Assessment of satellite-based precipitation estimates over Paraguay

Science.gov (United States)

Oreggioni Weiberlen, Fiorella; Báez Benítez, Julián

2018-04-01

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

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

OpenAIRE

Ayehu, Getachew Tesfaye; Tadesse, Tsegaye; Gessesse, Berhan; Dinku, Tufa

2018-01-01

Accurate measurement of rainfall is vital to analyze the spatial and temporal patterns of precipitation at various scales. However, the conventional rain gauge observations in many parts of the world such as Ethiopia are sparse and unevenly distributed. An alternative to traditional rain gauge observations could be satellite-based rainfall estimates. Satellite rainfall estimates could be used as a sole product (e.g., in areas with no (or poor) ground observations) or through...

Estimating Typhoon Rainfall over Sea from SSM/I Satellite Data Using an Improved Genetic Programming

Science.gov (United States)

Yeh, K.; Wei, H.; Chen, L.; Liu, G.

2010-12-01

Estimating Typhoon Rainfall over Sea from SSM/I Satellite Data Using an Improved Genetic Programming Keh-Chia Yeha, Hsiao-Ping Weia,d, Li Chenb, and Gin-Rong Liuc a Department of Civil Engineering, National Chiao Tung University, Hsinchu, Taiwan, 300, R.O.C. b Department of Civil Engineering and Engineering Informatics, Chung Hua University, Hsinchu, Taiwan, 300, R.O.C. c Center for Space and Remote Sensing Research, National Central University, Tao-Yuan, Taiwan, 320, R.O.C. d National Science and Technology Center for Disaster Reduction, Taipei County, Taiwan, 231, R.O.C. Abstract This paper proposes an improved multi-run genetic programming (GP) and applies it to predict the rainfall using meteorological satellite data. GP is a well-known evolutionary programming and data mining method, used to automatically discover the complex relationships among nonlinear systems. The main advantage of GP is to optimize appropriate types of function and their associated coefficients simultaneously. This study makes an improvement to enhance escape ability from local optimums during the optimization procedure. The GP continuously runs several times by replacing the terminal nodes at the next run with the best solution at the current run. The current novel model improves GP, obtaining a highly nonlinear mathematical equation to estimate the rainfall. In the case study, this improved GP described above combining with SSM/I satellite data is employed to establish a suitable method for estimating rainfall at sea surface during typhoon periods. These estimated rainfalls are then verified with the data from four rainfall stations located at Peng-Jia-Yu, Don-Gji-Dao, Lan-Yu, and Green Island, which are four small islands around Taiwan. From the results, the improved GP can generate sophisticated and accurate nonlinear mathematical equation through two-run learning procedures which outperforms the traditional multiple linear regression, empirical equations and back-propagated network

Comparing Satellite Rainfall Estimates with Rain-Gauge Data: Optimal Strategies Suggested by a Spectral Model

Science.gov (United States)

Bell, Thomas L.; Kundu, Prasun K.; Lau, William K. M. (Technical Monitor)

2002-01-01

Validation of satellite remote-sensing methods for estimating rainfall against rain-gauge data is attractive because of the direct nature of the rain-gauge measurements. Comparisons of satellite estimates to rain-gauge data are difficult, however, because of the extreme variability of rain and the fact that satellites view large areas over a short time while rain gauges monitor small areas continuously. In this paper, a statistical model of rainfall variability developed for studies of sampling error in averages of satellite data is used to examine the impact of spatial and temporal averaging of satellite and gauge data on intercomparison results. The model parameters were derived from radar observations of rain, but the model appears to capture many of the characteristics of rain-gauge data as well. The model predicts that many months of data from areas containing a few gauges are required to validate satellite estimates over the areas, and that the areas should be of the order of several hundred km in diameter. Over gauge arrays of sufficiently high density, the optimal areas and averaging times are reduced. The possibility of using time-weighted averages of gauge data is explored.

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

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Singaiah Chintalapudi

2014-05-01

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

Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale

Science.gov (United States)

Beria, Harsh; Nanda, Trushnamayee; Singh Bisht, Deepak; Chatterjee, Chandranath

2017-12-01

The last couple of decades have seen the outburst of a number of satellite-based precipitation products with Tropical Rainfall Measuring Mission (TRMM) as the most widely used for hydrologic applications. Transition of TRMM into the Global Precipitation Measurement (GPM) promises enhanced spatio-temporal resolution along with upgrades to sensors and rainfall estimation techniques. The dependence of systematic error components in rainfall estimates of the Integrated Multi-satellitE Retrievals for GPM (IMERG), and their variation with climatology and topography, was evaluated over 86 basins in India for year 2014 and compared with the corresponding (2014) and retrospective (1998-2013) TRMM estimates. IMERG outperformed TRMM for all rainfall intensities across a majority of Indian basins, with significant improvement in low rainfall estimates showing smaller negative biases in 75 out of 86 basins. Low rainfall estimates in TRMM showed a systematic dependence on basin climatology, with significant overprediction in semi-arid basins, which gradually improved in the higher rainfall basins. Medium and high rainfall estimates of TRMM exhibited a strong dependence on basin topography, with declining skill in higher elevation basins. The systematic dependence of error components on basin climatology and topography was reduced in IMERG, especially in terms of topography. Rainfall-runoff modeling using the Variable Infiltration Capacity (VIC) model over two flood-prone basins (Mahanadi and Wainganga) revealed that improvement in rainfall estimates in IMERG did not translate into improvement in runoff simulations. More studies are required over basins in different hydroclimatic zones to evaluate the hydrologic significance of IMERG.

Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale

Directory of Open Access Journals (Sweden)

H. Beria

2017-12-01

Full Text Available The last couple of decades have seen the outburst of a number of satellite-based precipitation products with Tropical Rainfall Measuring Mission (TRMM as the most widely used for hydrologic applications. Transition of TRMM into the Global Precipitation Measurement (GPM promises enhanced spatio-temporal resolution along with upgrades to sensors and rainfall estimation techniques. The dependence of systematic error components in rainfall estimates of the Integrated Multi-satellitE Retrievals for GPM (IMERG, and their variation with climatology and topography, was evaluated over 86 basins in India for year 2014 and compared with the corresponding (2014 and retrospective (1998–2013 TRMM estimates. IMERG outperformed TRMM for all rainfall intensities across a majority of Indian basins, with significant improvement in low rainfall estimates showing smaller negative biases in 75 out of 86 basins. Low rainfall estimates in TRMM showed a systematic dependence on basin climatology, with significant overprediction in semi-arid basins, which gradually improved in the higher rainfall basins. Medium and high rainfall estimates of TRMM exhibited a strong dependence on basin topography, with declining skill in higher elevation basins. The systematic dependence of error components on basin climatology and topography was reduced in IMERG, especially in terms of topography. Rainfall-runoff modeling using the Variable Infiltration Capacity (VIC model over two flood-prone basins (Mahanadi and Wainganga revealed that improvement in rainfall estimates in IMERG did not translate into improvement in runoff simulations. More studies are required over basins in different hydroclimatic zones to evaluate the hydrologic significance of IMERG.

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

OpenAIRE

Carolien Toté; Domingos Patricio; Hendrik Boogaard; Raymond van der Wijngaart; Elena Tarnavsky; Chris Funk

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 Cl...

Satellite and gauge rainfall merging using geographically weighted regression

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Q. Hu

2015-05-01

Full Text Available A residual-based rainfall merging scheme using geographically weighted regression (GWR has been proposed. This method is capable of simultaneously blending various satellite rainfall data with gauge measurements and could describe the non-stationary influences of geographical and terrain factors on rainfall spatial distribution. Using this new method, an experimental study on merging daily rainfall from the Climate Prediction Center Morphing dataset (CMOROH and gauge measurements was conducted for the Ganjiang River basin, in Southeast China. We investigated the capability of the merging scheme for daily rainfall estimation under different gauge density. Results showed that under the condition of sparse gauge density the merging rainfall scheme is remarkably superior to the interpolation using just gauge data.

Validation of satellite daily rainfall estimates in complex terrain of Bali Island, Indonesia

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Rahmawati, Novi; Lubczynski, Maciek W.

2017-11-01

Satellite rainfall products have different performances in different geographic regions under different physical and climatological conditions. In this study, the objective was to select the most reliable and accurate satellite rainfall products for specific, environmental conditions of Bali Island. The performances of four spatio-temporal satellite rainfall products, i.e., CMORPH25, CMORPH8, TRMM, and PERSIANN, were evaluated at the island, zonation (applying elevation and climatology as constraints), and pixel scales, using (i) descriptive statistics and (ii) categorical statistics, including bias decomposition. The results showed that all the satellite products had low accuracy because of spatial scale effect, daily resolution and the island complexity. That accuracy was relatively lower in (i) dry seasons and dry climatic zones than in wet seasons and wet climatic zones; (ii) pixels jointly covered by sea and mountainous land than in pixels covered by land or by sea only; and (iii) topographically diverse than uniform terrains. CMORPH25, CMORPH8, and TRMM underestimated and PERSIANN overestimated rainfall when comparing them to gauged rain. The CMORPH25 had relatively the best performance and the PERSIANN had the worst performance in the Bali Island. The CMORPH25 had the lowest statistical errors, the lowest miss, and the highest hit rainfall events; it also had the lowest miss rainfall bias and was relatively the most accurate in detecting, frequent in Bali, ≤ 20 mm day-1 rain events. Lastly, the CMORPH25 coarse grid better represented rainfall events from coastal to inlands areas than other satellite products, including finer grid CMORPH8.

Rainfall recharge estimation on a nation-wide scale using satellite information in New Zealand

Science.gov (United States)

Westerhoff, Rogier; White, Paul; Moore, Catherine

2015-04-01

Models of rainfall recharge to groundwater are challenged by the need to combine uncertain estimates of rainfall, evapotranspiration, terrain slope, and unsaturated zone parameters (e.g., soil drainage and hydraulic conductivity of the subsurface). Therefore, rainfall recharge is easiest to estimate on a local scale in well-drained plains, where it is known that rainfall directly recharges groundwater. In New Zealand, this simplified approach works in the policy framework of regional councils, who manage water allocation at the aquifer and sub-catchment scales. However, a consistent overview of rainfall recharge is difficult to obtain at catchment and national scale: in addition to data uncertainties, data formats are inconsistent between catchments; the density of ground observations, where these exist, differs across regions; each region typically uses different local models for estimating recharge components; and different methods and ground observations are used for calibration and validation of these models. The research described in this paper therefore presents a nation-wide approach to estimate rainfall recharge in New Zealand. The method used is a soil water balance approach, with input data from national rainfall and soil and geology databases. Satellite data (i.e., evapotranspiration, soil moisture, and terrain) aid in the improved calculation of rainfall recharge, especially in data-sparse areas. A first version of the model has been implemented on a 1 km x 1 km and monthly scale between 2000 and 2013. A further version will include a quantification of recharge estimate uncertainty: with both "top down" input error propagation methods and catchment-wide "bottom up" assessments of integrated uncertainty being adopted. Using one nation-wide methodology opens up new possibilities: it can, for example, help in more consistent estimation of water budgets, groundwater fluxes, or other hydrological parameters. Since recharge is estimated for the entire land

Application of seasonal rainfall forecasts and satellite rainfall observations to crop yield forecasting for Africa

Science.gov (United States)

Greatrex, H. L.; Grimes, D. I. F.; Wheeler, T. R.

2009-04-01

Rain-fed agriculture is of utmost importance in sub-Saharan Africa; the FAO estimates that over 90% of food consumed in the region is grown in rain-fed farming systems. As the climate in sub-Saharan Africa has a high interannual variability, this dependence on rainfall can leave communities extremely vulnerable to food shortages, especially when coupled with a lack of crop management options. The ability to make a regional forecast of crop yield on a timescale of months would be of enormous benefit; it would enable both governmental and non-governmental organisations to be alerted in advance to crop failure and could facilitate national and regional economic planning. Such a system would also enable individual communities to make more informed crop management decisions, increasing their resilience to climate variability and change. It should be noted that the majority of crops in the region are rainfall limited, therefore the ability to create a seasonal crop forecast depends on the ability to forecast rainfall at a monthly or seasonal timescale and to temporally downscale this to a daily time-series of rainfall. The aim of this project is to develop a regional-scale seasonal forecast for sub-Saharan crops, utilising the General Large Area Model for annual crops (GLAM). GLAM would initially be driven using both dynamical and statistical seasonal rainfall forecasts to provide an initial estimate of crop yield. The system would then be continuously updated throughout the season by replacing the seasonal rainfall forecast with daily weather observations. TAMSAT satellite rainfall estimates are used rather than rain-gauge data due to the scarcity of ground based observations. An important feature of the system is the use of the geo-statistical method of sequential simulation to create an ensemble of daily weather inputs from both the statistical seasonal rainfall forecasts and the satellite rainfall estimates. This allows a range of possible yield outputs to be

Rainfall frequency analysis for ungauged sites using satellite precipitation products

Science.gov (United States)

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

2017-11-01

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

Satellite rainfall retrieval by logistic regression

Science.gov (United States)

Chiu, Long S.

1986-01-01

The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.

Error threshold inference from Global Precipitation Measurement (GPM) satellite rainfall data and interpolated ground-based rainfall measurements in Metro Manila

Science.gov (United States)

Ampil, L. J. Y.; Yao, J. G.; Lagrosas, N.; Lorenzo, G. R. H.; Simpas, J.

2017-12-01

The Global Precipitation Measurement (GPM) mission is a group of satellites that provides global observations of precipitation. Satellite-based observations act as an alternative if ground-based measurements are inadequate or unavailable. Data provided by satellites however must be validated for this data to be reliable and used effectively. In this study, the Integrated Multisatellite Retrievals for GPM (IMERG) Final Run v3 half-hourly product is validated by comparing against interpolated ground measurements derived from sixteen ground stations in Metro Manila. The area considered in this study is the region 14.4° - 14.8° latitude and 120.9° - 121.2° longitude, subdivided into twelve 0.1° x 0.1° grid squares. Satellite data from June 1 - August 31, 2014 with the data aggregated to 1-day temporal resolution are used in this study. The satellite data is directly compared to measurements from individual ground stations to determine the effect of the interpolation by contrast against the comparison of satellite data and interpolated measurements. The comparisons are calculated by taking a fractional root-mean-square error (F-RMSE) between two datasets. The results show that interpolation improves errors compared to using raw station data except during days with very small amounts of rainfall. F-RMSE reaches extreme values of up to 654 without a rainfall threshold. A rainfall threshold is inferred to remove extreme error values and make the distribution of F-RMSE more consistent. Results show that the rainfall threshold varies slightly per month. The threshold for June is inferred to be 0.5 mm, reducing the maximum F-RMSE to 9.78, while the threshold for July and August is inferred to be 0.1 mm, reducing the maximum F-RMSE to 4.8 and 10.7, respectively. The maximum F-RMSE is reduced further as the threshold is increased. Maximum F-RMSE is reduced to 3.06 when a rainfall threshold of 10 mm is applied over the entire duration of JJA. These results indicate that

An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa

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Margaret Wambui Kimani

2017-05-01

Full Text Available Accurate and consistent rainfall observations are vital for climatological studies in support of better agricultural and water management decision-making and planning. In East Africa, accurate rainfall estimation with an adequate spatial distribution is limited due to sparse rain gauge networks. Satellite rainfall products can potentially play a role in increasing the spatial coverage of rainfall estimates; however, their performance needs to be understood across space–time scales and factors relating to their errors. This study assesses the performance of seven satellite products: Tropical Applications of Meteorology using Satellite and ground-based observations (TAMSAT, African Rainfall Climatology And Time series (TARCAT, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS, Tropical Rainfall Measuring Mission (TRMM-3B43, Climate Prediction Centre (CPC Morphing technique (CMORPH, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR, CPC Merged Analysis of Precipitation (CMAP, and Global Precipitation Climatology Project (GPCP, using locally developed gridded (0.05° rainfall data for 15 years (1998–2012 over East Africa. The products’ assessments were done at monthly and yearly timescales and were remapped to the gridded rain gauge data spatial scale during the March to May (MAM and October to December (OND rainy seasons. A grid-based statistical comparison between the two datasets was used, but only pixel values located at the rainfall stations were considered for validation. Additionally, the impact of topography on the performance of the products was assessed by analyzing the pixels in areas of highest negative bias. All the products could substantially replicate rainfall patterns, but their differences are mainly based on retrieving high rainfall amounts, especially of localized orographic types. The products exhibited systematic errors, which

Satellite rainfall monitoring over Africa for food security, using multi-channel MSG data

Science.gov (United States)

Chadwick, R.; Grimes, D.; Saunders, R.; Blackmore, T.; Francis, P.

