS software. Along with GPS station observations, we use Jason-2 GPS, SLR and DORIS observations, over a data span of 6 months (28/05/2011-03/12/2011). We use the Geophysical Data Records-D (GDR-D) orbit estimation standards for the Jason-2 satellite. A GPS-only solution is computed as well, where only the GPS station observations are used. It appears that adding the LEO GPS observations results in an increase of about 0.7% of ambiguities fixed, with respect to the GPS-only solution. The resulting GPS orbits from both solutions are of equivalent quality, agreeing with each other at about 7 mm on Root Mean Square (RMS). Comparisons of the resulting GPS orbits to the International GNSS Service (IGS) final orbits show the same level of agreement for both the GPS-only orbits, at 1.38 cm in RMS, and the GPS + Jason2 orbits at 1.33 cm in RMS. We also compare the resulting Jason-2 orbits with the 3-technique Segment Sol multi-missions d'ALTimétrie, d'orbitographie et de localisation précise (SSALTO) POD products. The orbits show good agreement, with 2.02 cm of orbit differences global RMS, and 0.98 cm of orbit differences RMS on the radial component.

  13. A Comparison of Satellite and Emnpirical Formula Techniques for Estimating Insolation over the Oceans.

    Science.gov (United States)

    Frouin, Robert; Gautier, Catherine; Katsaros, Kristina B.; Lind, Richard J.

    1988-09-01

    Surface insulation data collected during the Mixed Layer Dynamiccs Experiment are used to intercompare the satellite technique of Gautier et al. (1980) and five commonly referenced empirical formulas for estimating daily insulation over the oceans. The results demonstrate the superiority of the satellite technique, which exhibits a 0.97 correlation coefficient, a 12.0 W m M2 error of estimate, and a 4.9 W m2 bias error, and which is also able to account for water vapor, ozone, and dust amount variations in the atmosphere and monitor quasi-instantaneously vast extents of ocean. Among the empirical formulas, Mosby's (1936) yields the best predictions with a 0.84 correlation coefficient, a 19.1 W m2 standard error of estimate, and a 3.4 W m2 bias. Kimball'(1928) and Reed's (1977) formulas however, perform nearly as well. The largest biases are obtained with Berliand's (1960) and Laevastu' (1960) formulas, which overestimate insolation by 15.2 and 24.5 W m2, respectively. It is suggested the empirical formulas, even though established from visual cloud cover observations, would provide useful insolation estimates if employed with satellite-derived cloud cover.

  14. Estimation of Thermodynamic and Dynamic Contribution on Regional Precipitation Intensity and Frequency Changes under Global Warming

    Science.gov (United States)

    Chen, C.-A.; Chou, C.; Chen, C.-T.

    2012-04-01

    From global point of view, an increased tendency of mean precipitation, which is associated with a shift toward more intense and extreme precipitation, has been found in observations and global warming simulations. However, changes in regional precipitation might be different due to contributions of thermodynamic and dynamic components. It implies that changes in regional rainfall intensity and frequency, which is connected to regional mean precipitation changes, should be more complicated under global warming. To understand how regional intensity and frequency will change under global warming, the global warming simulations from the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset in the A1B scenario were examined in this study. Over regions with increased mean precipitation, positive precipitation anomaly is usually contributed by more frequent heavy rain and enhanced rainfall intensity, even though there are less light rain events in the future. On the other hand, over regions with decreased mean precipitation, negative precipitation anomaly is associated with decreases in frequency for almost every rain events and weakened rainfall intensity, even though there are more very heavy and light rain events. The thermodynamic component is uniform in different regions, and tends to enhance precipitation frequency and intensity, while the dynamic component varies with regions, and can either enhance or reduce precipitation frequency and intensity.

  15. The Role of Satellite Imagery to Improve Pastureland Estimates in South America

    Science.gov (United States)

    Graesser, J.

    2015-12-01

    Agriculture has changed substantially across the globe over the past half century. While much work has been done to improve spatial-temporal estimates of agricultural changes, we still know more about the extent of row-crop agriculture than livestock-grazed land. The gap between cropland and pastureland estimates exists largely because it is challenging to characterize natural versus grazed grasslands from a remote sensing perspective. However, the impasse of pastureland estimates is set to break, with an increasing number of spaceborne sensors and freely available satellite data. The Landsat satellite archive in particular provides researchers with immense amounts of data to improve pastureland information. Here we focus on South America, where pastureland expansion has been scrutinized for the past few decades. We explore the challenges of estimating pastureland using temporal Landsat imagery and focus on key agricultural countries, regions, and ecosystems. We focus on the suggested shift of pastureland from the Argentine Pampas to northern Argentina, and the mixing of small-scale and large-scale ranching in eastern Paraguay and how it could impact the Chaco forest to the west. Further, the Beni Savannahs of northern Bolivia and the Colombian Llanos—both grassland and savannah regions historically used for livestock grazing—have been hinted at as future areas for cropland expansion. There are certainly environmental concerns with pastureland expansion into forests; but what are the environmental implications when well-managed pasture systems are converted to intensive soybean or palm oil plantation? Tropical, grazed grasslands are important habitats for biodiversity, and pasturelands can mitigate soil erosion when well managed. Thus, we must improve estimates of grazed land before we can make informed policy and conservation decisions. This talk presents insights into pastureland estimates in South America and discusses the feasibility to improve current

  16. BeiDou satellite's differential code biases estimation based on uncombined precise point positioning with triple-frequency observable

    Science.gov (United States)