2009-04-01

Near real-time rainfall estimates are crucial in sub-Saharan Africa for a variety of humanitarian and agricultural purposes. However, for economic and infrastructural reasons, regularly reporting rain-gauges are sparse and precipitation radar networks extremely rare. Satellite rainfall estimates, particularly from geostationary satellites such as Meteosat Second Generation (MSG), present one method of filling this information gap, as they produce data at high temporal and spatial resolution. An algorithm has been developed to produce rainfall estimates for Africa from multi-channel MSG data. The algorithm is calibrated using precipitation radar data collected in Niamey, Niger as part of the African Monsoon Multidisciplinary Analyses (AMMA) project in 2006, and is based on an algorithm used operationally over Europe by the UK Met Office. Contingency tables are used to establish a statistical relationship between multi-channel MSG data and probability of rainfall at several different rain-rate magnitudes as sensed by the radar. Rain-rate estimates can then be produced at a variety of spatial and temporal scales, with MSG scan length (15 minutes) and pixel size (3-4km) as the lower limit. Results will be presented of a validation of this algorithm over the Sahel region of Africa. Rainfall estimates from this algorithm, processed for 2004, will be validated against gridded rain-gauge data at a 0.5 degree and 10 day timescale suitable for drought monitoring purposes. A comparison will also be made against rainfall estimates from the TAMSAT algorithm, which uses single channel IR data from MSG, and has been shown to perform well in the Sahel region.

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

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Saoussen Dhib

2017-06-01

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

On the Characterization of Rainfall Associated with U.S. Landfalling North Atlantic Tropical Cyclones Based on Satellite Data and Numerical Weather Prediction Outputs

Science.gov (United States)

Luitel, B. N.; Villarini, G.; Vecchi, G. A.

2014-12-01

When we talk about tropical cyclones (TCs), the first things that come to mind are strong winds and storm surge affecting the coastal areas. However, according to the Federal Emergency Management Agency (FEMA) 59% of the deaths caused by TCs since 1970 is due to fresh water flooding. Heavy rainfall associated with TCs accounts for 13% of heavy rainfall events nationwide for the June-October months, with this percentage being much higher if the focus is on the eastern and southern United States. This study focuses on the evaluation of precipitation associated with the North Atlantic TCs that affected the continental United States over the period 2007 - 2012. We evaluate the rainfall associated with these TCs using four satellite based rainfall products: Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA; both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); Climate Prediction Center (CPC) MORPHing technique (CMORPH). As a reference data we use gridded rainfall provided by CPC (Daily US Unified Gauge-Based Analysis of Precipitation). Rainfall fields from each of these satellite products are compared to the reference data, providing valuable information about the realism of these products in reproducing the rainfall associated with TCs affecting the continental United States. In addition to the satellite products, we evaluate the forecasted rainfall produced by five state-of-the-art numerical weather prediction (NWP) models: European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC). The skill of these models in reproducing TC rainfall is quantified for different lead times, and discussed in light of the performance of the satellite products.

Multiple data fusion for rainfall estimation using a NARX-based recurrent neural network – the development of the REIINN model

International Nuclear Information System (INIS)

Ang, M R C O; Gonzalez, R M; Castro, P P M

2014-01-01

Rainfall, one of the important elements of the hydrologic cycle, is also the most difficult to model. Thus, accurate rainfall estimation is necessary especially in localized catchment areas where variability of rainfall is extremely high. Moreover, early warning of severe rainfall through timely and accurate estimation and forecasting could help prevent disasters from flooding. This paper presents the development of two rainfall estimation models that utilize a NARX-based neural network architecture namely: REIINN 1 and REIINN 2. These REIINN models, or Rainfall Estimation by Information Integration using Neural Networks, were trained using MTSAT cloud-top temperature (CTT) images and rainfall rates from the combined rain gauge and TMPA 3B40RT datasets. Model performance was assessed using two metrics – root mean square error (RMSE) and correlation coefficient (R). REIINN 1 yielded an RMSE of 8.1423 mm/3h and an overall R of 0.74652 while REIINN 2 yielded an RMSE of 5.2303 and an overall R of 0.90373. The results, especially that of REIINN 2, are very promising for satellite-based rainfall estimation in a catchment scale. It is believed that model performance and accuracy will greatly improve with a denser and more spatially distributed in-situ rainfall measurements to calibrate the model with. The models proved the viability of using remote sensing images, with their good spatial coverage, near real time availability, and relatively inexpensive to acquire, as an alternative source for rainfall estimation to complement existing ground-based measurements

Connecting Satellite-Based Precipitation Estimates to Users

Science.gov (United States)

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

2018-01-01

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

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

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

Science.gov (United States)

Kirschbaum, Dalia; Stanley, Thomas

2018-03-01

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

Constraining relationships between rainfall and landsliding with satellite derived rainfall measurements and landslide inventories.

Science.gov (United States)

Marc, Odin; Malet, Jean-Philippe; Stumpf, Andre; Gosset, Marielle

2017-04-01

In mountainous and hilly regions, landslides are an important source of damage and fatalities. Landsliding correlates with extreme rainfall events and may increase with climate change. Still, how precipitation drives landsliding at regional scales is poorly understood quantitatively in part because constraining simultaneously landsliding and rainfall across large areas is challenging. By combining optical images acquired from satellite observation platforms and rainfall measurements from satellite constellations we are building a database of landslide events caused by with single storm events. We present results from storm-induced landslides from Brazil, Taiwan, Micronesia, Central America, Europe and the USA. We present scaling laws between rainfall metrics derived by satellites (total rainfall, mean intensity, antecedent rainfall, ...) and statistical descriptors of landslide events (total area and volume, size distribution, mean runout, ...). Total rainfall seems to be the most important parameter driving non-linearly the increase in total landslide number, and area and volume. The maximum size of bedrock landslides correlates with the total number of landslides, and thus with total rainfall, within the limits of available topographic relief. In contrast, the power-law scaling exponent of the size distribution, controlling the relative abundance of small and large landslides, appears rather independent of the rainfall metrics (intensity, duration and total rainfall). These scaling laws seem to explain both the intra-storm pattern of landsliding, at the scale of satellite rainfall measurements ( 25kmx25km), and the different impacts observed for various storms. Where possible, we evaluate the limits of standard rainfall products (TRMM, GPM, GSMaP) by comparing them to in-situ data. Then we discuss how slope distribution and other geomorphic factors (lithology, soil presence,...) modulate these scaling laws. Such scaling laws at the basin scale and based only on a

TRMM Satellite Algorithm Estimates to Represent the Spatial Distribution of Rainstorms

Directory of Open Access Journals (Sweden)

Patrick Marina

2017-01-01

Full Text Available On-site measurements from rain gauge provide important information for the design, construction, and operation of water resources engineering projects, groundwater potentials, and the water supply and irrigation systems. A dense gauging network is needed to accurately characterize the variation of rainfall over a region, unfitting for conditions with limited networks, such as in Sarawak, Malaysia. Hence, satellite-based algorithm estimates are introduced as an innovative solution to these challenges. With accessibility to dataset retrievals from public domain websites, it has become a useful source to measure rainfall for a wider coverage area at finer temporal resolution. This paper aims to investigate the rainfall estimates prepared by Tropical Rainfall Measuring Mission (TRMM to explain whether it is suitable to represent the distribution of extreme rainfall in Sungai Sarawak Basin. Based on the findings, more uniform correlations for the investigated storms can be observed for low to medium altitude (>40 MASL. It is found for the investigated events of Jan 05-11, 2009: the normalized root mean square error (NRMSE = 36.7 %; and good correlation (CC = 0.9. These findings suggest that satellite algorithm estimations from TRMM are suitable to represent the spatial distribution of extreme rainfall.

Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia

Science.gov (United States)

Brocca, Luca; Pellarin, Thierry; Crow, Wade T.; Ciabatta, Luca; Massari, Christian; Ryu, Dongryeol; Su, Chun-Hsu; Rüdiger, Christoph; Kerr, Yann

2016-10-01

Remote sensing of soil moisture has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) satellite is used for improving satellite rainfall estimates obtained from the Tropical Rainfall Measuring Mission multisatellite precipitation analysis product (TMPA) using three different "bottom up" techniques: SM2RAIN, Soil Moisture Analysis Rainfall Tool, and Antecedent Precipitation Index Modification. The implementation of these techniques aims at improving the well-known "top down" rainfall estimate derived from TMPA products (version 7) available in near real time. Ground observations provided by the Australian Water Availability Project are considered as a separate validation data set. The three algorithms are calibrated against the gauge-corrected TMPA reanalysis product, 3B42, and used for adjusting the TMPA real-time product, 3B42RT, using SMOS soil moisture data. The study area covers the entire Australian continent, and the analysis period ranges from January 2010 to November 2013. Results show that all the SMOS-based rainfall products improve the performance of 3B42RT, even at daily time scale (differently from previous investigations). The major improvements are obtained in terms of estimation of accumulated rainfall with a reduction of the root-mean-square error of more than 25%. Also, in terms of temporal dynamic (correlation) and rainfall detection (categorical scores) the SMOS-based products provide slightly better results with respect to 3B42RT, even though the relative performance between the methods is not always the same. The strengths and weaknesses of each algorithm and the spatial variability of their performances are identified in order to indicate the ways forward for this promising research activity. Results show that the integration of bottom up and top down approaches

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Science.gov (United States)

Tesfaye Ayehu, Getachew; Tadesse, Tsegaye; Gessesse, Berhan; Dinku, Tufa

2018-04-01

Accurate measurement of rainfall is vital to analyze the spatial and temporal patterns of precipitation at various scales. However, the conventional rain gauge observations in many parts of the world such as Ethiopia are sparse and unevenly distributed. An alternative to traditional rain gauge observations could be satellite-based rainfall estimates. Satellite rainfall estimates could be used as a sole product (e.g., in areas with no (or poor) ground observations) or through integrating with rain gauge measurements. In this study, the potential of a newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall product has been evaluated in comparison to rain gauge data over the Upper Blue Nile basin in Ethiopia for the period of 2000 to 2015. In addition, the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT 3) and the African Rainfall Climatology (ARC 2) products have been used as a benchmark and compared with CHIRPS. From the overall analysis at dekadal (10 days) and monthly temporal scale, CHIRPS exhibited better performance in comparison to TAMSAT 3 and ARC 2 products. An evaluation based on categorical/volumetric and continuous statistics indicated that CHIRPS has the greatest skills in detecting rainfall events (POD = 0.99, 1.00) and measure of volumetric rainfall (VHI = 1.00, 1.00), the highest correlation coefficients (r = 0.81, 0.88), better bias values (0.96, 0.96), and the lowest RMSE (28.45 mm dekad-1, 59.03 mm month-1) than TAMSAT 3 and ARC 2 products at dekadal and monthly analysis, respectively. CHIRPS overestimates the frequency of rainfall occurrence (up to 31 % at dekadal scale), although the volume of rainfall recorded during those events was very small. Indeed, TAMSAT 3 has shown a comparable performance with that of the CHIRPS product, mainly with regard to bias. The ARC 2 product was found to have the weakest performance underestimating rain gauge observed rainfall by

Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals

Science.gov (United States)

Meyer, Hanna; Kühnlein, Meike; Appelhans, Tim; Nauss, Thomas

2016-03-01

Machine learning (ML) algorithms have successfully been demonstrated to be valuable tools in satellite-based rainfall retrievals which show the practicability of using ML algorithms when faced with high dimensional and complex data. Moreover, recent developments in parallel computing with ML present new possibilities for training and prediction speed and therefore make their usage in real-time systems feasible. This study compares four ML algorithms - random forests (RF), neural networks (NNET), averaged neural networks (AVNNET) and support vector machines (SVM) - for rainfall area detection and rainfall rate assignment using MSG SEVIRI data over Germany. Satellite-based proxies for cloud top height, cloud top temperature, cloud phase and cloud water path serve as predictor variables. The results indicate an overestimation of rainfall area delineation regardless of the ML algorithm (averaged bias = 1.8) but a high probability of detection ranging from 81% (SVM) to 85% (NNET). On a 24-hour basis, the performance of the rainfall rate assignment yielded R2 values between 0.39 (SVM) and 0.44 (AVNNET). Though the differences in the algorithms' performance were rather small, NNET and AVNNET were identified as the most suitable algorithms. On average, they demonstrated the best performance in rainfall area delineation as well as in rainfall rate assignment. NNET's computational speed is an additional advantage in work with large datasets such as in remote sensing based rainfall retrievals. However, since no single algorithm performed considerably better than the others we conclude that further research in providing suitable predictors for rainfall is of greater necessity than an optimization through the choice of the ML algorithm.

An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa

OpenAIRE

Kimani, M.W.; Hoedjes, Johannes Cornelis Bernardus; Su, Z.

2017-01-01

Accurate and consistent rainfall observations are vital for climatological studies in support of better agricultural and water management decision-making and planning. In East Africa, accurate rainfall estimation with an adequate spatial distribution is limited due to sparse rain gauge networks. Satellite rainfall products can potentially play a role in increasing the spatial coverage of rainfall estimates; however, their performance needs to be understood across space–time scales and factors...

The Variation of Tropical Cyclone Rainfall within the North Atlantic and Pacific as Observed from Satellites

Science.gov (United States)

Rodgers, Edward; Pierce, Harold; Adler, Robert

1999-01-01

Tropical cyclone monthly rainfall amounts are estimated from passive microwave satellite observations in the North Atlantic and in three equal geographical regions of the North Pacific (i.e., Western, Central, and Eastern North Pacific). These satellite-derived rainfall amounts are used to assess the impact of tropical cyclone rainfall in altering the geographical, seasonal, and inter-annual distribution of the 1987-1989, 1991-1998 North Atlantic and Pacific rainfall during June-November when tropical cyclones are most abundant. To estimate these tropical cyclone rainfall amounts, mean monthly rain rates are derived from the Defence Meteorological Satellite Program (DMSP) Special Sensor Microwave/ Radiometer (SSM/I) observations within 444 km radius of the center of those North Atlantic and Pacific tropical cyclones that reached storm stage and greater. These rain rate observations are then multiplied by the number of hours in a given month. Mean monthly rainfall amounts are also constructed for all the other North Atlantic and Pacific raining systems during this eleven year period for the purpose of estimating the geographical distribution and intensity of rainfall contributed by non-tropical cyclone systems. Further, the combination of the non-tropical cyclone and tropical cyclone (i.e., total) rainfall is constructed to delineate the fractional amount that tropical cyclones contributed to the total North Pacific rainfall.

Tropical Rainfall Measuring Mission (TRMM) and the Future of Rainfall Estimation from Space

Science.gov (United States)

Kakar, Ramesh; Adler, Robert; Smith, Eric; Starr, David OC. (Technical Monitor)

2001-01-01

Tropical rainfall is important in the hydrological cycle and to the lives and welfare of humans. Three-fourths of the energy that drives the atmospheric wind circulation comes from the latent heat released by tropical precipitation. Recognizing the importance of rain in the tropics, NASA for the U.S.A. and NASDA for Japan have partnered in the design, construction and flight of a satellite mission to measure tropical rainfall and calculate the associated latent heat release. The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on November 27, 1997, and data from all the instruments first became available approximately 30 days after launch. Since then, much progress has been made in the calibration of the sensors, the improvement of the rainfall algorithms and applications of these results to areas such as Data Assimilation and model initialization. TRMM has reduced the uncertainty of climatological rainfall in tropics by over a factor of two, therefore establishing a standard for comparison with previous data sets and climatologies. It has documented the diurnal variation of precipitation over the oceans, showing a distinct early morning peak and this satellite mission has shown the utility of precipitation information for the improvement of numerical weather forecasts and climate modeling. This paper discusses some promising applications using TRMM data and introduces a measurement concept being discussed by NASA/NASDA and ESA for the future of rainfall estimation from space.

Improved rainfall estimation over the Indian monsoon region by synergistic use of Kalpana-1 and rain gauge data

OpenAIRE

Gairola, R. M.; Prakash, Satya; Pal, P. K.

2015-01-01

In this paper, an attempt has been made to estimate rainfall over the Indian monsoon region by the synergistic use of the geostationary Kalpana-1 satellite-derived INSAT Multispectral Rainfall Algorithm (IMSRA) rainfall estimates and rain gauge data, using a successive correction method in order to further refine the operational IMSRA rainfall estimates. The successive correction method benefits from high spatial and temporal resolutions of the Kalpana-1 satellite and accurate rainfall estima...

Performance of High Resolution Satellite Rainfall Products over Data Scarce Parts of Eastern Ethiopia

Directory of Open Access Journals (Sweden)

Shimelis B. Gebere

2015-09-01

Full Text Available Accurate estimation of rainfall in mountainous areas is necessary for various water resource-related applications. Though rain gauges accurately measure rainfall, they are rarely found in mountainous regions and satellite rainfall data can be used as an alternative source over these regions. This study evaluated the performance of three high-resolution satellite rainfall products, the Tropical Rainfall Measuring Mission (TRMM 3B42, the Global Satellite Mapping of Precipitation (GSMaP_MVK+, and the Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Networks (PERSIANN at daily, monthly, and seasonal time scales against rain gauge records over data-scarce parts of Eastern Ethiopia. TRMM 3B42 rain products show relatively better performance at the three time scales, while PERSIANN did much better than GSMaP. At the daily time scale, TRMM correctly detected 88% of the rainfall from the rain gauge. The correlation at the monthly time scale also revealed that the TRMM has captured the observed rainfall better than the other two. For Belg (short rain and Kiremt (long rain seasons, the TRMM did better than the others by far. However, during Bega (dry season, PERSIANN showed a relatively good estimate. At all-time scales, noticing the bias, TRMM tends to overestimate, while PERSIANN and GSMaP tend to underestimate the rainfall. The overall result suggests that monthly and seasonal TRMM rainfall performed better than daily rainfall. It has also been found that both GSMaP and PERSIANN performed better in relatively flat areas than mountainous areas. Before the practical use of TRMM, the RMSE value needs to be improved by considering the topography of the study area or adjusting the bias.

Technical Report Series on Global Modeling and Data Assimilation. Volume 12; Comparison of Satellite Global Rainfall Algorithms

Science.gov (United States)

Suarez, Max J. (Editor); Chang, Alfred T. C.; Chiu, Long S.

1997-01-01

Seventeen months of rainfall data (August 1987-December 1988) from nine satellite rainfall algorithms (Adler, Chang, Kummerow, Prabhakara, Huffman, Spencer, Susskind, and Wu) were analyzed to examine the uncertainty of satellite-derived rainfall estimates. The variability among algorithms, measured as the standard deviation computed from the ensemble of algorithms, shows regions of high algorithm variability tend to coincide with regions of high rain rates. Histograms of pattern correlation (PC) between algorithms suggest a bimodal distribution, with separation at a PC-value of about 0.85. Applying this threshold as a criteria for similarity, our analyses show that algorithms using the same sensor or satellite input tend to be similar, suggesting the dominance of sampling errors in these satellite estimates.

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Directory of Open Access Journals (Sweden)

G. T. Ayehu

2018-04-01

Full Text Available Accurate measurement of rainfall is vital to analyze the spatial and temporal patterns of precipitation at various scales. However, the conventional rain gauge observations in many parts of the world such as Ethiopia are sparse and unevenly distributed. An alternative to traditional rain gauge observations could be satellite-based rainfall estimates. Satellite rainfall estimates could be used as a sole product (e.g., in areas with no (or poor ground observations or through integrating with rain gauge measurements. In this study, the potential of a newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS rainfall product has been evaluated in comparison to rain gauge data over the Upper Blue Nile basin in Ethiopia for the period of 2000 to 2015. In addition, the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT 3 and the African Rainfall Climatology (ARC 2 products have been used as a benchmark and compared with CHIRPS. From the overall analysis at dekadal (10 days and monthly temporal scale, CHIRPS exhibited better performance in comparison to TAMSAT 3 and ARC 2 products. An evaluation based on categorical/volumetric and continuous statistics indicated that CHIRPS has the greatest skills in detecting rainfall events (POD  =  0.99, 1.00 and measure of volumetric rainfall (VHI  =  1.00, 1.00, the highest correlation coefficients (r =  0.81, 0.88, better bias values (0.96, 0.96, and the lowest RMSE (28.45 mm dekad−1, 59.03 mm month−1 than TAMSAT 3 and ARC 2 products at dekadal and monthly analysis, respectively. CHIRPS overestimates the frequency of rainfall occurrence (up to 31 % at dekadal scale, although the volume of rainfall recorded during those events was very small. Indeed, TAMSAT 3 has shown a comparable performance with that of the CHIRPS product, mainly with regard to bias. The ARC 2 product was found to have the weakest performance

Rainfall: State of the Science

Science.gov (United States)

Testik, Firat Y.; Gebremichael, Mekonnen

Rainfall: State of the Science offers the most up-to-date knowledge on the fundamental and practical aspects of rainfall. Each chapter, self-contained and written by prominent scientists in their respective fields, provides three forms of information: fundamental principles, detailed overview of current knowledge and description of existing methods, and emerging techniques and future research directions. The book discusses • Rainfall microphysics: raindrop morphodynamics, interactions, size distribution, and evolution • Rainfall measurement and estimation: ground-based direct measurement (disdrometer and rain gauge), weather radar rainfall estimation, polarimetric radar rainfall estimation, and satellite rainfall estimation • Statistical analyses: intensity-duration-frequency curves, frequency analysis of extreme events, spatial analyses, simulation and disaggregation, ensemble approach for radar rainfall uncertainty, and uncertainty analysis of satellite rainfall products The book is tailored to be an indispensable reference for researchers, practitioners, and graduate students who study any aspect of rainfall or utilize rainfall information in various science and engineering disciplines.

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

NARCIS (Netherlands)

Tote, C.; Patricio, D.; Boogaard, H.L.; Wijngaart, van der R.; Tarnavsky, E.; Funk, 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

Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile

Directory of Open Access Journals (Sweden)

Emad Habib

2014-07-01

Full Text Available Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume.

Contribution of Tropical Cyclones to the North Pacific Climatological Rainfall as Observed from Satellites.

Science.gov (United States)

Rodgers, Edward B.; Adler, Robert F.; Pierce, Harold F.

2000-10-01

Tropical cyclone monthly rainfall amounts are estimated from passive microwave satellite observations for an 11-yr period. These satellite-derived rainfall amounts are used to assess the impact of tropical cyclone rainfall in altering the geographical, seasonal, and interannual distribution of the North Pacific Ocean total rainfall during June-November when tropical cyclones are most important.To estimate these tropical cyclone rainfall amounts, mean monthly rain rates are derived from passive microwave satellite observations within 444-km radius of the center of those North Pacific tropical cyclones that reached storm stage and greater. These rain-rate observations are converted to monthly rainfall amounts and then compared with those for nontropical cyclone systems.The main results of this study indicate that 1) tropical cyclones contribute 7% of the rainfall to the entire domain of the North Pacific during the tropical cyclone season and 12%, 3%, and 4% when the study area is limited to, respectively, the western, central, and eastern third of the ocean; 2) the maximum tropical cyclone rainfall is poleward (5°-10° latitude depending on longitude) of the maximum nontropical cyclone rainfall; 3) tropical cyclones contribute a maximum of 30% northeast of the Philippine Islands and 40% off the lower Baja California coast; 4) in the western North Pacific, the tropical cyclone rainfall lags the total rainfall by approximately two months and shows seasonal latitudinal variation following the Intertropical Convergence Zone; and 5) in general, tropical cyclone rainfall is enhanced during the El Niño years by warm SSTs in the eastern North Pacific and by the monsoon trough in the western and central North Pacific.

A Merging Framework for Rainfall Estimation at High Spatiotemporal Resolution for Distributed Hydrological Modeling in a Data-Scarce Area

Directory of Open Access Journals (Sweden)

Yinping Long

2016-07-01

Full Text Available Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. The objective of the study is to present a satellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. In the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25° daily rainfall field derived from the Tropical Rainfall Measuring Mission (TRMM Multisatellite Precipitation Analysis (TMPA precipitation product version 7. The nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled TRMM and gauged rainfall with minimum cross-validation error. An indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. The framework was applied to estimate daily precipitation at a 1 km resolution in the Qinghai Lake Basin, a data-scarce area in the northeast of the Qinghai-Tibet Plateau. The final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (GBHM. The proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas.

Evaluation of sub daily satellite rainfall estimates through flash flood modelling in the Lower Middle Zambezi Basin

Directory of Open Access Journals (Sweden)

T. Matingo

2018-05-01

Full Text Available Flash floods are experienced almost annually in the ungauged Mbire District of the Middle Zambezi Basin. Studies related to hydrological modelling (rainfall-runoff and flood forecasting require major inputs such as precipitation which, due to shortage of observed data, are increasingly using indirect methods for estimating precipitation. This study therefore evaluated performance of CMORPH and TRMM satellite rainfall estimates (SREs for 30 min, 1 h, 3 h and daily intensities through hydrologic and flash flood modelling in the Lower Middle Zambezi Basin for the period 2013–2016. On a daily timestep, uncorrected CMORPH and TRMM show Probability of Detection (POD of 61 and 59 %, respectively, when compared to rain gauge observations. The best performance using Correlation Coefficient (CC was 70 and 60 % on daily timesteps for CMORPH and TRMM, respectively. The best RMSE for CMORPH was 0.81 % for 30 min timestep and for TRMM was 2, 11 % on 3 h timestep. For the year 2014 to 2015, the HEC-HMS (Hydrological Engineering Centre-Hydrological Modelling System daily model calibration Nash Sutcliffe efficiency (NSE for Musengezi sub catchment was 59 % whilst for Angwa it was 55 %. Angwa sub-catchment daily NSE results for the period 2015–2016 was 61 %. HEC-RAS flash flood modeling at 100, 50 and 25 year return periods for Angwa sub catchment, inundated 811 and 867 ha for TRMM rainfall simulated discharge at 3 h and daily timesteps, respectively. For CMORPH generated rainfall, the inundation was 818, 876, 890 and 891 ha at daily, 3 h, 1 h and 30 min timesteps. The 30 min time step for CMORPH effectively captures flash floods with the measure of agreement between simulated flood extent and ground control points of 69 %. For TRMM, the 3 h timestep effectively captures flash floods with coefficient of 67 %. The study therefore concludes that satellite products are most effective in capturing localized

Performance of bias corrected MPEG rainfall estimate for rainfall-runoff simulation in the upper Blue Nile Basin, Ethiopia

Science.gov (United States)

Worqlul, Abeyou W.; Ayana, Essayas K.; Maathuis, Ben H. P.; MacAlister, Charlotte; Philpot, William D.; Osorio Leyton, Javier M.; Steenhuis, Tammo S.

2018-01-01

In many developing countries and remote areas of important ecosystems, good quality precipitation data are neither available nor readily accessible. Satellite observations and processing algorithms are being extensively used to produce satellite rainfall products (SREs). Nevertheless, these products are prone to systematic errors and need extensive validation before to be usable for streamflow simulations. In this study, we investigated and corrected the bias of Multi-Sensor Precipitation Estimate-Geostationary (MPEG) data. The corrected MPEG dataset was used as input to a semi-distributed hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) for simulation of discharge of the Gilgel Abay and Gumara watersheds in the Upper Blue Nile basin, Ethiopia. The result indicated that the MPEG satellite rainfall captured 81% and 78% of the gauged rainfall variability with a consistent bias of underestimating the gauged rainfall by 60%. A linear bias correction applied significantly reduced the bias while maintaining the coefficient of correlation. The simulated flow using bias corrected MPEG SRE resulted in a simulated flow comparable to the gauge rainfall for both watersheds. The study indicated the potential of MPEG SRE in water budget studies after applying a linear bias correction.

Contributions of Tropical Cyclones to the North Atlantic Climatological Rainfall as Observed from Satellites

Science.gov (United States)

Rodgers, Edward B.; Adler, Robert F.; Pierce, Harold F.; Einaudi, Franco (Technical Monitor)

2000-01-01

The tropical cyclone rainfall climatology study that was performed for the North Pacific was extended to the North Atlantic. Similar to the North Pacific tropical cyclone study, mean monthly rainfall within 444 km of the center of the North Atlantic tropical cyclones (i.e., that reached storm stage and greater) was estimated from passive microwave satellite observations during, an eleven year period. These satellite-observed rainfall estimates were used to assess the impact of tropical cyclone rainfall in altering the geographical, seasonal, and inter-annual distribution of the North Atlantic total rainfall during, June-November when tropical cyclones were most abundant. The main results from this study indicate: 1) that tropical cyclones contribute, respectively, 4%, 3%, and 4% to the western, eastern, and entire North Atlantic; 2) similar to that observed in the North Pacific, the maximum in North Atlantic tropical cyclone rainfall is approximately 5 - 10 deg poleward (depending on longitude) of the maximum non-tropical cyclone rainfall; 3) tropical cyclones contribute regionally a maximum of 30% of the total rainfall 'northeast of Puerto Rico, within a region near 15 deg N 55 deg W, and off the west coast of Africa; 4) there is no lag between the months with maximum tropical cyclone rainfall and non-tropical cyclone rainfall in the western North Atlantic, while in the eastern North Atlantic, maximum tropical cyclone rainfall precedes maximum non-tropical cyclone rainfall; 5) like the North Pacific, North Atlantic tropical cyclones Of hurricane intensity generate the greatest amount of rainfall in the higher latitudes; and 6) warm ENSO events inhibit tropical cyclone rainfall.

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

Science.gov (United States)

Poon, P.; Kinoshita, A. M.

2017-12-01

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

INTEGRATION OF SATELLITE RAINFALL DATA AND CURVE NUMBER METHOD FOR RUNOFF ESTIMATION UNDER SEMI-ARID WADI SYSTEM

Directory of Open Access Journals (Sweden)

E. O. Adam

2017-11-01

Full Text Available The arid and semi-arid catchments in dry lands in general require a special effective management as the scarcity of resources and information which is needed to leverage studies and investigations is the common characteristic. Hydrology is one of the most important elements in the management of resources. Deep understanding of hydrological responses is the key towards better planning and land management. Surface runoff quantification of such ungauged semi-arid catchments considered among the important challenges. A 7586 km2 catchment under investigation is located in semi-arid region in central Sudan where mean annual rainfall is around 250 mm and represent the ultimate source for water supply. The objective is to parameterize hydrological characteristics of the catchment and estimate surface runoff using suitable methods and hydrological models that suit the nature of such ungauged catchments with scarce geospatial information. In order to produce spatial runoff estimations, satellite rainfall was used. Remote sensing and GIS were incorporated in the investigations and the generation of landcover and soil information. Five days rainfall event (50.2 mm was used for the SCS CN model which is considered the suitable for this catchment, as SCS curve number (CN method is widely used for estimating infiltration characteristics depending on the landcover and soil property. Runoff depths of 3.6, 15.7 and 29.7 mm were estimated for the three different Antecedent Moisture Conditions (AMC-I, AMC-II and AMC-III. The estimated runoff depths of AMCII and AMCIII indicate the possibility of having small artificial surface reservoirs that could provide water for domestic and small household agricultural use.

Inter-comparison of Rainfall Estimation from Radar and Satellite During 2016 June 23 Yancheng Tornado Event over Eastern China

Science.gov (United States)

Huang, C.; Chen, S.; Liang, Z.; Hu, B.

2017-12-01

ABSTRACT: On the afternoon of June 23, 2016, Yancheng city in eastern China was hit by a severe thunderstorm that produced a devastating tornado. This tornado was ranked as an EF4 on the Enhanced Fujita scale by China Meteorological Administration, and killed at least 99 people and injured 846 others (152 seriously). This study evaluates rainfall estimates from ground radar network and four satellite algorithms with a relatively dense rain gauge network over eastern China including Jiangsu province and its adjacent regions for the Yancheng June 23 Tornado extreme convective storm in different spatiotemporal scales (from 0.04° to 0.1° and hourly to event total accumulation). The radar network is composed of about 6 S-band Doppler weather radars. Satellite precipitation products include Integrated Multi-satellitE Retrievals for GPM (IMERG), Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), and Global Satellite Mapping of Precipitation (GSMap). Relative Bias (RB), Root-Mean-Squared Error (RMSE), Correlation Coefficient (CC), Probability Of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) are used to quantify the performance of these precipitation products.