    Fan, Lei; Li, Min; Wang, Cheng; Shi, Chuang

    2017-02-01

    The differential code bias (DCB) of BeiDou satellite is an important topic to make better use of BeiDou system (BDS) for many practical applications. This paper proposes a new method to estimate the BDS satellite DCBs based on triple-frequency uncombined precise point positioning (UPPP). A general model of both triple-frequency UPPP and Geometry-Free linear combination of Phase-Smoothed Range (GFPSR) is presented, in which, the ionospheric observable and the combination of triple-frequency satellite and receiver DCBs (TF-SRDCBs) are derived. Then the satellite and receiver DCBs (SRDCBs) are estimated together with the ionospheric delay that is modeled at each individual station in a weighted least-squares estimator, and the satellite DCBs are determined by introducing the zero-mean condition of all available BDS satellites. To validate the new method, 90 day's real tracking GNSS data (from January to March in 2014) collected from 9 Multi-GNSS Experiment (MGEX) stations (equipped with Trimble NETR9 receiver) is used, and the BDS satellite DCB products from German Aerospace Center (DLR) are taken as reference values for comparison. Results show that the proposed method is able to precisely estimate BDS satellite DCBs: (1) the mean value of the day-to-day scattering for all available BDS satellites is about 0.24 ns, which is reduced in average by 23% when compared with the results derived by only GFPSR. Moreover, the mean value of the day-to-day scattering of IGSO satellites is lower than that of GEO and MEO satellites; (2) the mean value of RMS of the difference with respect to DLR DCB products is about 0.39 ns, which is improved by an average of 11% when compared with the results derived by only GFPSR. Besides, the RMS of IGSO and MEO satellites is at the same level which is better than that of GEO satellites.

  17. Estimating Reliability of Disturbances in Satellite Time Series Data Based on Statistical Analysis

    Science.gov (United States)

    Zhou, Z.-G.; Tang, P.; Zhou, M.

    2016-06-01

    Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with "Change/ No change" by most of the present methods, while few methods focus on estimating reliability (or confidence level) of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1) Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST). (2) Forecasting and detecting disturbances in new time series data. (3) Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI) and Confidence Levels (CL). The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

  18. 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.

  19. New estimations of precipitation and surface sublimation in East Antarctica from snow accumulation measurements

    Energy Technology Data Exchange (ETDEWEB)

    Frezzotti, Massimo; Gragnani, Roberto; Proposito, Marco [l' Energia e l' Ambiente, ' Progetto Clima Globale' , Ente per le Nuove Tecnologie, Rome (Italy); Pourchet, Michel; Gay, Michel; Vincent, Christian; Fily, Michel [CNRS, Laboratoire de Glaciologie et Geophysique de l' Environnement, Saint Martin d' Heres (France); Flora, Onelio [University of Trieste, Dipartimento di Scienze Geologiche, Ambientali e Marine, Trieste (Italy); Gandolfi, Stefano [University of Bologna, Dipartimento di Ingegneria delle Strutture, dei Trasporti, delle Acque, del Rilevamento, del Territorio, Bologna (Italy); Urbini, Stefano [Istituto Nazionale di Geofisica e Vulcanologia, Rome (Italy); Becagli, Silvia; Severi, Mirko; Traversi, Rita; Udisti, Roberto [University of Florence, Dipartimento di Chimica, Florence (Italy)

    2004-12-01

    Surface mass balance (SMB) distribution and its temporal and spatial variability is an essential input parameter in mass balance studies. Different methods were used, compared and integrated (stake farms, ice cores, snow radar, surface morphology, remote sensing) at eight sites along a transect from Terra Nova Bay (TNB) to Dome C (DC) (East Antarctica), to provide detailed information on the SMB. Spatial variability measurements show that the measured maximum snow accumulation (SA) in a 15 km area is well correlated to firn temperature. Wind-driven sublimation processes, controlled by the surface slope in the wind direction, have a huge impact (up to 85% of snow precipitation) on SMB and are significant in terms of past, present and future SMB evaluations. The snow redistribution process is local and has a strong impact on the annual variability of accumulation. The spatial variability of SMB at the kilometre scale is one order of magnitude higher than its temporal variability (20-30%) at the centennial time scale. This high spatial variability is due to wind-driven sublimation. Compared with our SMB calculations, previous compilations generally over-estimate SMB, up to 65% in some areas. (orig.)

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

    Science.gov (United States)

    Mittermaier, M. P.

    2008-05-01

    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.

  1. Estimation of extreme daily precipitation: comparison between regional and geostatistical approaches.

    Science.gov (United States)

    Hellies, Matteo; Deidda, Roberto; Langousis, Andreas

    2016-04-01

    We study the extreme rainfall regime of the Island of Sardinia in Italy, based on annual maxima of daily precipitation. The statistical analysis is conducted using 229 daily rainfall records with at least 50 complete years of observations, collected at different sites by the Hydrological Survey of the Sardinia Region. Preliminary analysis, and the L-skewness and L-kurtosis diagrams, show that the Generalized Extreme Value (GEV) distribution model performs best in describing daily rainfall extremes. The GEV distribution parameters are estimated using the method of Probability Weighted Moments (PWM). To obtain extreme rainfall estimates at ungauged sites, while minimizing uncertainties due to sampling variability, a regional and a geostatistical approach are compared. The regional approach merges information from different gauged sites, within homogeneous regions, to obtain GEV parameter estimates at ungauged locations. The geostatistical approach infers the parameters of the GEV distribution model at locations where measurements are available, and then spatially interpolates them over the study region. In both approaches we use local rainfall means as index-rainfall. In the regional approach we define homogeneous regions by applying a hierarchical cluster analysis based on Ward's method, with L-moment ratios (i.e. L-CV and L-Skewness) as metrics. The analysis results in four contiguous regions, which satisfy the Hosking and Wallis (1997) homogeneity tests. The latter have been conducted using a Monte-Carlo approach based on a 4-parameter Kappa distribution model, fitted to each station cluster. Note that the 4-parameter Kappa model includes the GEV distribution as a sub-case, when the fourth parameter h is set to 0. In the geostatistical approach we apply kriging for uncertain data (KUD), which accounts for the error variance in local parameter estimation and, therefore, may serve as a useful tool for spatial interpolation of metrics affected by high uncertainty. In

  2. Comparison between radar estimations and rain gauge precipitations in the Moldavian Plateau (Romania)

    Science.gov (United States)

    Cheval, Sorin; Burcea, Sorin; Dumitrescu, Alexandru; Antonescu, Bogdan; Bell, Aurora; Breza, Traian

    2010-05-01

    Heavy rainfall events have produced significant damages and casualties in the Moldavian Plateau (Romania) in the last decades. Such phenomena are characterized by large spatial and temporal variations, and the forecast of their occurrence is thus very challenging. This study aims to compare the radar estimations and the rain gauge measurements, in order to improve the quantitative precipitation estimation (QPE) in the area of interest. The research uses data from the WSR-98D S-band Doppler radar located in Bârnova, and from rain gauges within weather stations run by Meteo Romania (Romanian National Meteorological Administration). We have focused on daily (24 h) accumulations registered at weather stations, and the output sustains the radar calibration, fostering the hydrological modeling, including flash flood forecast. The differences between R and G were investigated based on two objectives functions -the ratio R/G (BIAS) and the Root Mean Square Factor (RMSf)- while the correlations used the Pearson scores. Considerable spatial distinctions between areas with good radar accuracy for QPE and perimeters where radar is not capable to provide robust information have been emphasized during the investigations. The validation aimed to predict the rain gauge amounts in certain spots by using the radar information and resulted adjustment parameters. It has been demonstrated that the Bârnova radar data are reliable within approx. 150 km radius, and the comparison with rain gauge measurements can foster consistently the QPE accuracy in the area. This research was completed in the framework of the EU FP6 Project HYDRATE (Hydrometeorological data resources and technologies for effective flash flood forecasting), Contract no: 037024, 2006-2009.

  3. A quantitative method for estimating cloud cover over tropical cyclones from satellite data

    OpenAIRE

    BALOGUN, E. E.

    2011-01-01

    A photometric method for quantifying cloud cover over tropical cyclones as observed from satellite photographs is presented. Two gridded photographs of tropical cyclones are analyzed by this method. On each photograph, nine concentric circles are drawn. The observed or reported centre of the cyclones is used as the centre for each set of concentric circles. Photometric estimates of cloud cover are made along the nine concentric circles. The principle of harmonic analysis is applied to the cl...

  4. 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 classic

  5. 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 classic

  6. Distributed Extended Kalman Filter for Position, Velocity, Time, Estimation in Satellite Navigation Receivers

    Directory of Open Access Journals (Sweden)

    O. Jakubov

    2013-09-01

    Full Text Available Common techniques for position-velocity-time estimation in satellite navigation, iterative least squares and the extended Kalman filter, involve matrix operations. The matrix inversion and inclusion of a matrix library pose requirements on a computational power and operating platform of the navigation processor. In this paper, we introduce a novel distributed algorithm suitable for implementation in simple parallel processing units each for a tracked satellite. Such a unit performs only scalar sum, subtraction, multiplication, and division. The algorithm can be efficiently implemented in hardware logic. Given the fast position-velocity-time estimator, frequent estimates can foster dynamic performance of a vector tracking receiver. The algorithm has been designed from a factor graph representing the extended Kalman filter by splitting vector nodes into scalar ones resulting in a cyclic graph with few iterations needed. Monte Carlo simulations have been conducted to investigate convergence and accuracy. Simulation case studies for a vector tracking architecture and experimental measurements with a real-time software receiver developed at CTU in Prague were conducted. The algorithm offers compromises in stability, accuracy, and complexity depending on the number of iterations. In scenarios with a large number of tracked satellites, it can outperform the traditional methods at low complexity.

  7. Estimation of snow cover distribution in Beas basin, Indian Himalaya using satellite data and ground measurements

    Indian Academy of Sciences (India)

    H S Negi; A V Kulkarni; B S Semwal

    2009-10-01

    In the present paper,a methodology has been developed for the mapping of snow cover in Beas basin,Indian Himalaya using AWiFS (IRS-P6)satellite data.The complexities in the mapping of snow cover in the study area are snow under vegetation,contaminated snow and patchy snow. To overcome these problems,field measurements using spectroradiometer were carried out and reflectance/snow indices trend were studied.By evaluation and validation of different topographic correction models,it was observed that,the normalized difference snow index (NDSI)values remain constant with the variations in slope and aspect and thus NDSI can take care of topography effects.Different snow cover mapping methods using snow indices are compared to find the suitable mapping technique.The proposed methodology for snow cover mapping uses the NDSI (estimated using planetary re flectance),NIR band reflectance and forest/vegetation cover information.The satellite estimated snow or non-snow pixel information using proposed methodology was validated with the snow cover information collected at three observatory locations and it was found that the algorithm classify all the sample points correctly,once that pixel is cloud free.The snow cover distribution was estimated using one year (2004 –05)cloud free satellite data and good correlation was observed between increase/decrease areal extent of seasonal snow cover and ground observed fresh snowfall and standing snow data.

  8. Signal Conditioning for the Kalman Filter: Application to Satellite Attitude Estimation with Magnetometer and Sun Sensors.

    Science.gov (United States)

    Esteban, Segundo; Girón-Sierra, Jose M; Polo, Óscar R; Angulo, Manuel

    2016-10-31

    Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.

  9. Signal Conditioning for the Kalman Filter: Application to Satellite Attitude Estimation with Magnetometer and Sun Sensors

    Directory of Open Access Journals (Sweden)

    Segundo Esteban

    2016-10-01

    Full Text Available Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.