Validation of the TRMM Multi Satellite Rainfall Product 3B42 and estimation of scavenging coefficients for (131)I and (137)Cs using TRMM 3B42 rainfall data.

Science.gov (United States)

Shrivastava, R; Dash, S K; Hegde, M N; Pradeepkumar, K S; Sharma, D N

2014-12-01

The TRMM rainfall product 3B42 is compared with rain gauge observations for Kaiga, India on monthly and seasonal time scales. This comparison is carried out for the years 2004-2007 spanning four monsoon seasons. A good correlation is obtained between the two data sets however; magnitude wise, the cumulative precipitation of the satellite product on monthly and seasonal time scales is deficient by almost 33-40% as compared to the rain gauge data. The satellite product is also compared with APHRODITE's Monsoon Asia data set on the same time scales. This comparison indicates a much better agreement since both these data sets represent an average precipitation over the same area. The scavenging coefficients for (131)I and (137)Cs are estimated using TRMM 3B42, rain gauge and APHRODITE data. The values obtained using TRMM 3B42 rainfall data compare very well with those obtained using rain gauge and APHRODITE data. Copyright © 2014 Elsevier Ltd. All rights reserved.

Rainfall Imprint on Sea Surface Salinity in the ITCZ: new satellite perspectives

Science.gov (United States)

Boutin, J.; Viltard, N.; Supply, A.; Martin, N.; Vergely, J. L.; Hénocq, C.; Reverdin, G. P.

2016-02-01

The European Soil Moisture and Ocean Salinity (SMOS) satellite mission monitors sea surface salinity (SSS) over the global ocean for more than 5 years since 2010. The MADRAS microwave radiometer carried by the French (CNES) Indian (ISRO) satellite mission Megha-Tropiques sampled the 30° N-30° S region end of 2011 and in 2012, very complementary to other Global Precipitation Measurement(GPM) missions. In tropical regions, SMOS SSS contains a large imprint of atmospheric rainfall, but is also likely affected by oceanographic processes (advection and diffusion). At local and short time scales, Boutin et al. (2013, 2014) have shown that the spatio-temporal variability of SSS is dominated by rainfall as detected by satellite microwave radiometers and have demonstrated a close to linear relationship between SMOS SSS freshening under rain cells and satellite rain rate. The order of magnitude is in remarkable agreement with the theoretical renewal model of Schlussel et al. (1997) and compatible with AQUARIUS SSS observations, as well as with in situ drifters observations although the latter are local and taken at 45cm depth while satellite L-band SSS roughly correspond to the top 1cm depth and are spatially integrated over 43-150km. It is thus expected that the combined information of satellite rain rates and satellite SSS brings new constraints on the precipitation budget. We first look at the consistency between the spatial structures of SMOS SSS decrease and of rain rates derived either from the MADRAS microwave radiometer or from the CMORPH combined products that do not use MADRAS rain rates. This provides an indirect validation of the rain rates estimates. We then investigate the impact of rain history and of wind speed on the observed SMOS freshening. Based on these results, we discuss the precision on various precipitation estimates over 2012 in the ITCZ region and the major sources of uncertainties that the SPURS2 campaign could help to resolve.

Using Convective Stratiform Technique (CST) method to estimate rainfall (case study in Bali, December 14th 2016)

Science.gov (United States)

Vista Wulandari, Ayu; Rizki Pratama, Khafid; Ismail, Prayoga

2018-05-01

Accurate and realtime data in wide spatial space at this time is still a problem because of the unavailability of observation of rainfall in each region. Weather satellites have a very wide range of observations and can be used to determine rainfall variability with better resolution compared with a limited direct observation. Utilization of Himawari-8 satellite data in estimating rainfall using Convective Stratiform Technique (CST) method. The CST method is performed by separating convective and stratiform cloud components using infrared channel satellite data. Cloud components are classified by slope because the physical and dynamic growth processes are very different. This research was conducted in Bali area on December 14, 2016 by verifying the result of CST process with rainfall data from Ngurah Rai Meteorology Station Bali. It is found that CST method result had simililar value with data observation in Ngurah Rai meteorological station, so it assumed that CST method can be used for rainfall estimation in Bali region.

The all-year rainfall region of South Africa: Satellite rainfall-estimate perspective

CSIR Research Space (South Africa)

Engelbrecht, CJ

2012-09-01

Full Text Available Climate predictability and variability studies over South Africa typically focus on the summer rainfall region and to a lesser extent on the winter rainfall region. The all-year rainfall region of South Africa, a narrow strip located along the Cape...

Assessment on spatiotemporal relationship between rainfall and cloud top temperature from new generation weather satellite imagery

Science.gov (United States)

Wei, Chiang; Yeh, Hui-Chung; Chen, Yen-Chang

2017-04-01

This study addressed the relationship between rainfall and cloud top temperature (CCT) from new generation satellite Himawari-8 imagery at different spatiotemporal scale. This satellite provides higher band, more bits for data format, spatial and temporal resolution compared with previous GMS series. The multi-infrared channels with 10-minute and 1-2 km resolution make it possible for rainfall estimating/forecasting in small/medium watershed. The preliminary result investigated at Chenyulan watershed (443.6 square kilometer) of Central Taiwan in 2016 Typhoon Megi shows the regression coefficient fitted by negative exponential equation of largest rainfall vs. CCT (B8 band) at pixel scale increases as time scales enlarges and reach 0.462 for 120-minute accumulative rainfall; the value (CTT of B15 band) decreases from 0.635 for 10-minute to 0.423 for 120-minute accumulative rainfall at basin-wide scale. More rainfall events for different regime are yet to evaluate to get solid results.

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.

Investigation of Adaptive-threshold Approaches for Determining Area-Time Integrals from Satellite Infrared Data to Estimate Convective Rain Volumes

Science.gov (United States)

Smith, Paul L.; VonderHaar, Thomas H.

1996-01-01

The principal goal of this project is to establish relationships that would allow application of area-time integral (ATI) calculations based upon satellite data to estimate rainfall volumes. The research is being carried out as a collaborative effort between the two participating organizations, with the satellite data analysis to determine values for the ATIs being done primarily by the STC-METSAT scientists and the associated radar data analysis to determine the 'ground-truth' rainfall estimates being done primarily at the South Dakota School of Mines and Technology (SDSM&T). Synthesis of the two separate kinds of data and investigation of the resulting rainfall-versus-ATI relationships is then carried out jointly. The research has been pursued using two different approaches, which for convenience can be designated as the 'fixed-threshold approach' and the 'adaptive-threshold approach'. In the former, an attempt is made to determine a single temperature threshold in the satellite infrared data that would yield ATI values for identifiable cloud clusters which are closely related to the corresponding rainfall amounts as determined by radar. Work on the second, or 'adaptive-threshold', approach for determining the satellite ATI values has explored two avenues: (1) attempt involved choosing IR thresholds to match the satellite ATI values with ones separately calculated from the radar data on a case basis; and (2) an attempt involved a striaghtforward screening analysis to determine the (fixed) offset that would lead to the strongest correlation and lowest standard error of estimate in the relationship between the satellite ATI values and the corresponding rainfall volumes.

Assessment of satellite rainfall products over the Andean plateau

Science.gov (United States)

Satgé, Frédéric; Bonnet, Marie-Paule; Gosset, Marielle; Molina, Jorge; Hernan Yuque Lima, Wilson; Pillco Zolá, Ramiro; Timouk, Franck; Garnier, Jérémie

2016-01-01

Nine satellite rainfall estimations (SREs) were evaluated for the first time over the South American Andean plateau watershed by comparison with rain gauge data acquired between 2005 and 2007. The comparisons were carried out at the annual, monthly and daily time steps. All SREs reproduce the salient pattern of the annual rain field, with a marked north-south gradient and a lighter east-west gradient. However, the intensity of the gradient differs among SREs: it is well marked in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 (TMPA-3B42), Precipitation Estimation from remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Global Satellite Mapping of Precipitation (GSMaP) products, and it is smoothed out in the Climate prediction center MORPHing (CMORPH) products. Another interesting difference among products is the contrast in rainfall amounts between the water surfaces (Lake Titicaca) and the surrounding land. Some products (TMPA-3B42, PERSIANN and GSMaP) show a contradictory rainfall deficit over Lake Titicaca, which may be due to the emissivity contrast between the lake and the surrounding lands and warm rain cloud processes. An analysis differentiating coastal Lake Titicaca from inland pixels confirmed this trend. The raw or Real Time (RT) products have strong biases over the study region. These biases are strongly positive for PERSIANN (above 90%), moderately positive for TMPA-3B42 (28%), strongly negative for CMORPH (- 42%) and moderately negative for GSMaP (- 18%). The biases are associated with a deformation of the rain rate frequency distribution: GSMaP underestimates the proportion of rainfall events for all rain rates; CMORPH overestimates the proportion of rain rates below 2 mm day- 1; and the other products tend to overestimate the proportion of moderate to high rain rates. These biases are greatly reduced by the gauge adjustment in the TMPA-3B42, PERSIANN and CMORPH products, whereas a

From TRMM to GPM: How well can heavy rainfall be detected from space?

Science.gov (United States)

Prakash, Satya; Mitra, Ashis K.; Pai, D. S.; AghaKouchak, Amir

2016-02-01

In this study, we investigate the capabilities of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and the recently released Integrated Multi-satellitE Retrievals for GPM (IMERG) in detecting and estimating heavy rainfall across India. First, the study analyzes TMPA data products over a 17-year period (1998-2014). While TMPA and reference gauge-based observations show similar mean monthly variations of conditional heavy rainfall events, the multi-satellite product systematically overestimates its inter-annual variations. Categorical as well as volumetric skill scores reveal that TMPA over-detects heavy rainfall events (above 75th percentile of reference data), but it shows reasonable performance in capturing the volume of heavy rain across the country. An initial assessment of the GPM-based multi-satellite IMERG precipitation estimates for the southwest monsoon season shows notable improvements over TMPA in capturing heavy rainfall over India. The recently released IMERG shows promising results to help improve modeling of hydrological extremes (e.g., floods and landslides) using satellite observations.

Feasibility Study on the Satellite Rainfall Data for Prediction of Sediment- Related Disaster by the Japanese Prediction Methodology

Science.gov (United States)

Shimizu, Y.; Ishizuka, T.; Osanai, N.; Okazumi, T.

2014-12-01

In this study, the sediment-related disaster prediction method which based ground gauged rainfall-data, currently practiced in Japan was coupled with satellite rainfall data and applied to domestic large-scale sediment-related disasters. The study confirmed the feasibility of this integrated method. In Asia, large-scale sediment-related disasters which can sweep away an entire settlement occur frequently. Leyte Island suffered from a huge landslide in 2004, and Typhoon Molakot in 2009 caused huge landslides in Taiwan. In the event of these sediment-related disasters, immediate responses by central and local governments are crucial in crisis management. In general, there are not enough rainfall gauge stations in developing countries. Therefore national and local governments have little information to determine the risk level of water induced disasters in their service areas. In the Japanese methodology, a criterion is set by combining two indices: the short-term rainfall index and long-term rainfall index. The short-term rainfall index is defined as the 60-minute total rainfall; the long-term rainfall index as the soil-water index, which is an estimation of the retention status of fallen rainfall in soil. In July 2009, a high-density sediment related disaster, or a debris flow, occurred in Hofu City of Yamaguchi Prefecture, in the western region of Japan. This event was calculated by the Japanese standard methodology, and then analyzed for its feasibility. Hourly satellite based rainfall has underestimates compared with ground based rainfall data. Long-term index correlates with each other. Therefore, this study confirmed that it is possible to deliver information on the risk level of sediment-related disasters such as shallow landslides and debris flows. The prediction method tested in this study is expected to assist for timely emergency responses to rainfall-induced natural disasters in sparsely gauged areas. As the Global Precipitation Measurement (GPM) Plan

Characteristics and Diurnal Cycle of GPM Rainfall Estimates over the Central Amazon Region

Directory of Open Access Journals (Sweden)

Rômulo Oliveira

2016-06-01

Full Text Available Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement and GoAmazon (Observations and Modeling of the Green Ocean Amazon over the Brazilian Amazon during 2014/2015. This study aims to assess the characteristics of Global Precipitation Measurement (GPM satellite-based precipitation estimates in representing the diurnal cycle over the Brazilian Amazon. The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG and the Goddard Profiling Algorithm—Version 2014 (GPROF2014 algorithms are evaluated against ground-based radar observations. Specifically, the S-band weather radar from the Amazon Protection National System (SIPAM, is first validated against the X-band CHUVA radar and then used as a reference to evaluate GPM precipitation. Results showed satisfactory agreement between S-band SIPAM radar and both IMERG and GPROF2014 algorithms. However, during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI sensor, significantly overestimates the frequency of heavy rainfall volumes around 00:00–04:00 UTC and 15:00–18:00 UTC. This overestimation is particularly evident over the Negro, Solimões and Amazon rivers due to the poorly-calibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM

Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria

Directory of Open Access Journals (Sweden)

S. O

2017-12-01

Full Text Available The Global Precipitation Measurement (GPM Integrated Multi-satellite Retrievals for GPM (IMERG products provide quasi-global (60° N–60° S precipitation estimates, beginning March 2014, from the combined use of passive microwave (PMW and infrared (IR satellites comprising the GPM constellation. The IMERG products are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account. In this study, IMERG version 3 Early, Late, and Final (IMERG-E,IMERG-L, and IMERG-F half-hourly rainfall estimates are compared with gauge-based gridded rainfall data from the WegenerNet Feldbach region (WEGN high-density climate station network in southeastern Austria. The comparison is conducted over two IMERG 0.1°  ×  0.1° grid cells, entirely covered by 40 and 39 WEGN stations each, using data from the extended summer season (April–October for the first two years of the GPM mission. The entire data are divided into two rainfall intensity ranges (low and high and two seasons (warm and hot, and we evaluate the performance of IMERG, using both statistical and graphical methods. Results show that IMERG-F rainfall estimates are in the best overall agreement with the WEGN data, followed by IMERG-L and IMERG-E estimates, particularly for the hot season. We also illustrate, through rainfall event cases, how insufficient PMW sources and errors in motion vectors can lead to wide discrepancies in the IMERG estimates. Finally, by applying the method of Villarini and Krajewski (2007, we find that IMERG-F half-hourly rainfall estimates can be regarded as a 25 min gauge accumulation, with an offset of +40 min relative to its nominal time.

Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria

Science.gov (United States)

O, Sungmin; Foelsche, Ulrich; Kirchengast, Gottfried; Fuchsberger, Juergen; Tan, Jackson; Petersen, Walter A.

2017-12-01

The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide quasi-global (60° N-60° S) precipitation estimates, beginning March 2014, from the combined use of passive microwave (PMW) and infrared (IR) satellites comprising the GPM constellation. The IMERG products are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account. In this study, IMERG version 3 Early, Late, and Final (IMERG-E,IMERG-L, and IMERG-F) half-hourly rainfall estimates are compared with gauge-based gridded rainfall data from the WegenerNet Feldbach region (WEGN) high-density climate station network in southeastern Austria. The comparison is conducted over two IMERG 0.1° × 0.1° grid cells, entirely covered by 40 and 39 WEGN stations each, using data from the extended summer season (April-October) for the first two years of the GPM mission. The entire data are divided into two rainfall intensity ranges (low and high) and two seasons (warm and hot), and we evaluate the performance of IMERG, using both statistical and graphical methods. Results show that IMERG-F rainfall estimates are in the best overall agreement with the WEGN data, followed by IMERG-L and IMERG-E estimates, particularly for the hot season. We also illustrate, through rainfall event cases, how insufficient PMW sources and errors in motion vectors can lead to wide discrepancies in the IMERG estimates. Finally, by applying the method of Villarini and Krajewski (2007), we find that IMERG-F half-hourly rainfall estimates can be regarded as a 25 min gauge accumulation, with an offset of +40 min relative to its nominal time.

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

Science.gov (United States)

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

2008-01-01

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

The use of geostationary satellite based rainfall estimation and rainfall-runoff modelling for regional flash flood assessment

OpenAIRE

Suseno, Dwi Prabowo Yuga

2013-01-01

The availability of rainfall triggered hazard information such as flash flood is crucial in the flood disaster management and mitigation. However, providing that information is mainly hampered by the shortage of data because of the sparse, uneven or absence the hydrological or meteorological observation. Remote sensing techniques that make frequent observations with continuous spatial coverage provide useful information for detecting the hydrometeorological phenomena such as rainfall and floo...