  10. Differences in estimating terrestrial water flux from three satellite-based Priestley-Taylor algorithms

    Science.gov (United States)

    Yao, Yunjun; Liang, Shunlin; Yu, Jian; Zhao, Shaohua; Lin, Yi; Jia, Kun; Zhang, Xiaotong; Cheng, Jie; Xie, Xianhong; Sun, Liang; Wang, Xuanyu; Zhang, Lilin

    2017-04-01

    Accurate estimates of terrestrial latent heat of evaporation (LE) for different biomes are essential to assess energy, water and carbon cycles. Different satellite- based Priestley-Taylor (PT) algorithms have been developed to estimate LE in different biomes. However, there are still large uncertainties in LE estimates for different PT algorithms. In this study, we evaluated differences in estimating terrestrial water flux in different biomes from three satellite-based PT algorithms using ground-observed data from eight eddy covariance (EC) flux towers of China. The results reveal that large differences in daily LE estimates exist based on EC measurements using three PT algorithms among eight ecosystem types. At the forest (CBS) site, all algorithms demonstrate high performance with low root mean square error (RMSE) (less than 16 W/m2) and high squared correlation coefficient (R2) (more than 0.9). At the village (HHV) site, the ATI-PT algorithm has the lowest RMSE (13.9 W/m2), with bias of 2.7 W/m2 and R2 of 0.66. At the irrigated crop (HHM) site, almost all models algorithms underestimate LE, indicating these algorithms may not capture wet soil evaporation by parameterization of the soil moisture. In contrast, the SM-PT algorithm shows high values of R2 (comparable to those of ATI-PT and VPD-PT) at most other (grass, wetland, desert and Gobi) biomes. There are no obvious differences in seasonal LE estimation using MODIS NDVI and LAI at most sites. However, all meteorological or satellite-based water-related parameters used in the PT algorithm have uncertainties for optimizing water constraints. This analysis highlights the need to improve PT algorithms with regard to water constraints.

  11. The impacts of climatological adjustment of quantitative precipitation estimates on the accuracy of flash flood detection

    Science.gov (United States)

    Zhang, Yu; Reed, Sean; Gourley, Jonathan J.; Cosgrove, Brian; Kitzmiller, David; Seo, Dong-Jun; Cifelli, Robert

    2016-10-01

    The multisensor Quantitative Precipitation Estimates (MQPEs) created by the US National Weather Service (NWS) are subject to a non-stationary bias. This paper quantifies the impacts of climatological adjustment of MQPEs alone, as well as the compound impacts of adjustment and model calibration, on the accuracy of simulated flood peak magnitude and that in detecting flood events. Our investigation is based on 19 watersheds in the mid-Atlantic region of US, which are grouped into small (500 km2) watersheds. NWS archival MQPEs over 1997-2013 for this region are adjusted to match concurrent gauge-based monthly precipitation accumulations. Then raw and adjusted MQPEs serve as inputs to the NWS distributed hydrologic model-threshold frequency framework (DHM-TF). Two experiments via DHM-TF are performed. The first one examines the impacts of adjustment alone through uncalibrated model simulations, whereas the second one focuses on the compound effects of adjustment and calibration on the detection of flood events. Uncalibrated model simulations show broad underestimation of flood peaks for small watersheds and overestimation those for large watersheds. Prior to calibration, adjustment alone tends to reduce the magnitude of simulated flood peaks for small and large basins alike, with 95% of all watersheds experienced decline over 2004-2013. A consequence is that a majority of small watersheds experience no improvement, or deterioration in bias (0% of basins experiencing improvement). By contrast, most (73%) of larger ones exhibit improved bias. Outcomes of the detection experiment show that the role of adjustment is not diminished by calibration for small watersheds, with only 25% of which exhibiting reduced bias after adjustment with calibrated parameters. Furthermore, it is shown that calibration is relatively effective in reducing false alarms (e.g., false alarm rate is down from 0.28 to 0.19 after calibration for small watersheds with calibrated parameters); but its

  12. 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.

  13. Monte-Carlo Estimation of the Inflight Performance of the GEMS Satellite X-Ray Polarimeter

    Science.gov (United States)

    Kitaguchi, Takao; Tamagawa, Toru; Hayato, Asami; Enoto, Teruaki; Yoshikawa, Akifumi; Kaneko, Kenta; Takeuchi, Yoko; Black, Kevin; Hill, Joanne; Jahoda, Keith; Krizmanic, John; Sturner, Steve; Griffiths, Scott; Kaaret, Philip; Marlowe, Hannah

    2014-01-01

    We report a Monte-Carlo estimation of the in-orbit performance of a cosmic X-ray polarimeter designed to be installed on the focal plane of a small satellite. The simulation uses GEANT for the transport of photons and energetic particles and results from Magboltz for the transport of secondary electrons in the detector gas. We validated the simulation by comparing spectra and modulation curves with actual data taken with radioactive sources and an X-ray generator. We also estimated the in-orbit background induced by cosmic radiation in low Earth orbit.

  14. 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 attenti

  15. 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 attenti

  16. Exploration of a Dynamic Merging Scheme for Precipitation Estimation over a Small Urban Catchment

    Science.gov (United States)

    Al-Azerji, Sherien; Rico-Ramirez, Miguel, ,, Dr.; Han, Dawei, ,, Prof.