Estimation of Rainfall Erosivity via 1-Minute to Hourly Rainfall Data from Taipei, Taiwan

Science.gov (United States)

Huang, Ting-Yin; Yang, Ssu-Yao; Jan, Chyan-Deng

2017-04-01

Soil erosion is a natural process on hillslopes that threats people's life and properties, having a considerable environmental and economic implications for soil degradation, agricultural activity and water quality. The rainfall erosivity factor (R-factor) in the Universal Soil Loss Equation (USLE), composed of total kinetic energy (E) and the maximum 30-min rainfall intensity (I30), is widely used as an indicator to measure the potential risks of soil loss caused by rainfall at a regional scale. This R factor can represent the detachment and entrainment involved in climate conditions on hillslopes, but lack of 30-min rainfall intensity data usually lead to apply this factor more difficult in many regions. In recent years, fixed-interval, hourly rainfall data is readily available and widely used due to the development of automatic weather stations. Here we assess the estimations of R, E, and I30 based on 1-, 5-, 10-, 15-, 30-, 60-minute rainfall data, and hourly rainfall data obtained from Taipei weather station during 2004 to 2010. Results show that there is a strong correlation among R-factors estimated from different interval rainfall data. Moreover, the shorter time-interval rainfall data (e.g., 1-min) yields larger value of R-factor. The conversion factors of rainfall erosivity (ratio of values estimated from the resolution lower than 30-min rainfall data to those estimated from 60-min and hourly rainfall data, respectively) range from 1.85 to 1.40 (resp. from 1.89 to 1.02) for 60-min (resp. hourly) rainfall data as the time resolution increasing from 30-min to 1-min. This paper provides useful information on estimating R-factor when hourly rainfall data is only available.

Daily TRMM and Others Rainfall Estimate (3B42 V7 derived) V7

Data.gov (United States)

National Aeronautics and Space Administration — The Tropical Rainfall Measuring Mission (TRMM) is a joint U.S.-Japan satellite mission to monitor tropical and subtropical precipitation and to estimate its...

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.

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

Science.gov (United States)

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

2017-12-01

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

Satellite-Based Precipitation Datasets

Science.gov (United States)

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

2017-12-01

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

Impact analysis of satellite rainfall products on flow simulations in the Magdalena River Basin, Colombia

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Amr Elgamal

2017-02-01

Full Text Available The Magdalena River is the most important river in Colombia in terms of economic activities and is home to about 77% of the country’s population. The river faces water resources allocation challenges, which require reliable hydrological assessments. However, hydrological analysis and model simulations are hampered by insufficient and uncertain knowledge of the actual rainfall fields. In this research the reliability of groundbased measurements, different satellite products of rainfall and their combinations are tested for their impact on the discharge simulations of the Magdalena River. Two different satellite rainfall products from the Tropical Rainfall Measuring Mission (TRMM, have been compared and merged with the ground-based measurements and their impact on the Magdalena river flows quantified using the Representative Elementary Watershed (REW distributed hydrological model.

Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi

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D. Kneis

2014-07-01

Full Text Available The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India. The considered data sets known as 3B42 and 3B42-RT (version 7/7A are routinely produced by the tropical rainfall measuring mission (TRMM from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2 the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT. Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate 80 mm day-1. At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6. In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at

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

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

2010-12-01

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

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

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Jae Young Seo

2017-07-01

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

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

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

Validation of Satellite Estimates (Tropical Rainfall Measuring Mission, TRMM for Rainfall Variability over the Pacific Slope and Coast of Ecuador

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Bolívar Erazo

2018-02-01

Full Text Available A dense rain-gauge network within continental Ecuador was used to evaluate the quality of various products of rainfall data over the Pacific slope and coast of Ecuador (EPSC. A cokriging interpolation method is applied to the rain-gauge data yielding a gridded product at 5-km resolution covering the period 1965–2015. This product is compared with the Global Precipitation Climatology Centre (GPCC dataset, the Climatic Research Unit–University of East Anglia (CRU dataset, the Tropical Rainfall Measuring Mission (TRMM/TMPA 3B43 Version 7 dataset and the ERA-Interim Reanalysis. The analysis reveals that TRMM data show the most realistic features. The relative bias index (Rbias indicates that TRMM data is closer to the observations, mainly over lowlands (mean Rbias of 7% but have more limitations in reproducing the rainfall variability over the Andes (mean Rbias of −28%. The average RMSE and Rbias of 68.7 and −2.8% of TRMM are comparable with the GPCC (69.8 and 5.7% and CRU (102.3 and −2.3% products. This study also focuses on the rainfall inter-annual variability over the study region which experiences floods that have caused high economic losses during extreme El Niño events. Finally, our analysis evaluates the ability of TRMM data to reproduce rainfall events during El Niño years over the study area and the large basins of Esmeraldas and Guayas rivers. The results show that TRMM estimates report reasonable levels of heavy rainfall detection (for the extreme 1998 El Niño event over the EPSC and specifically towards the center-south of the EPSC (Guayas basin but present underestimations for the moderate El Niño of 2002–2003 event and the weak 2009–2010 event. Generally, the rainfall seasonal features, quantity and long-term climatology patterns are relatively well estimated by TRMM.

Rainfall assimilation in RAMS by means of the Kuo parameterisation inversion: method and preliminary results

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Orlandi, A.; Ortolani, A.; Meneguzzo, F.; Levizzani, V.; Torricella, F.; Turk, F. J.

2004-03-01

In order to improve high-resolution forecasts, a specific method for assimilating rainfall rates into the Regional Atmospheric Modelling System model has been developed. It is based on the inversion of the Kuo convective parameterisation scheme. A nudging technique is applied to 'gently' increase with time the weight of the estimated precipitation in the assimilation process. A rough but manageable technique is explained to estimate the partition of convective precipitation from stratiform one, without requiring any ancillary measurement. The method is general purpose, but it is tuned for geostationary satellite rainfall estimation assimilation. Preliminary results are presented and discussed, both through totally simulated experiments and through experiments assimilating real satellite-based precipitation observations. For every case study, Rainfall data are computed with a rapid update satellite precipitation estimation algorithm based on IR and MW satellite observations. This research was carried out in the framework of the EURAINSAT project (an EC research project co-funded by the Energy, Environment and Sustainable Development Programme within the topic 'Development of generic Earth observation technologies', Contract number EVG1-2000-00030).

Hydrological Evaluation of TRMM Rainfall over the Upper Senegal River Basin

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Ansoumana Bodian

2016-04-01

Full Text Available The availability of climatic data, especially on a daily time step, has become very rare in West Africa over the last few years due to the high costs of climate data monitoring. This scarcity of climatic data is a huge obstacle to conduct hydrological studies over some watersheds. In this context, our study aimed to evaluate the capacity of Tropical Rainfall Measuring Mission (TRMM satellite data to simulate the observed runoffs over the Bafing (the main important tributary of the Senegal River before their potential integration in hydrological studies. The conceptual hydrological model GR4J (modèle du Génie Rural (Agricultural Engineering Model à 4 paramètres Journalier (4 parameters Daily has been used, calibrated and validated over the 1961–1997 period with rainfall and Potential Evapotranspiration (PET as inputs. Then, the parameters that best reflect the rainfall-runoff relation, obtained during the cross-calibration-validation phase, were used to simulate runoff over the 1998–2004 period using observed and TRMM rainfalls. The findings of this study show that there is a high consistency between satellite-based estimates and ground-based observations of rainfall. Over the 1998–2004 simulation period, the two rainfall data series show quite satisfactorily results. The output hydrographs from satellite-based estimates and ground-based observations of rainfall coincide quite well with the shape of observed hydrographs with Nash-Sutcliffe Efficiency coefficient (NSE of 0.88 and 0.80 for observed rainfalls and TRMM rainfalls, respectively.

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

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

2016-01-01

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

Extreme flood event analysis in Indonesia based on rainfall intensity and recharge capacity

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Narulita, Ida; Ningrum, Widya

2018-02-01

Indonesia is very vulnerable to flood disaster because it has high rainfall events throughout the year. Flood is categorized as the most important hazard disaster because it is causing social, economic and human losses. The purpose of this study is to analyze extreme flood event based on satellite rainfall dataset to understand the rainfall characteristic (rainfall intensity, rainfall pattern, etc.) that happened before flood disaster in the area for monsoonal, equatorial and local rainfall types. Recharge capacity will be analyzed using land cover and soil distribution. The data used in this study are CHIRPS rainfall satellite data on 0.05 ° spatial resolution and daily temporal resolution, and GSMap satellite rainfall dataset operated by JAXA on 1-hour temporal resolution and 0.1 ° spatial resolution, land use and soil distribution map for recharge capacity analysis. The rainfall characteristic before flooding, and recharge capacity analysis are expected to become the important information for flood mitigation in Indonesia.

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

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E. Goudenhoofdt

2017-10-01

Full Text Available In Belgium, only rain gauge time series have been used so far to study extreme rainfall at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE from a single weather radar is evaluated. For the period 2005–2016, 1 and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The peak intensities are fitted to the exponential distribution using regression in Q-Q plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift associated with convective cells and strong radar signal attenuation. Differences between radar and gauge rainfall values are caused by spatial and temporal sampling, gauge underestimations and radar errors. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis for 1 h duration is performed at the locations of four gauges with 1965–2008 records using the spatially independent QPE data in a circle of 20 km. The confidence interval of the radar fit, which is small due to the sample size, contains the gauge fit for the two closest stations from the radar. In Brussels, the radar extremes are significantly higher than the gauge rainfall extremes, but similar to those observed by an automatic gauge during the same period. The extreme statistics exhibit slight variations related to topography. The radar-based extreme value analysis can be extended to other durations.

Adequacy of TRMM satellite rainfall data in driving the SWAT modeling of Tiaoxi catchment (Taihu lake basin, China)

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Li, Dan; Christakos, George; Ding, Xinxin; Wu, Jiaping

2018-01-01

Spatial rainfall data is an essential input to Distributed Hydrological Models (DHM), and a significant contributor to hydrological model uncertainty. Model uncertainty is higher when rain gauges are sparse, as is often the case in practice. Currently, satellite-based precipitation products increasingly provide an alternative means to ground-based rainfall estimates, in which case a rigorous product assessment is required before implementation. Accordingly, the twofold objective of this work paper was the real-world assessment of both (a) the Tropical Rainfall Measuring Mission (TRMM) rainfall product using gauge data, and (b) the TRMM product's role in forcing data for hydrologic simulations in the area of the Tiaoxi catchment (Taihu lake basin, China). The TRMM rainfall products used in this study are the Version-7 real-time 3B42RT and the post-real-time 3B42. It was found that the TRMM rainfall data showed a superior performance at the monthly and annual scales, fitting well with surface observation-based frequency rainfall distributions. The Nash-Sutcliffe Coefficient of Efficiency (NSCE) and the relative bias ratio (BIAS) were used to evaluate hydrologic model performance. The satisfactory performance of the monthly runoff simulations in the Tiaoxi study supports the view that the implementation of real-time 3B42RT allows considerable room for improvement. At the same time, post-real-time 3B42 can be a valuable tool of hydrologic modeling, water balance analysis, and basin water resource management, especially in developing countries or at remote locations in which rainfall gauges are scarce.

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

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

2017-12-01

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

Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network

NARCIS (Netherlands)

Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

2016-01-01

Microwave links in commercial cellular communication networks hold a promise for areal rainfall monitoring and could complement rainfall estimates from ground-based weather radars, rain gauges, and satellites. It has been shown that country-wide (≈ 35 500 km²) 15 min rainfall maps can

Using satellite-based rainfall data to support the implementation of ...

African Journals Online (AJOL)

The methods currently available in South Africa to implement environmental flows are based on real-time rainfall-runoff models (which require accurate inputs of rainfall data) or the use of flow gauges. Both methods are useful but have limitations which must be fully understood. The main limitation of the latter approach is ...

Detecting Climate Variability in Tropical Rainfall

Science.gov (United States)

Berg, W.

2004-05-01

A number of satellite and merged satellite/in-situ rainfall products have been developed extending as far back as 1979. While the availability of global rainfall data covering over two decades and encompassing two major El Niño events is a valuable resource for a variety of climate studies, significant differences exist between many of these products. Unfortunately, issues such as availability often determine the use of a product for a given application instead of an understanding of the strengths and weaknesses of the various products. Significant efforts have been made to address the impact of sparse sampling by satellite sensors of variable rainfall processes by merging various satellite and in-situ rainfall products. These combine high spatial and temporal frequency satellite infrared data with higher quality passive microwave observations and rain gauge observations. Combining such an approach with spatial and temporal averaging of the data can reduce the large random errors inherent in satellite rainfall estimates to very small levels. Unfortunately, systematic biases can and do result in artificial climate signals due to the underconstrained nature of the rainfall retrieval problem. Because all satellite retrieval algorithms make assumptions regarding the cloud structure and microphysical properties, systematic changes in these assumed parameters between regions and/or times results in regional and/or temporal biases in the rainfall estimates. These biases tend to be relatively small compared to random errors in the retrieval, however, when random errors are reduced through spatial and temporal averaging for climate applications, they become the dominant source of error. Whether or not such biases impact the results for climate studies is very much dependent on the application. For example, all of the existing satellite rainfall products capture the increased rainfall in the east Pacific associated with El Niño, however, the resulting tropical response to

Assimilating satellite soil moisture into rainfall-runoff modelling: towards a systematic study

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Massari, Christian; Tarpanelli, Angelica; Brocca, Luca; Moramarco, Tommaso

2015-04-01

Soil moisture is the main factor for the repartition of the mass and energy fluxes between the land surface and the atmosphere thus playing a fundamental role in the hydrological cycle. Indeed, soil moisture represents the initial condition of rainfall-runoff modelling that determines the flood response of a catchment. Different initial soil moisture conditions can discriminate between catastrophic and minor effects of a given rainfall event. Therefore, improving the estimation of initial soil moisture conditions will reduce uncertainties in early warning flood forecasting models addressing the mitigation of flood hazard. In recent years, satellite soil moisture products have become available with fine spatial-temporal resolution and a good accuracy. Therefore, a number of studies have been published in which the impact of the assimilation of satellite soil moisture data into rainfall-runoff modelling is investigated. Unfortunately, data assimilation involves a series of assumptions and choices that significantly affect the final result. Given a satellite soil moisture observation, a rainfall-runoff model and a data assimilation technique, an improvement or a deterioration of discharge predictions can be obtained depending on the choices made in the data assimilation procedure. Consequently, large discrepancies have been obtained in the studies published so far likely due to the differences in the implementation of the data assimilation technique. On this basis, a comprehensive and robust procedure for the assimilation of satellite soil moisture data into rainfall-runoff modelling is developed here and applied to six subcatchment of the Upper Tiber River Basin for which high-quality hydrometeorological hourly observations are available in the period 1989-2013. The satellite soil moisture product used in this study is obtained from the Advanced SCATterometer (ASCAT) onboard Metop-A satellite and it is available since 2007. The MISDc ("Modello Idrologico Semi

Urban rainfall estimation employing commercial microwave links

Science.gov (United States)

Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko; ten Veldhuis, Marie-claire

2015-04-01

Urban areas often lack rainfall information. To increase the number of rainfall observations in cities, microwave links from operational cellular telecommunication networks may be employed. Although this new potential source of rainfall information has been shown to be promising, its quality needs to be demonstrated more extensively. In the Rain Sense kickstart project of the Amsterdam Institute for Advanced Metropolitan Solutions (AMS), sensors and citizens are preparing Amsterdam for future weather. Part of this project is rainfall estimation using new measurement techniques. Innovative sensing techniques will be utilized such as rainfall estimation from microwave links, umbrellas for weather sensing, low-cost sensors at lamp posts and in drainage pipes for water level observation. These will be combined with information provided by citizens in an active way through smartphone apps and in a passive way through social media posts (Twitter, Flickr etc.). Sensor information will be integrated, visualized and made accessible to citizens to help raise citizen awareness of urban water management challenges and promote resilience by providing information on how citizens can contribute in addressing these. Moreover, citizens and businesses can benefit from reliable weather information in planning their social and commercial activities. In the end city-wide high-resolution rainfall maps will be derived, blending rainfall information from microwave links and weather radars. This information will be used for urban water management. This presentation focuses on rainfall estimation from commercial microwave links. Received signal levels from tens of microwave links within the Amsterdam region (roughly 1 million inhabitants) in the Netherlands are utilized to estimate rainfall with high spatial and temporal resolution. Rainfall maps will be presented and compared to a gauge-adjusted radar rainfall data set. Rainfall time series from gauge(s), radars and links will be compared.

Hydrological similarity approach and rainfall satellite utilization for mini hydro power dam basic design (case study on the ungauged catchment at West Borneo, Indonesia)

Science.gov (United States)

Prakoso, W. G.; Murtilaksono, K.; Tarigan, S. D.; Purwanto, Y. J.

2018-05-01

An approach on flow duration and flood design estimation on the ungauged catchment with no rainfall and discharge data availability was been being develop with hydrological modelling including rainfall run off model implemented with watershed characteristic dataset. Near real time Rainfall data from multi satellite platform e.g. TRMM can be utilized for regionalization approach on the ungauged catchment. Watershed hydrologically similarity analysis were conducted including all of the major watershed in Borneo which was predicted to be similar with the Nanga Raun Watershed. It was found that a satisfactory hydrological model calibration could be achieved using catchment weighted time series of TRMM daily rainfall data, performed on nearby catchment deemed to be sufficiently similar to Nanga Raun catchment in hydrological terms. Based on this calibration, rainfall runoff parameters were then transferred to a model. Relatively reliable flow duration curve and extreme discharge value estimation were produced with reasonable several limitation. Further approach may be performed in order to deal with the primary limitations inherent in the hydrological and statistical analysis, especially to give prolongation to the availability of the rainfall and climate data with some novel approach like downscaling of global climate model.

Hydrological real-time modelling in the Zambezi river basin using satellite-based soil moisture and rainfall data

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P. Meier

2011-03-01

Full Text Available Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. For the management of ecological releases even discharge forecasts with moderate accuracy can be beneficial. Sequential data assimilation using the Ensemble Kalman Filter provides a tool that is both efficient and robust for a real-time modelling framework. One key parameter in a hydrological system is the soil moisture, which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by a radar scatterometer. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modelling framework yields good results in watersheds where soil storage is an important factor. The lead time of the forecast is dependent on the size and the retention capacity of the watershed. For the largest watershed a forecast over 40 days can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In a watershed with little soil storage and a quick response to rainfall events, the performance is relatively poor and the lead time is as short as 10 days only.

A TRMM-Calibrated Infrared Rainfall Algorithm Applied Over Brazil

Science.gov (United States)

Negri, A. J.; Xu, L.; Adler, R. F.; Einaudi, Franco (Technical Monitor)

2000-01-01

The development of a satellite infrared technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall in Amazonia are presented. The Convective-Stratiform. Technique, calibrated by coincident, physically retrieved rain rates from the Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI), is applied during January to April 1999 over northern South America. The diurnal cycle of rainfall, as well as the division between convective and stratiform rainfall is presented. Results compare well (a one-hour lag) with the diurnal cycle derived from Tropical Ocean-Global Atmosphere (TOGA) radar-estimated rainfall in Rondonia. The satellite estimates reveal that the convective rain constitutes, in the mean, 24% of the rain area while accounting for 67% of the rain volume. The effects of geography (rivers, lakes, coasts) and topography on the diurnal cycle of convection are examined. In particular, the Amazon River, downstream of Manaus, is shown to both enhance early morning rainfall and inhibit afternoon convection. Monthly estimates from this technique, dubbed CST/TMI, are verified over a dense rain gage network in the state of Ceara, in northeast Brazil. The CST/TMI showed a high bias equal to +33% of the gage mean, indicating that possibly the TMI estimates alone are also high. The root mean square difference (after removal of the bias) equaled 36.6% of the gage mean. The correlation coefficient was 0.77 based on 72 station-months.

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

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

Science.gov (United States)

Wolff, David B.; Fisher, Brad L.

2011-01-01

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25deg terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU

Satellite precipitation estimation over the Tibetan Plateau

Science.gov (United States)

Porcu, F.; Gjoka, U.

2012-04-01

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

Censored rainfall modelling for estimation of fine-scale extremes

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Cross, David; Onof, Christian; Winter, Hugo; Bernardara, Pietro

2018-01-01

Reliable estimation of rainfall extremes is essential for drainage system design, flood mitigation, and risk quantification. However, traditional techniques lack physical realism and extrapolation can be highly uncertain. In this study, we improve the physical basis for short-duration extreme rainfall estimation by simulating the heavy portion of the rainfall record mechanistically using the Bartlett-Lewis rectangular pulse (BLRP) model. Mechanistic rainfall models have had a tendency to underestimate rainfall extremes at fine temporal scales. Despite this, the simple process representation of rectangular pulse models is appealing in the context of extreme rainfall estimation because it emulates the known phenomenology of rainfall generation. A censored approach to Bartlett-Lewis model calibration is proposed and performed for single-site rainfall from two gauges in the UK and Germany. Extreme rainfall estimation is performed for each gauge at the 5, 15, and 60 min resolutions, and considerations for censor selection discussed.

Internationally coordinated multi-mission planning is now critical to sustain the space-based rainfall observations needed for managing floods globally

International Nuclear Information System (INIS)

Reed, Patrick M; Herman, Jonathan D; Chaney, Nathaniel W; Wood, Eric F; Ferringer, Matthew P

2015-01-01

At present 4 of 10 dedicated rainfall observing satellite systems have exceeded their design life, some by more than a decade. Here, we show operational implications for flood management of a ‘collapse’ of space-based rainfall observing infrastructure as well as the high-value opportunities for a globally coordinated portfolio of satellite missions and data services. Results show that the current portfolio of rainfall missions fails to meet operational data needs for flood management, even when assuming a perfectly coordinated data product from all current rainfall-focused missions (i.e., the full portfolio). In the full portfolio, satellite-based rainfall data deficits vary across the globe and may preclude climate adaptation in locations vulnerable to increasing flood risks. Moreover, removing satellites that are currently beyond their design life (i.e., the reduced portfolio) dramatically increases data deficits globally and could cause entire high intensity flood events to be unobserved. Recovery from the reduced portfolio is possible with internationally coordinated replenishment of as few as 2 of the 4 satellite systems beyond their design life, yielding rainfall data coverages that outperform the current full portfolio (i.e., an optimized portfolio of eight satellites can outperform ten satellites). This work demonstrates the potential for internationally coordinated satellite replenishment and data services to substantially enhance the cost-effectiveness, sustainability and operational value of space-based rainfall observations in managing evolving flood risks. (letter)

Temporal rainfall estimation using input data reduction and model inversion

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Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.

2016-12-01

Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a

Merging Satellite Precipitation Products for Improved Streamflow Simulations

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

2017-12-01

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

Coupling rainfall observations and satellite soil moisture for predicting event soil loss in Central Italy

Science.gov (United States)

Todisco, Francesca; Brocca, Luca; Termite, Loris Francesco; Wagner, Wolfgang

2015-04-01

The accuracy of water soil loss prediction depends on the ability of the model to account for effects of the physical phenomena causing the output and the accuracy by which the parameters have been determined. The process based models require considerable effort to obtain appropriate parameter values and their failure to produce better results than achieved using the USLE/RUSLE model, encourages the use of the USLE/RUSLE model in roles of which it was not designed. In particular it is widely used in watershed models even at the event temporal scale. At hillslope scale, spatial variability in soil and vegetation result in spatial variations in soil moisture and consequently in runoff within the area for which soil loss estimation is required, so the modeling approach required to produce those estimates needs to be sensitive to those spatial variations in runoff. Some models include explicit consideration of runoff in determining the erosive stresses but this increases the uncertainty of the prediction due to the difficulty in parameterising the models also because the direct measures of surface runoff are rare. The same remarks are effective also for the USLE/RUSLE models including direct consideration of runoff in the erosivity factor (i.e. USLE-M by Kinnell and Risse, 1998, and USLE-MM by Bagarello et al., 2008). Moreover actually most of the rainfall-runoff models are based on the knowledge of the pre-event soil moisture that is a fundamental variable in the rainfall-runoff transformation. In addiction soil moisture is a readily available datum being possible to have easily direct pre-event measures of soil moisture using in situ sensors or satellite observations at larger spatial scale; it is also possible to derive the antecedent water content with soil moisture simulation models. The attempt made in the study is to use the pre-event soil moisture to account for the spatial variation in runoff within the area for which the soil loss estimates are required. More

A Modified Gash Model for Estimating Rainfall Interception Loss of Forest Using Remote Sensing Observations at Regional Scale

Directory of Open Access Journals (Sweden)

Yaokui Cui

2014-04-01

Full Text Available Rainfall interception loss of forest is an important component of water balance in a forested ecosystem. The Gash analytical model has been widely used to estimate the forest interception loss at field scale. In this study, we proposed a simple model to estimate rainfall interception loss of heterogeneous forest at regional scale with several reasonable assumptions using remote sensing observations. The model is a modified Gash analytical model using easily measured parameters of forest structure from satellite data and extends the original Gash model from point-scale to the regional scale. Preliminary results, using remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS products, field measured rainfall data, and meteorological data of the Automatic Weather Station (AWS over a picea crassifolia forest in the upper reaches of the Heihe River Basin in northwestern China, showed reasonable accuracy in estimating rainfall interception loss at both the Dayekou experimental site (R2 = 0.91, RMSE = 0.34 mm∙d −1 and the Pailugou experimental site (R2 = 0.82, RMSE = 0.6 mm∙d −1, compared with ground measurements based on per unit area of forest. The interception loss map of the study area was shown to be strongly heterogeneous. The modified model has robust physics and is insensitive to the input parameters, according to the sensitivity analysis using numerical simulations. The modified model appears to be stable and easy to be applied for operational estimation of interception loss over large areas.

RainyDay: An Online, Open-Source Tool for Physically-based Rainfall and Flood Frequency Analysis

Science.gov (United States)

Wright, D.; Yu, G.; Holman, K. D.

2017-12-01

Flood frequency analysis in ungaged or changing watersheds typically requires rainfall intensity-duration-frequency (IDF) curves combined with hydrologic models. IDF curves only depict point-scale rainfall depth, while true rainstorms exhibit complex spatial and temporal structures. Floods result from these rainfall structures interacting with watershed features such as land cover, soils, and variable antecedent conditions as well as river channel processes. Thus, IDF curves are traditionally combined with a variety of "design storm" assumptions such as area reduction factors and idealized rainfall space-time distributions to translate rainfall depths into inputs that are suitable for flood hydrologic modeling. The impacts of such assumptions are relatively poorly understood. Meanwhile, modern precipitation estimates from gridded weather radar, grid-interpolated rain gages, satellites, and numerical weather models provide more realistic depictions of rainfall space-time structure. Usage of such datasets for rainfall and flood frequency analysis, however, are hindered by relatively short record lengths. We present RainyDay, an open-source stochastic storm transposition (SST) framework for generating large numbers of realistic rainfall "scenarios." SST "lengthens" the rainfall record by temporal resampling and geospatial transposition of observed storms to extract space-time information from regional gridded rainfall data. Relatively short (10-15 year) records of bias-corrected radar rainfall data are sufficient to estimate rainfall and flood events with much longer recurrence intervals including 100-year and 500-year events. We describe the SST methodology as implemented in RainyDay and compare rainfall IDF results from RainyDay to conventional estimates from NOAA Atlas 14. Then, we demonstrate some of the flood frequency analysis properties that are possible when RainyDay is integrated with a distributed hydrologic model, including robust estimation of flood

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

A Physically—Based Geometry Model for Transport Distance Estimation of Rainfall-Eroded Soil Sediment

Directory of Open Access Journals (Sweden)

Qian-Gui Zhang

2016-01-01

Full Text Available Estimations of rainfall-induced soil erosion are mostly derived from the weight of sediment measured in natural runoff. The transport distance of eroded soil is important for evaluating landscape evolution but is difficult to estimate, mainly because it cannot be linked directly to the eroded sediment weight. The volume of eroded soil is easier to calculate visually using popular imaging tools, which can aid in estimating the transport distance of eroded soil through geometry relationships. In this study, we present a straightforward geometry model to predict the maximum sediment transport distance incurred by rainfall events of various intensity and duration. In order to verify our geometry prediction model, a series of experiments are reported in the form of a sediment volume. The results show that cumulative rainfall has a linear relationship with the total volume of eroded soil. The geometry model can accurately estimate the maximum transport distance of eroded soil by cumulative rainfall, with a low root-mean-square error (4.7–4.8 and a strong linear correlation (0.74–0.86.

Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China

Directory of Open Access Journals (Sweden)

Lei Bai

2018-02-01

Full Text Available Precipitation is the main component of global water cycle. At present, satellite quantitative precipitation estimates (QPEs are widely applied in the scientific community. However, the evaluations of satellite QPEs have some limitations in terms of the deficiency in observation, evaluation methodology, the selection of time windows for evaluation and short periods for evaluation. The objective of this work is to make some improvements by evaluating the spatio-temporal pattern of the long-terms Climate Hazard Group InfraRed Precipitation Satellite’s (CHIRPS’s QPEs over mainland China. In this study, we compared the daily precipitation estimates from CHIRPS with 2480 rain gauges across China and gridded observation using several statistical metrics in the long-term period of 1981–2014. The results show that there is significant difference between point evaluation and grid evaluation for CHIRPS. CHIRPS has better performance for a large amount of precipitation than it does for arid and semi-arid land. The change in good performance zones has strong relationship with monsoon’s movement. Therefore, CHIRPS performs better in river basins of southern China and exhibits poor performance in river basins in northwestern and northern China. Moreover, CHIRPS exhibits better in warm season than in Winter, owing to its limited ability to detect snowfall. Nevertheless, CHIRPS is moderately sensitive to the precipitation from typhoon weather systems. The limitations for CHIRPS result from the Tropical Rainfall Measuring Mission (TRMM 3B42 estimates’ accuracy and valid spatial coverage.

Estimating methane emissions from landfills based on rainfall, ambient temperature, and waste composition: The CLEEN model.

Science.gov (United States)

Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria

2015-12-01

Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimating tropical vertical motion profile shapes from satellite observations

Science.gov (United States)

Back, L. E.; Handlos, Z.

2013-12-01

The vertical structure of tropical deep convection strongly influences interactions with larger scale circulations and climate. This research focuses on investigating this vertical structure and its relationship with mesoscale tropical weather states. We test the hypothesis that vertical motion shape varies in association with weather state type. We estimate mean state vertical motion profile shapes for six tropical weather states defined using cloud top pressure and optical depth properties from the International Satellite Cloud Climatology Project. The relationship between vertical motion and the dry static energy budget are utilized to set up a regression analysis that empirically determines two modes of variability in vertical motion from reanalysis data. We use these empirically determined modes, this relationship and surface convergence to estimate vertical motion profile shape from observations of satellite retrievals of rainfall and surface convergence. We find that vertical motion profile shapes vary systematically between different tropical weather states. The "isolated systems" regime exhibits a more ''bottom-heavy'' profile shape compared to the convective/thick cirrus and vigorous deep convective regimes, with maximum upward vertical motion occurring in the lower troposphere rather than the middle to upper troposphere. The variability we observe with our method does not coincide with that expected based on conventional ideas about how stratiform rain fraction and vertical motion are related.

Remote sensing-based characterization of rainfall during atmospheric rivers over the central United States

Science.gov (United States)

Nayak, Munir A.; Villarini, Gabriele

2018-01-01

Atmospheric rivers (ARs) play a central role in the hydrology and hydroclimatology of the central United States. More than 25% of the annual rainfall is associated with ARs over much of this region, with many large flood events tied to their occurrence. Despite the relevance of these storms for flood hydrology and water budget, the characteristics of rainfall associated with ARs over the central United has not been investigated thus far. This study fills this major scientific gap by describing the rainfall during ARs over the central United States using five remote sensing-based precipitation products over a 12-year study period. The products we consider are: Stage IV, Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH). As part of the study, we evaluate these products against a rain gauge-based dataset using both graphical- and metrics-based diagnostics. Based on our analyses, Stage IV is found to better reproduce the reference data. Hence, we use it for the characterization of rainfall in ARs. Most of the AR-rainfall is located in a narrow region within ∼150 km on both sides of the AR major axis. In this region, rainfall has a pronounced positive relationship with the magnitude of the water vapor transport. Moreover, we have also identified a consistent increase in rainfall intensity with duration (or persistence) of AR conditions. However, there is not a strong indication of diurnal variability in AR rainfall. These results can be directly used in developing flood protection strategies during ARs. Further, weather prediction agencies can benefit from the results of this study to achieve higher skill of resolving precipitation processes in their models.