    2016-04-01

    The accuracy of quantitative precipitation estimation is of significant importance for urban areas due to the potentially damaging consequences that can result from pluvial flooding. Improved accuracy could be accomplished by merging rain gauge measurements with weather radar data through different merging methods. Several factors may affect the accuracy of the merged data, and the gauge density used for merging is one of the most important. However, if there are no gauges inside the research area, then a gauge network outside the research area can be used for the merging. Generally speaking, the denser the rain gauge network is, the better the merging results that can be achieved. However, in practice, the rain gauge network around the research area is fixed, and the research question is about the optimal merging area. The hypothesis is that if the merging area is too small, there are fewer gauges for merging and thus the result would be poor. If the merging area is too large, gauges far away from the research area can be included in merging. However, due to their large distances, those gauges far away from the research area provide little relevant information to the study and may even introduce noise in merging. Therefore, an optimal merging area that produces the best merged rainfall estimation in the research area could exist. To test this hypothesis, the distance from the centre of the research area and the number of merging gauges around the research area were gradually increased and merging with a new domain of radar data was then performed. The performance of the new merging scheme was compared with a gridded interpolated rainfall from four experimental rain gauges installed inside the research area for validation. The result of this analysis shows that there is indeed an optimum distance from the centre of research area and consequently an optimum number of rain gauges that produce the best merged rainfall data inside the research area. This study is of

  17. Simultaneous state and actuator fault estimation for satellite attitude control systems

    Institute of Scientific and Technical Information of China (English)

    Cheng Yao; Wang Rixin; Xu Minqiang; Li Yuqing

    2016-01-01

    In this paper, a new nonlinear augmented observer is proposed and applied to satellite attitude control systems. The observer can estimate system state and actuator fault simultaneously. It can enhance the performances of rapidly-varying faults estimation. Only original system matrices are adopted in the parameter design. The considered faults can be unbounded, and the proposed augmented observer can estimate a large class of faults. Systems without disturbances and the fault whose finite times derivatives are zero piecewise are initially considered, followed by a discussion of a general situation where the system is subject to disturbances and the finite times derivatives of the faults are not null but bounded. For the considered nonlinear system, convergence conditions of the observer are provided and the stability analysis is performed using Lyapunov direct method. Then a feasible algorithm is explored to compute the observer parameters using linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed approach is illustrated by considering an example of a closed-loop satellite attitude control system. The simulation results show satisfactory perfor-mance in estimating states and actuator faults. It also shows that multiple faults can be estimated successfully.

  18. Inversion Technique for Estimating Emissions of Volcanic Ash from Satellite Imagery

    Science.gov (United States)

    Pelley, Rachel; Cooke, Michael; Manning, Alistair; Thomson, David; Witham, Claire; Hort, Matthew

    2014-05-01

    When using dispersion models such as NAME (Numerical Atmospheric-dispersion Modelling Environment) to predict the dispersion of volcanic ash, a source term defining the mass release rate of ash is required. Inversion modelling using observations of the ash plume provides a method of estimating the source term for use in NAME. Our inversion technique makes use of satellite retrievals, calculated using data from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument on-board the MSG (Meteosat Second Generation) satellite, as the ash observations. InTEM (Inversion Technique for Emission Modelling) is the UK Met Office's inversion modelling system. Recently the capability to estimate time and height varying source terms has been implemented and applied to volcanic ash. InTEM uses a probabilistic approach to fit NAME model concentrations to satellite retrievals. This is achieved by applying Bayes Theorem to give a cost function for the source term. Source term profiles with lower costs generate model concentrations that better fit the satellite retrievals. InTEM uses the global optimisation technique, simulated annealing, to find the minimum of the cost function. The use of a probabilistic approach allows the uncertainty in the satellite retrievals to be incorporated into the inversion technique. InTEM makes use of satellite retrievals of both ash column loadings and of cloud free regions. We present a system that allows InTEM to be used during an eruption. The system is automated and can produce source term updates up to four times a day. To allow automation hourly satellite retrievals of ash are routinely produced using conservative detection limits. The conservative detection limits provide good detection of the ash plume while limiting the number of false alarms. Regions which are flagged as ash contaminated or free from cloud (both meteorological and ash) are used in the InTEM system. This approach is shown to improve the concentrations in the

  19. Fast emission estimates in China and South Africa constrained by satellite observations

    Science.gov (United States)

    Mijling, Bas; van der A, Ronald

    2013-04-01

    Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Unfortunately, bottom-up emission inventories, compiled from large quantities of statistical data, are easily outdated for emerging economies such as China and South Africa, where rapid economic growth change emissions accordingly. Alternatively, top-down emission estimates from satellite observations of air constituents have important advantages of being spatial consistent, having high temporal resolution, and enabling emission updates shortly after the satellite data become available. However, constraining emissions from observations of concentrations is computationally challenging. Within the GlobEmission project (part of the Data User Element programme of ESA) a new algorithm has been developed, specifically designed for fast daily emission estimates of short-lived atmospheric species on a mesoscopic scale (0.25 × 0.25 degree) from satellite observations of column concentrations. The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates in East China and South Africa, using the CHIMERE chemical transport model together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. The observations are used to construct a monthly emission time series, which reveal important emission trends such as the emission reduction measures during the Beijing Olympic Games, and the impact and recovery from the global economic crisis. The algorithm is also able to detect emerging sources (e.g. new power plants) and improve emission information for areas where proxy data are not or badly known (e

  20. REKF and RUKF for pico satellite attitude estimation in the presence of measurement faults

    Institute of Scientific and Technical Information of China (English)

    Halil Ersin Söken; Chingiz Hajiyev

    2014-01-01

    When a pico satel ite is under normal operational condi-tions, whether it is extended or unscented, a conventional Kalman filter gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunc-tions in the estimation system, the Kalman filter gives inaccurate results and diverges by time. This study compares two different robust Kalman filtering algorithms, robust extended Kalman filter (REKF) and robust unscented Kalman filter (RUKF), for the case of measurement malfunctions. In both filters, by the use of de-fined variables named as the measurement noise scale factor, the faulty measurements are taken into the consideration with a smal weight, and the estimations are corrected without affecting the characteristic of the accurate ones. The proposed robust Kalman filters are applied for the attitude estimation process of a pico satel-lite, and the results are compared.