Large-scale assessment of soil erosion in Africa: satellites help to jointly account for dynamic rainfall and vegetation cover

Science.gov (United States)

Vrieling, Anton; Hoedjes, Joost C. B.; van der Velde, Marijn

2015-04-01

Efforts to map and monitor soil erosion need to account for the erratic nature of the soil erosion process. Soil erosion by water occurs on sloped terrain when erosive rainfall and consequent surface runoff impact soils that are not well-protected by vegetation or other soil protective measures. Both rainfall erosivity and vegetation cover are highly variable through space and time. Due to data paucity and the relative ease of spatially overlaying geographical data layers into existing models like USLE (Universal Soil Loss Equation), many studies and mapping efforts merely use average annual values for erosivity and vegetation cover as input. We first show that rainfall erosivity can be estimated from satellite precipitation data. We obtained average annual erosivity estimates from 15 yr of 3-hourly TRMM Multi-satellite Precipitation Analysis (TMPA) data (1998-2012) using intensity-erosivity relationships. Our estimates showed a positive correlation (r = 0.84) with long-term annual erosivity values of 37 stations obtained from literature. Using these TMPA erosivity retrievals, we demonstrate the large interannual variability, with maximum annual erosivity often exceeding two to three times the mean value, especially in semi-arid areas. We then calculate erosivity at a 10-daily time-step and combine this with vegetation cover development for selected locations in Africa using NDVI - normalized difference vegetation index - time series from SPOT VEGETATION. Although we do not integrate the data at this point, the joint analysis of both variables stresses the need for joint accounting for erosivity and vegetation cover for large-scale erosion assessment and monitoring.

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

Science.gov (United States)

Wright, Daniel B.; Mantilla, Ricardo; Peters-Lidard, Christa D.

2018-01-01

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions. PMID:29657544

Sampling Errors in Monthly Rainfall Totals for TRMM and SSM/I, Based on Statistics of Retrieved Rain Rates and Simple Models

Science.gov (United States)

Bell, Thomas L.; Kundu, Prasun K.; Einaudi, Franco (Technical Monitor)

2000-01-01

Estimates from TRMM satellite data of monthly total rainfall over an area are subject to substantial sampling errors due to the limited number of visits to the area by the satellite during the month. Quantitative comparisons of TRMM averages with data collected by other satellites and by ground-based systems require some estimate of the size of this sampling error. A method of estimating this sampling error based on the actual statistics of the TRMM observations and on some modeling work has been developed. "Sampling error" in TRMM monthly averages is defined here relative to the monthly total a hypothetical satellite permanently stationed above the area would have reported. "Sampling error" therefore includes contributions from the random and systematic errors introduced by the satellite remote sensing system. As part of our long-term goal of providing error estimates for each grid point accessible to the TRMM instruments, sampling error estimates for TRMM based on rain retrievals from TRMM microwave (TMI) data are compared for different times of the year and different oceanic areas (to minimize changes in the statistics due to algorithmic differences over land and ocean). Changes in sampling error estimates due to changes in rain statistics due 1) to evolution of the official algorithms used to process the data, and 2) differences from other remote sensing systems such as the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I), are analyzed.

Impact of Uncertainty Characterization of Satellite Rainfall Inputs and Model Parameters on Hydrological Data Assimilation with the Ensemble Kalman Filter for Flood Prediction

Science.gov (United States)

Vergara, H. J.; Kirstetter, P.; Hong, Y.; Gourley, J. J.; Wang, X.

2013-12-01

the sensitivities of the EnKF is revealed examining the impact of uncertainty representation on the assimilation process and subsequent forecasts. The methodology is tested on the hydrologic modeling of a basin subject to recurrent flooding events triggered by landfalling tropical systems. An ensemble framework for flood forecasting is employed to simulate the hydrologic processes using different levels of model structural complexity. Tropical Rainfall Measuring Mission (TRMM)-based QPE are used to force the hydrologic modeling system. A radar-based QPE product is utilized as a reference to estimate errors in satellite rainfall estimates and to characterize the uncertainty in model inputs. This set-up contributes to the development of methodologies to understand and characterize the error in satellite rainfall in a hydrologic modeling framework. Consequently, the results from this study will provide guidance for the integration of other remote-sensing observations through data assimilation for a more complete picture of the hydrometeorological phenomena of interest.

A Machine Learning-based Rainfall System for GPM Dual-frequency Radar

Science.gov (United States)

Tan, H.; Chandrasekar, V.; Chen, H.

2017-12-01

Precipitation measurement produced by the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) plays an important role in researching the water circle and forecasting extreme weather event. Compare with its predecessor - Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), GRM DPR measures precipitation in two different frequencies (i.e., Ku and Ka band), which can provide detailed information on the microphysical properties of precipitation particles, quantify particle size distribution and quantitatively measure light rain and falling snow. This paper presents a novel Machine Learning system for ground-based and space borne radar rainfall estimation. The system first trains ground radar data for rainfall estimation using rainfall measurements from gauges and subsequently uses the ground radar based rainfall estimates to train GPM DPR data in order to get space based rainfall product. Therein, data alignment between space DPR and ground radar is conducted using the methodology proposed by Bolen and Chandrasekar (2013), which can minimize the effects of potential geometric distortion of GPM DPR observations. For demonstration purposes, rainfall measurements from three rain gauge networks near Melbourne, Florida, are used for training and validation purposes. These three gauge networks, which are located in Kennedy Space Center (KSC), South Florida Water Management District (SFL), and St. Johns Water Management District (STJ), include 33, 46, and 99 rain gauge stations, respectively. Collocated ground radar observations from the National Weather Service (NWS) Weather Surveillance Radar - 1988 Doppler (WSR-88D) in Melbourne (i.e., KMLB radar) are trained with the gauge measurements. The trained model is then used to derive KMLB radar based rainfall product, which is used to train GPM DPR data collected from coincident overpasses events. The machine learning based rainfall product is compared against the GPM standard products

Drought Early Warning and Agro-Meteorological Risk Assessment using Earth Observation Rainfall Datasets and Crop Water Budget Modelling

Science.gov (United States)

Tarnavsky, E.

2016-12-01

The water resources satisfaction index (WRSI) model is widely used in drought early warning and food security analyses, as well as in agro-meteorological risk management through weather index-based insurance. Key driving data for the model is provided from satellite-based rainfall estimates such as ARC2 and TAMSAT over Africa and CHIRPS globally. We evaluate the performance of these rainfall datasets for detecting onset and cessation of rainfall and estimating crop production conditions for the WRSI model. We also examine the sensitivity of the WRSI model to different satellite-based rainfall products over maize growing regions in Tanzania. Our study considers planting scenarios for short-, medium-, and long-growing cycle maize, and we apply these for 'regular' and drought-resistant maize, as well as with two different methods for defining the start of season (SOS). Simulated maize production estimates are compared against available reported production figures at the national and sub-national (province) levels. Strengths and weaknesses of the driving rainfall data, insights into the role of the SOS definition method, and phenology-based crop yield coefficient and crop yield reduction functions are discussed in the context of space-time drought characteristics. We propose a way forward for selecting skilled rainfall datasets and discuss their implication for crop production monitoring and the design and structure of weather index-based insurance products as risk transfer mechanisms implemented across scales for smallholder farmers to national programmes.

Linking soil type and rainfall characteristics towards estimation of surface evaporative capacitance

Science.gov (United States)

Or, D.; Bickel, S.; Lehmann, P.

2017-12-01

Separation of evapotranspiration (ET) to evaporation (E) and transpiration (T) components for attribution of surface fluxes or for assessment of isotope fractionation in groundwater remains a challenge. Regional estimates of soil evaporation often rely on plant-based (Penman-Monteith) ET estimates where is E is obtained as a residual or a fraction of potential evaporation. We propose a novel method for estimating E from soil-specific properties, regional rainfall characteristics and considering concurrent internal drainage that shelters soil water from evaporation. A soil-dependent evaporative characteristic length defines a depth below which soil water cannot be pulled to the surface by capillarity; this depth determines the maximal soil evaporative capacitance (SEC). The SEC is recharged by rainfall and subsequently emptied by competition between drainage and surface evaporation (considering canopy interception evaporation). We show that E is strongly dependent on rainfall characteristics (mean annual, number of storms) and soil textural type, with up to 50% of rainfall lost to evaporation in loamy soil. The SEC concept applied to different soil types and climatic regions offers direct bounds on regional surface evaporation independent of plant-based parameterization or energy balance calculations.

Propagation of Satelite Rainfall Products uncertainties in hydrological applications : Examples in West-Africa in the framework of the Megha-Tropiques Satellite Mission

Science.gov (United States)

Casse, C.; Gosset, M.; Peugeot, C.; Boone, A.; Pedinotti, V.

2013-12-01

The use of satellite based rainfall in research or operational Hydrological application is becoming more and more frequent. This is specially true in the Tropics where ground based gauge (or radar) network are generally scarce and often degrading. Member of the GPM constellation, the new French-Indian satellite Mission Megha-Tropiques (MT) dedicated to the water and energy budget in the tropical atmosphere contributes to a better monitoring of rainfall in the inter-tropical zone. As part of this mission, research is developed on the use of MT rainfall products for hydrological research or operational application such as flood monitoring. A key issue for such applications is how to account for rainfall products biases and uncertainties, and how to propagate them in the end user models ? Another important question is how to choose the best space-time resolution for the rainfall forcing, given that both model performances and rain-product uncertainties are resolution dependent. This talk will present on going investigations and perspectives on this subject, with examples from the Megha_tropiques Ground validation sites in West Africa. The CNRM model Surfex-ISBA/TRIP has been set up to simulate the hydrological behavior of the Niger River. This modeling set up is being used to study the predictability of Niger Floods events in the city of Niamey and the performances of satellite rainfall products as forcing for such predictions. One of the interesting feature of the Niger outflow in Niamey is its double peak : a first peak attributed to 'local' rainfall falling in small to medium size basins situated in the region of Niamey, and a second peak linked to the rainfall falling in the upper par of the river, and slowly propagating through the river towards Niamey. The performances of rainfall products are found to differ between the wetter/upper part of the basin, and the local/sahelian areas. Both academic tests with artificially biased or 'perturbed' rainfield and actual

Validation of a global satellite rainfall product for real time monitoring of meteorological extremes

Science.gov (United States)

Cánovas-García, Fulgencio; García-Galiano, Sandra; Karbalaee, Negar

2017-10-01

The real time monitoring of storms is important for the management and prevention of flood risks. However, in the southeast of Spain, it seems that the density of the rain gauge network may not be sufficient to adequately characterize the rainfall spatial distribution or the high rainfall intensities that are reached during storms. Satellite precipitation products such as PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System) could be used to complement the automatic rain gauge networks and so help solve this problem. However, the PERSIANN-CCS product has only recently become available, so its operational validity for areas such as south-eastern Spain is not yet known. In this work, a methodology for the hourly validation of PERSIANN-CCS is presented. We used the rain gauge stations of the SIAM (Sistema de Información Agraria de Murcia) network to study three storms with a very high return period. These storms hit the east and southeast of the Iberian Peninsula and resulted in the loss of human life, major damage to agricultural crops and a strong impact on many different types of infrastructure. The study area is the province of Murcia (Region of Murcia), located in the southeast of the Iberian Peninsula, covering an area of more than 11,000 km2 and with a population of almost 1.5 million. In order to validate the PERSIANN-CCS product for these three storms, contrasts were made with the hyetographs registered by the automatic rain gauges, analyzing statistics such as bias, mean square difference and Pearson's correlation coefficient. Although in some cases the temporal distribution of rainfall was well captured by PERSIANN-CCS, in several rain gauges high intensities were not properly represented. The differences were strongly correlated with the rain gauge precipitation, but not with satellite-obtained rainfall. The main conclusion concerns the need for specific local calibration

Modelling and assessment of urban flood hazards based on rainfall intensity-duration-frequency curves reformation

OpenAIRE

Ghazavi, Reza; Moafi Rabori, Ali; Ahadnejad Reveshty, Mohsen

2016-01-01

Estimate design storm based on rainfall intensity–duration–frequency (IDF) curves is an important parameter for hydrologic planning of urban areas. The main aim of this study was to estimate rainfall intensities of Zanjan city watershed based on overall relationship of rainfall IDF curves and appropriate model of hourly rainfall estimation (Sherman method, Ghahreman and Abkhezr method). Hydrologic and hydraulic impacts of rainfall IDF curves change in flood properties was evaluated via Stormw...

Multifractal rainfall extremes: Theoretical analysis and practical estimation

International Nuclear Information System (INIS)

Langousis, Andreas; Veneziano, Daniele; Furcolo, Pierluigi; Lepore, Chiara

2009-01-01

We study the extremes generated by a multifractal model of temporal rainfall and propose a practical method to estimate the Intensity-Duration-Frequency (IDF) curves. The model assumes that rainfall is a sequence of independent and identically distributed multiplicative cascades of the beta-lognormal type, with common duration D. When properly fitted to data, this simple model was found to produce accurate IDF results [Langousis A, Veneziano D. Intensity-duration-frequency curves from scaling representations of rainfall. Water Resour Res 2007;43. (doi:10.1029/2006WR005245)]. Previous studies also showed that the IDF values from multifractal representations of rainfall scale with duration d and return period T under either d → 0 or T → ∞, with different scaling exponents in the two cases. We determine the regions of the (d, T)-plane in which each asymptotic scaling behavior applies in good approximation, find expressions for the IDF values in the scaling and non-scaling regimes, and quantify the bias when estimating the asymptotic power-law tail of rainfall intensity from finite-duration records, as was often done in the past. Numerically calculated exact IDF curves are compared to several analytic approximations. The approximations are found to be accurate and are used to propose a practical IDF estimation procedure.

Bayesian estimation of extreme flood quantiles using a rainfall-runoff model and a stochastic daily rainfall generator

Science.gov (United States)

Costa, Veber; Fernandes, Wilson

2017-11-01

Extreme flood estimation has been a key research topic in hydrological sciences. Reliable estimates of such events are necessary as structures for flood conveyance are continuously evolving in size and complexity and, as a result, their failure-associated hazards become more and more pronounced. Due to this fact, several estimation techniques intended to improve flood frequency analysis and reducing uncertainty in extreme quantile estimation have been addressed in the literature in the last decades. In this paper, we develop a Bayesian framework for the indirect estimation of extreme flood quantiles from rainfall-runoff models. In the proposed approach, an ensemble of long daily rainfall series is simulated with a stochastic generator, which models extreme rainfall amounts with an upper-bounded distribution function, namely, the 4-parameter lognormal model. The rationale behind the generation model is that physical limits for rainfall amounts, and consequently for floods, exist and, by imposing an appropriate upper bound for the probabilistic model, more plausible estimates can be obtained for those rainfall quantiles with very low exceedance probabilities. Daily rainfall time series are converted into streamflows by routing each realization of the synthetic ensemble through a conceptual hydrologic model, the Rio Grande rainfall-runoff model. Calibration of parameters is performed through a nonlinear regression model, by means of the specification of a statistical model for the residuals that is able to accommodate autocorrelation, heteroscedasticity and nonnormality. By combining the outlined steps in a Bayesian structure of analysis, one is able to properly summarize the resulting uncertainty and estimating more accurate credible intervals for a set of flood quantiles of interest. The method for extreme flood indirect estimation was applied to the American river catchment, at the Folsom dam, in the state of California, USA. Results show that most floods

Improving PERSIANN-CCS rain estimation using probabilistic approach and multi-sensors information

Science.gov (United States)

Karbalaee, N.; Hsu, K. L.; Sorooshian, S.; Kirstetter, P.; Hong, Y.

2016-12-01

This presentation discusses the recent implemented approaches to improve the rainfall estimation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System (PERSIANN-CCS). PERSIANN-CCS is an infrared (IR) based algorithm being integrated in the IMERG (Integrated Multi-Satellite Retrievals for the Global Precipitation Mission GPM) to create a precipitation product in 0.1x0.1degree resolution over the chosen domain 50N to 50S every 30 minutes. Although PERSIANN-CCS has a high spatial and temporal resolution, it overestimates or underestimates due to some limitations.PERSIANN-CCS can estimate rainfall based on the extracted information from IR channels at three different temperature threshold levels (220, 235, and 253k). This algorithm relies only on infrared data to estimate rainfall indirectly from this channel which cause missing the rainfall from warm clouds and false estimation for no precipitating cold clouds. In this research the effectiveness of using other channels of GOES satellites such as visible and water vapors has been investigated. By using multi-sensors the precipitation can be estimated based on the extracted information from multiple channels. Also, instead of using the exponential function for estimating rainfall from cloud top temperature, the probabilistic method has been used. Using probability distributions of precipitation rates instead of deterministic values has improved the rainfall estimation for different type of clouds.