  1. Toward the Estimation of Surface Soil Moisture Content Using Geostationary Satellite Data over Sparsely Vegetated Area

    Directory of Open Access Journals (Sweden)

    Pei Leng

    2015-04-01

    Full Text Available Based on a novel bare surface soil moisture (SSM retrieval model developed from the synergistic use of the diurnal cycles of land surface temperature (LST and net surface shortwave radiation (NSSR (Leng et al. 2014. “Bare Surface Soil Moisture Retrieval from the Synergistic Use of Optical and Thermal Infrared Data”. International Journal of Remote Sensing 35: 988–1003., this paper mainly investigated the model’s capability to estimate SSM using geostationary satellite observations over vegetated area. Results from the simulated data primarily indicated that the previous bare SSM retrieval model is capable of estimating SSM in the low vegetation cover condition with fractional vegetation cover (FVC ranging from 0 to 0.3. In total, the simulated data from the Common Land Model (CoLM on 151 cloud-free days at three FLUXNET sites that with different climate patterns were used to describe SSM estimates with different underlying surfaces. The results showed a strong correlation between the estimated SSM and the simulated values, with a mean Root Mean Square Error (RMSE of 0.028 m3·m−3 and a coefficient of determination (R2 of 0.869. Moreover, diurnal cycles of LST and NSSR derived from the Meteosat Second Generation (MSG satellite data on 59 cloud-free days were utilized to estimate SSM in the REMEDHUS soil moisture network (Spain. In particular, determination of the model coefficients synchronously using satellite observations and SSM measurements was explored in detail in the cases where meteorological data were not available. A preliminary validation was implemented to verify the MSG pixel average SSM in the REMEDHUS area with the average SSM calculated from the site measurements. The results revealed a significant R2 of 0.595 and an RMSE of 0.021 m3·m−3.

  2. Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data

    Directory of Open Access Journals (Sweden)

    Sandra Eckert

    2012-03-01

    Full Text Available Accurate estimation of aboveground biomass and carbon stock has gained importance in the context of the United Nations Framework Convention on Climate Change (UNFCCC and the Kyoto Protocol. In order to develop improved forest stratum–specific aboveground biomass and carbon estimation models for humid rainforest in northeast Madagascar, this study analyzed texture measures derived from WorldView-2 satellite data. A forest inventory was conducted to develop stratum-specific allometric equations for dry biomass. On this basis, carbon was calculated by applying a conversion factor. After satellite data preprocessing, vegetation indices, principal components, and texture measures were calculated. The strength of their relationships with the stratum-specific plot data was analyzed using Pearson’s correlation. Biomass and carbon estimation models were developed by performing stepwise multiple linear regression. Pearson’s correlation coefficients revealed that (a texture measures correlated more with biomass and carbon than spectral parameters, and (b correlations were stronger for degraded forest than for non-degraded forest. For degraded forest, the texture measures of Correlation, Angular Second Moment, and Contrast, derived from the red band, contributed to the best estimation model, which explained 84% of the variability in the field data (relative RMSE = 6.8%. For non-degraded forest, the vegetation index EVI and the texture measures of Variance, Mean, and Correlation, derived from the newly introduced coastal blue band, both NIR bands, and the red band, contributed to the best model, which explained 81% of the variability in the field data (relative RMSE = 11.8%. These results indicate that estimation of tropical rainforest biomass/carbon, based on very high resolution satellite data, can be improved by (a developing and applying forest stratum–specific models, and (b including textural information in addition to spectral information.

  3. 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...... 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...

  4. Evaluation and intercomparison of clouds, precipitation, and radiation budgets in recent reanalyses using satellite-surface observations

    Science.gov (United States)

    Dolinar, Erica K.; Dong, Xiquan; Xi, Baike

    2016-04-01

    Atmospheric reanalysis datasets offer a resource for investigating climate processes and extreme events; however, their uncertainties must first be addressed. In this study, we evaluate the five reanalyzed (20CR, CFSR, Era-Interim, JRA-25, and MERRA) cloud fraction (CF), precipitation rates (PR), and top-of-atmosphere (TOA) and surface radiation budgets using satellite observations during the period 03/2000-02/2012. Compared to the annual averaged CF of 56.7 % from CERES MODIS (CM) four of the five reanalyses underpredict CFs by 1.7-4.6 %, while 20CR overpredicts this result by 7.4 %. PR from the Tropical Rainfall Measurement Mission (TRMM) is 3.0 mm/day and the reanalyzed PRs agree with TRMM within 0.1-0.6 mm/day. The shortwave (SW) and longwave (LW) TOA cloud radiative effects (CREtoa) calculated by CERES EBAF (CE) are -48.1 and 27.3 W/m2, respectively, indicating a net cooling effect of -20.8 W/m2. Of the available reanalysis results, the CFSR and MERRA calculated net CREtoa values agree with CE within 1 W/m2, while the JRA-25 result is ~10 W/m2 more negative than the CE result, predominantly due to the underpredicted magnitude of the LW warming in the JRA-25 reanalysis. A regime metric is developed using the vertical motion field at 500 hPa over the oceans. Aptly named the "ascent" and "descent" regimes, these areas are distinguishable in their characteristic synoptic patterns and the predominant cloud-types; convective-type clouds and marine boundary layer (MBL) stratocumulus clouds. In general, clouds are overpredicted (underpredicted) in the ascent (descent) regime and the biases are often larger in the ascent regime than in the descent regime. PRs are overpredicted in both regimes; however the observed and reanalyzed PRs over the ascent regime are an order of magnitude larger than those over the descent regime, indicating different types of clouds exist in these two regimes. Based upon the Atmospheric Radiation Measurement Program ground-based and CM

  5. Model of a neural network inertial satellite navigation system capable of estimating the earth's gravitational field gradient

    Science.gov (United States)

    Devyatisil'nyi, A. S.

    2016-09-01

    A model for recognizing inertial and satellite data on an object's motion that are delivered by a set of distributed onboard sensors (newtonmeters, gyros, satellite receivers) has been described. Specifically, the model is capable of estimating the parameters of the gravitational field.

  6. Investigation of Atmospheric Modelling Framework for Better Reconstruction on Historical Extreme Precipitation Event in PMP Estimation

    Science.gov (United States)

    Chen, X.; Hossain, F.; Leung, L. R.

    2015-12-01

    During May 1-2, 2010, a record-breaking storm hit Nashville, and caused huge humanity and societal loss. It raises the importance of forecasting/reconstructing these types of extreme weather systems once again, in the meanwhile providing an excellent case for such atmospheric modelling studies. However, earlier studies suggest that successful reconstruction of this event depends on and is sensitive to a number of model options, making it difficult to establish a better model framework with more confidence. In this study we employed the Weather Research and Forecast (WRF) model to investigate how this extreme precipitation event is sensitive to the model configuration, and identified options that would produce better results. We tested several combinations of modelling grid sizes together with initial/boundary conditions (IC/BC). At different grid sizes, we conducted a set of tests on various combinations of microphysics (Morrison, new Thompson and WSM5) and cumulus process (Kain-Fristch, Grell-Devenyi and Grell-Freitas) parameterization schemes. The model results were intensively evaluated under bias analysis as well as other metrics (probability of detection, bias, false alerts, HSS, ETS). The evaluation suggests that in general, simulation results benefit from finer model grids (5km). At 5km level, NCEP2 or NAM IC/BCs are more representative for the 2010 Nashville storm. There are no universally good parameterization schemes, but the WSM5 microphysics scheme, Kain-Fristch and Grell-Freitas cumulus schemes are recommended over other tested schemes. These better schemes would help to make better estimation of PMP in the region.

  7. An improved technique for global daily sunshine duration estimation using satellite imagery

    Institute of Scientific and Technical Information of China (English)

    Muhammad Ali SHAMIM; Renji REMESAN; Da-wei HAN; Naeem EJAZ; Ayub ELAHI

    2012-01-01

    This paper presents an improved model for global sunshine duration estimation.The methodology incorporates geostationary satellite images by including snow cover information,sun and satellite angles and a trend correction factor for seasons,for the determination of cloud cover index.The effectiveness of the proposed methodology has been tested using Meteosat geostationary satellite images in the visible band with a temporal resolution of 1 h and spatial resolution of 2.5 km×2.5 km,for the Brue Catchment in the southwest of England.Validation results show a significant improvement in the estimation of global sunshine duration by the proposed method as compared to its predecessor (R2 is improved from 0.68 to 0.83,root mean squared error (RMSE) from 2.37 h/d to 1.19 h/d and the mean biased error (MBE) from 0.21 h/d to 0.08 h/d).Further studies are needed to test this method in other parts of the world with different climate and geographical conditions.

  8. Eliminating Obliquity Error from the Estimation of Ionospheric Delay in a Satellite-Based Augmentation System

    Science.gov (United States)

    Sparks, Lawrence

    2013-01-01

    Current satellite-based augmentation systems estimate ionospheric delay using algorithms that assume the electron density of the ionosphere is non-negligible only in a thin shell located near the peak of the actual profile. In its initial operating capability, for example, the Wide Area Augmentation System incorporated the thin shell model into an estimation algorithm that calculates vertical delay using a planar fit. Under disturbed conditions or at low latitude where ionospheric structure is complex, however, the thin shell approximation can serve as a significant source of estimation error. A recent upgrade of the system replaced the planar fit algorithm with an algorithm based upon kriging. The upgrade owes its success, in part, to the ability of kriging to mitigate the error due to this approximation. Previously, alternative delay estimation algorithms have been proposed that eliminate the need for invoking the thin shell model altogether. Prior analyses have compared the accuracy achieved by these methods to the accuracy achieved by the planar fit algorithm. This paper extends these analyses to include a comparison with the accuracy achieved by kriging. It concludes by examining how a satellite-based augmentation system might be implemented without recourse to the thin shell approximation.

  9. A preliminary estimate of geoid-induced variations in repeat orbit satellite altimeter observations

    Science.gov (United States)

    Brenner, Anita C.; Beckley, B. D.; Koblinsky, C. J.

    1990-01-01

    Altimeter satellites are often maintained in a repeating orbit to facilitate the separation of sea-height variations from the geoid. However, atmospheric drag and solar radiation pressure cause a satellite orbit to drift. For Geosat this drift causes the ground track to vary by + or - 1 km about the nominal repeat path. This misalignment leads to an error in the estimates of sea surface height variations because of the local slope in the geoid. This error has been estimated globally for the Geosat Exact Repeat Mission using a mean sea surface constructed from Geos 3 and Seasat altimeter data. Over most of the ocean the geoid gradient is small, and the repeat-track misalignment leads to errors of only 1 to 2 cm. However, in the vicinity of trenches, continental shelves, islands, and seamounts, errors can exceed 20 cm. The estimated error is compared with direct estimates from Geosat altimetry, and a strong correlation is found in the vicinity of the Tonga and Aleutian trenches. This correlation increases as the orbit error is reduced because of the increased signal-to-noise ratio.

  10. Fault estimation of satellite reaction wheels using covariance based adaptive unscented Kalman filter

    Science.gov (United States)

    Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat

    2017-05-01

    Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.

  11. A preliminary estimate of geoid-induced variations in repeat orbit satellite altimeter observations

    Science.gov (United States)

    Brenner, Anita C.; Beckley, B. D.; Koblinsky, C. J.

    1990-01-01

    Altimeter satellites are often maintained in a repeating orbit to facilitate the separation of sea-height variations from the geoid. However, atmospheric drag and solar radiation pressure cause a satellite orbit to drift. For Geosat this drift causes the ground track to vary by + or - 1 km about the nominal repeat path. This misalignment leads to an error in the estimates of sea surface height variations because of the local slope in the geoid. This error has been estimated globally for the Geosat Exact Repeat Mission using a mean sea surface constructed from Geos 3 and Seasat altimeter data. Over most of the ocean the geoid gradient is small, and the repeat-track misalignment leads to errors of only 1 to 2 cm. However, in the vicinity of trenches, continental shelves, islands, and seamounts, errors can exceed 20 cm. The estimated error is compared with direct estimates from Geosat altimetry, and a strong correlation is found in the vicinity of the Tonga and Aleutian trenches. This correlation increases as the orbit error is reduced because of the increased signal-to-noise ratio.

  12. Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

    Science.gov (United States)

    Husak, G.J.; Marshall, M. T.; Michaelsen, J.; Pedreros, Diego; Funk, Christopher C.; Galu, G.

    2008-01-01

    Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

  13. Elastic thickness and heat flux estimates for the uranian satellite Ariel

    Science.gov (United States)

    Peterson, G.; Nimmo, F.; Schenk, P.

    2015-04-01

    The surface of Ariel, an icy satellite orbiting Uranus, shows extensional tectonic features suggesting an episode of endogenic heating in the satellite's past. Using topography derived from stereo-photoclinometry, we identified flexural uplift at a rift zone suggesting elastic thickness values in the range 3.8-4.4 km. We estimate the temperature at the base of the lithosphere to be in the range 99-146 K, depending on the strain rate assumed, with corresponding heat fluxes of 28-92 mW/m2. Neither tidal heating, assuming Ariel's current eccentricity, nor radiogenic heat production from the silicate core are enough to cause the inferred heat fluxes. None of three proposed ancient mean-motion resonances produce equilibrium tidal heating values in excess of 4.3 mW/m2. Thus, the origin of the inferred high heat fluxes is currently mysterious.

  14. Satellite Estimates of Crop Area and Maize Yield in Zambia's Agricultural Districts

    Science.gov (United States)

    Azzari, G.; Lobell, D. B.

    2015-12-01

    Predicting crop yield and area from satellite is a valuable tool to monitor different aspects of productivity dynamics and food security. In Sub-Saharan Africa, where the agricultural landscape is complex and dominated by smallholder systems, such dynamics need to be investigated at the field scale. We leveraged the large data pool and computational power of Google Earth Engine to 1) generate 30 m resolution cover maps of selected provinces of Zambia, 2) estimate crop area, and 3) produce yearly maize yield maps using the recently developed SCYM (Scalable satellite-based Crop Yield Mapper) algorithm. We will present our results and their validation against a ground survey dataset collected yearly by the Zambia Ministry of Agriculture from about 12,500 households.

  15. 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.

  16. Satellite-based estimate of aerosol direct radiative effect over the South-East Atlantic

    Directory of Open Access Journals (Sweden)

    L. Costantino

    2013-09-01

    Full Text Available The net effect of aerosol Direct Radiative Forcing (DRF is the balance between the scattering effect that reflects solar radiation back to space (cooling, and the absorption that decreases the reflected sunlight (warming. The amplitude of these two effects and their balance depends on the aerosol load, its absorptivity, the cloud fraction and the respective position of aerosol and cloud layers. In this study, we use the information provided by CALIOP (CALIPSO satellite and MODIS (AQUA satellite instruments as input data to a Rapid Radiative Transfer Model (RRTM and quantify the shortwave (SW aerosol direct atmospheric forcing, over the South-East Atlantic. The combination of the passive and active measurements allows estimates of the horizontal and vertical distributions of the aerosol and cloud parameters. We use a parametrization of the Single Scattering Albedo (SSA based on the satellite-derived Angstrom coefficient. The South East Atlantic is a particular region, where bright stratocumulus clouds are often topped by absorbing smoke particles. Results from radiative transfer simulations confirm the similar amplitude of the cooling effect, due to light scattering by the aerosols, and the warming effect, due to the absorption by the same particles. Over six years of satellite retrievals, from 2005 to 2010, the South-East Atlantic all-sky SW DRF is −0.03 W m−2, with a spatial standard deviation of 8.03 W m−2. In good agreement with previous estimates, statistics show that a cloud fraction larger than 0.5 is generally associated with positive all-sky DRF. In case of cloudy-sky and aerosol located only above the cloud top, a SSA larger than 0.91 and cloud optical thickness larger than 4 can be considered as threshold values, beyond which the resulting radiative forcing becomes positive.

  17. Stratified estimation of forest area using satellite imagery, inventory data, and the k-nearest neighbors technique

    Science.gov (United States)

    Ronald E. McRoberts; Mark D. Nelson; Daniel G. Wendt

    2002-01-01

    For two large study areas in Minnesota, USA, stratified estimation using classified Landsat Thematic Mapper satellite imagery as the basis for stratification was used to estimate forest area. Measurements of forest inventory plots obtained for a 12-month period in 1998 and 1999 were used as the source of data for within-stratum estimates. These measurements further...

  18. An Efficient Two-Fold Marginalized Bayesian Filter for Multipath Estimation in Satellite Navigation Receivers

    Directory of Open Access Journals (Sweden)

    Robertson Patrick

    2010-01-01

    Full Text Available Multipath is today still one of the most critical problems in satellite navigation, in particular in urban environments, where the received navigation signals can be affected by blockage, shadowing, and multipath reception. Latest multipath mitigation algorithms are based on the concept of sequential Bayesian estimation and improve the receiver performance by exploiting the temporal constraints of the channel dynamics. In this paper, we specifically address the problem of estimating and adjusting the number of multipath replicas that is considered by the receiver algorithm. An efficient implementation via a two-fold marginalized Bayesian filter is presented, in which a particle filter, grid-based filters, and Kalman filters are suitably combined in order to mitigate the multipath channel by efficiently estimating its time-variant parameters in a track-before-detect fashion. Results based on an experimentally derived set of channel data corresponding to a typical urban propagation environment are used to confirm the benefit of our novel approach.

  19. Frequency E