Adjustment of rainfall estimates from weather radars using in-situ stormwater drainage sensors

DEFF Research Database (Denmark)

Ahm, Malte

importance as long as the estimated flow and water levels are correct. It makes sense to investigate the possibility of adjusting weather radar data to rainfall-runoff measurements instead of rain gauge measurements in order to obtain better predictions of flow and water levels. This Ph.D. study investigates......-rain gauge adjusted data is applied for urban drainage models, discrepancies between radar-estimated runoff and observed runoff still occur. The aim of urban drainage applications is to estimate flow and water levels in critical points in the system. The “true” rainfall at ground level is, therefore, of less...... how rainfall-runoff measurements can be utilised to adjust weather radars. Two traditional adjustments methods based on rain gauges were used as the basis for developing two radar-runoff adjustment methods. The first method is based on the ZR relationship describing the relation between radar...

Rainfall measurement based on in-situ storm drainage flow sensors

DEFF Research Database (Denmark)

Ahm, Malte; Rasmussen, Michael Robdrup

2017-01-01

Data for adjustment of weather radar rainfall estimations are mostly obtained from rain gauge observations. However, the density of rain gauges is often very low. Yet in many urban catchments, runoff sensors are typically available which can measure the rainfall indirectly. By utilising these sen......Data for adjustment of weather radar rainfall estimations are mostly obtained from rain gauge observations. However, the density of rain gauges is often very low. Yet in many urban catchments, runoff sensors are typically available which can measure the rainfall indirectly. By utilising...... these sensors, it may be possible to improve the ground rainfall estimate, and thereby improve the quantitative precipitation estimation from weather radars for urban drainage applications. To test the hypothesis, this paper presents a rainfall measurement method based on flow rate measurements from well......-defined urban surfaces. This principle was used to design a runoff measurement system in a parking structure in Aalborg, Denmark, where it was evaluated against rain gauges. The measurements show that runoff measurements from well-defined urban surfaces perform just as well as rain gauges. This opens up...

Opportunities and challenges for evaluating precipitation estimates during GPM mission

Energy Technology Data Exchange (ETDEWEB)

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

2006-10-15

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

TRMM Applications for Rainfall-Induced Landslide Early Warning

Science.gov (United States)

Dok, A.; Fukuoka, H.; Hong, Y.

2012-04-01

Early warning system (EWS) is the most effective method in saving lives and reducing property damages resulted from the catastrophic landslides if properly implemented in populated areas of landslide-prone nations. For predicting the occurrence of landslides, it requires examination of empirical relationship between rainfall characteristics and past landslide occurrence. In developed countries like Japan and the US, precipitation is monitored by rain radars and ground-based rain gauge matrix. However, in developing regions like Southeast Asian countries, very limited number of rain gauges is available, and there is no implemented methodology for issuing effective warming of landslides yet. Correspondingly, satellite precipitation monitoring could be therefore a possible and promising solution for launching landslide quasi-real-time early warning system in those countries. It is due to the fact that TMPA (TRMM Multi-satellite Precipitation Analysis) can provides a globally calibration-based sequential scheme for combining precipitation estimates from multiple satellites, and gauge analyses where feasible, at fine scales (3-hourly with 0.25°x0.25° spatial resolution). It is available both after and in quasi-real time, calibrated by TRMM Combined Instrument and TRMM Microwave Imager precipitation product. However, validation of ground based rain gauge and TRMM satellite data in the vulnerable regions is still not yet operative. Snake-line/Critical-line and Soil Water Index (SWI) are used for issuing warning of landslide occurrence in Japan; whereas, Caine criterion is preferable in Europe and western nations. Herewith, it presents rainfall behavior which took place in Beichuan city (located on the 2008 Chinese Wenchuan earthquake fault), Hofu and Shobara cities in Japan where localized heavy rainfall attacked in 2009 and 2010, respectively, from TRMM 3B42RT correlated with ground based rain gauge data. The 1-day rainfall intensity and 15-day cumulative rainfall

Evaluation of Clear Sky Models for Satellite-Based Irradiance Estimates

Energy Technology Data Exchange (ETDEWEB)

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

2013-12-01

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

Estimating Vegetation Rainfall Interception Using Remote Sensing Observations at Very High Resolution

Science.gov (United States)

Cui, Y.; Zhao, P.; Hong, Y.; Fan, W.; Yan, B.; Xie, H.

2017-12-01

Abstract: As an important compont of evapotranspiration, vegetation rainfall interception is the proportion of gross rainfall that is intercepted, stored and subsequently evaporated from all parts of vegetation during or following rainfall. Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model's strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception loss and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study. Keywords: rainfall interception; remote sensing; RS-Gash analytical model; high resolution

Areal rainfall estimation using moving cars - computer experiments including hydrological modeling

Science.gov (United States)

Rabiei, Ehsan; Haberlandt, Uwe; Sester, Monika; Fitzner, Daniel; Wallner, Markus

2016-09-01

The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rain rate. The optical sensors used in that study are designed for operating the windscreen wipers and showed promising results for rainfall measurement purposes. Their measurement accuracy has been quantified in laboratory experiments. Considering explicitly those errors, the main objective of this study is to investigate the benefit of using RCs for estimating areal rainfall. For that, computer experiments are carried out, where radar rainfall is considered as the reference and the other sources of data, i.e., RCs and rain gauges, are extracted from radar data. Comparing the quality of areal rainfall estimation by RCs with rain gauges and reference data helps to investigate the benefit of the RCs. The value of this additional source of data is not only assessed for areal rainfall estimation performance but also for use in hydrological modeling. Considering measurement errors derived from laboratory experiments, the result shows that the RCs provide useful additional information for areal rainfall estimation as well as for hydrological modeling. Moreover, by testing larger uncertainties for RCs, they observed to be useful up to a certain level for areal rainfall estimation and discharge simulation.

Point Cloud Based Relative Pose Estimation of a Satellite in Close Range

Directory of Open Access Journals (Sweden)

Lujiang Liu

2016-06-01

Full Text Available Determination of the relative pose of satellites is essential in space rendezvous operations and on-orbit servicing missions. The key problems are the adoption of suitable sensor on board of a chaser and efficient techniques for pose estimation. This paper aims to estimate the pose of a target satellite in close range on the basis of its known model by using point cloud data generated by a flash LIDAR sensor. A novel model based pose estimation method is proposed; it includes a fast and reliable pose initial acquisition method based on global optimal searching by processing the dense point cloud data directly, and a pose tracking method based on Iterative Closest Point algorithm. Also, a simulation system is presented in this paper in order to evaluate the performance of the sensor and generate simulated sensor point cloud data. It also provides truth pose of the test target so that the pose estimation error can be quantified. To investigate the effectiveness of the proposed approach and achievable pose accuracy, numerical simulation experiments are performed; results demonstrate algorithm capability of operating with point cloud directly and large pose variations. Also, a field testing experiment is conducted and results show that the proposed method is effective.

Probabilistic clustering of rainfall condition for landslide triggering

Science.gov (United States)

Rossi, Mauro; Luciani, Silvia; Cesare Mondini, Alessandro; Kirschbaum, Dalia; Valigi, Daniela; Guzzetti, Fausto

2013-04-01

Landslides are widespread natural and man made phenomena. They are triggered by earthquakes, rapid snow melting, human activities, but mostly by typhoons and intense or prolonged rainfall precipitations. In Italy mostly they are triggered by intense precipitation. The prediction of landslide triggered by rainfall precipitations over large areas is commonly based on the exploitation of empirical models. Empirical landslide rainfall thresholds are used to identify rainfall conditions for the possible landslide initiation. It's common practice to define rainfall thresholds by assuming a power law lower boundary in the rainfall intensity-duration or cumulative rainfall-duration space above which landslide can occur. The boundary is defined considering rainfall conditions associated to landslide phenomena using heuristic approaches, and doesn't consider rainfall events not causing landslides. Here we present a new fully automatic method to identify the probability of landslide occurrence associated to rainfall conditions characterized by measures of intensity or cumulative rainfall and rainfall duration. The method splits the rainfall events of the past in two groups: a group of events causing landslides and its complementary, then estimate their probabilistic distributions. Next, the probabilistic membership of the new event to one of the two clusters is estimated. The method doesn't assume a priori any threshold model, but simple exploits the real empirical distribution of rainfall events. The approach was applied in the Umbria region, Central Italy, where a catalogue of landslide timing, were obtained through the search of chronicles, blogs and other source of information in the period 2002-2012. The approach was tested using rain gauge measures and satellite rainfall estimates (NASA TRMM-v6), allowing in both cases the identification of the rainfall condition triggering landslides in the region. Compared to the other existing threshold definition methods, the prosed

SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture

Science.gov (United States)

Ciabatta, Luca; Massari, Christian; Brocca, Luca; Gruber, Alexander; Reimer, Christoph; Hahn, Sebastian; Paulik, Christoph; Dorigo, Wouter; Kidd, Richard; Wagner, Wolfgang

2018-02-01

Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca et al., 2014). Daily rainfall estimates are generated for an 18-year long period (1998-2015), with a spatial sampling of 0.25° on a global scale, and are based on the integration of the ACTIVE and the PASSIVE ESA CCI SM data sets.The quality of the SM2RAIN-CCI rainfall data set is evaluated by comparing it with two state-of-the-art rainfall satellite products, i.e. the Tropical Measurement Mission Multi-satellite Precipitation Analysis 3B42 real-time product (TMPA 3B42RT) and the Climate Prediction Center Morphing Technique (CMORPH), and one modeled data set (ERA-Interim). A quality check is carried out on a global scale at 1° of spatial sampling and 5 days of temporal sampling by comparing these products with the gauge-based Global Precipitation Climatology Centre Full Data Daily (GPCC-FDD) product. SM2RAIN-CCI shows relatively good results in terms of correlation coefficient (median value > 0.56), root mean square difference (RMSD, median value test the capabilities of the data set to correctly identify rainfall events under different climate and precipitation regimes.The SM2RAIN-CCI rainfall data set is freely available at https://doi.org/10.5281/zenodo.846259.

The Soil Moisture Dependence of TRMM Microwave Imager Rainfall Estimates

Science.gov (United States)

Seyyedi, H.; Anagnostou, E. N.

2011-12-01

This study presents an in-depth analysis of the dependence of overland rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) on the soil moisture conditions at the land surface. TMI retrievals are verified against rainfall fields derived from a high resolution rain-gauge network (MESONET) covering Oklahoma. Soil moisture (SOM) patterns are extracted based on recorded data from 2000-2007 with 30 minutes temporal resolution. The area is divided into wet and dry regions based on normalized SOM (Nsom) values. Statistical comparison between two groups is conducted based on recorded ground station measurements and the corresponding passive microwave retrievals from TMI overpasses at the respective MESONET station location and time. The zero order error statistics show that the Probability of Detection (POD) for the wet regions (higher Nsom values) is higher than the dry regions. The Falls Alarm Ratio (FAR) and volumetric FAR is lower for the wet regions. The volumetric missed rain for the wet region is lower than dry region. Analysis of the MESONET-to-TMI ratio values shows that TMI tends to overestimate for surface rainfall intensities less than 12 (mm/h), however the magnitude of the overestimation over the wet regions is lower than the dry regions.

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.

Estimating Winter Annual Biomass in the Sonoran and Mojave Deserts with Satellite- and Ground-Based Observations

Directory of Open Access Journals (Sweden)

Bradley C. Reed

2013-02-01

Full Text Available Winter annual plants in southwestern North America influence fire regimes, provide forage, and help prevent erosion. Exotic annuals may also threaten native species. Monitoring winter annuals is difficult because of their ephemeral nature, making the development of a satellite monitoring tool valuable. We mapped winter annual aboveground biomass in the Desert Southwest from satellite observations, evaluating 18 algorithms using time-series vegetation indices (VI. Field-based biomass estimates were used to calibrate and evaluate each algorithm. Winter annual biomass was best estimated by calculating a base VI across the period of record and subtracting it from the peak VI for each winter season (R2 = 0.92. The normalized difference vegetation index (NDVI derived from 8-day reflectance data provided the best estimate of winter annual biomass. It is important to account for the timing of peak vegetation when relating field-based estimates to satellite VI data, since post-peak field estimates may indicate senescent biomass which is inaccurately represented by VI-based estimates. Images generated from the best-performing algorithm show both spatial and temporal variation in winter annual biomass. Efforts to manage this variable resource would be enhanced by a tool that allows the monitoring of changes in winter annual resources over time.

Rainfall Product Evaluation for the TRMM Ground Validation Program

Science.gov (United States)

Amitai, E.; Wolff, D. B.; Robinson, M.; Silberstein, D. S.; Marks, D. A.; Kulie, M. S.; Fisher, B.; Einaudi, Franco (Technical Monitor)

2000-01-01

Evaluation of the Tropical Rainfall Measuring Mission (TRMM) satellite observations is conducted through a comprehensive Ground Validation (GV) Program. Standardized instantaneous and monthly rainfall products are routinely generated using quality-controlled ground based radar data from four primary GV sites. As part of the TRMM GV program, effort is being made to evaluate these GV products and to determine the uncertainties of the rainfall estimates. The evaluation effort is based on comparison to rain gauge data. The variance between the gauge measurement and the true averaged rain amount within the radar pixel is a limiting factor in the evaluation process. While monthly estimates are relatively simple to evaluate, the evaluation of the instantaneous products are much more of a challenge. Scattegrams of point comparisons between radar and rain gauges are extremely noisy for several reasons (e.g. sample volume discrepancies, timing and navigation mismatches, variability of Z(sub e)-R relationships), and therefore useless for evaluating the estimates. Several alternative methods, such as the analysis of the distribution of rain volume by rain rate as derived from gauge intensities and from reflectivities above the gauge network will be presented. Alternative procedures to increase the accuracy of the estimates and to reduce their uncertainties also will be discussed.

Estimation of potential rainfall recharge in the pothwar area

International Nuclear Information System (INIS)

Afzal, M.; Yaseen, M.

2015-01-01

Groundwater recharge is complex phenomenon to understand and describe because it cannot be seen with open eyes. We have to depend some theoretical assumptions to understand this complicated hidden natural underground water movement process. There are many factors affecting and controlling the water movement in soil profile. Groundwater use in district chakwal is of a fundamental importance to meet the rapidly expanding drinking and agricultural water requirements. The man factors contributing to groundwater recharge in chakwal are rainfall, evapotranspiration and geology. due to the semi arid climatic conditions of the area, this resource is almost the only key to economic development. There are a number of dug wells in the area where water is getting stored during rainy season. source and processes of recharge in humid areas are different compared with semi-arid areas. Due to the main resource of available water in the area, the potential groundwater recharge estimation could be good exercise to visulize the amount of rainwater entering the ground. For groundwater recharge estimation there are a number of simple and advanced techniques available. In the present study simple methods were used to estimate potential recharge due to available limited resources. Rainfall runoff, gravimetric and water table fluctuation methods were used to quantify rainfall recharge during the monsoon season. The average potential recharge estimated was 60% of the rainfall of 148 mm. Rainfall runoff and gravimetric methods were found to be comparable for short period potential recharge estimation while water table fluctuation method gives actual recharge and require longer period data. Potential recharge values were higher for area having grassland type vegetation and low for area covering shrubs and tick vegetation. (author)

Analytical solutions to sampling effects in drop size distribution measurements during stationary rainfall: Estimation of bulk rainfall variables

NARCIS (Netherlands)

Uijlenhoet, R.; Porrà, J.M.; Sempere Torres, D.; Creutin, J.D.

2006-01-01

A stochastic model of the microstructure of rainfall is used to derive explicit expressions for the magnitude of the sampling fluctuations in rainfall properties estimated from raindrop size measurements in stationary rainfall. The model is a marked point process, in which the points represent the

Rainfall estimation with TFR model using Ensemble Kalman filter

Science.gov (United States)

Asyiqotur Rohmah, Nabila; Apriliani, Erna

2018-03-01

Rainfall fluctuation can affect condition of other environment, correlated with economic activity and public health. The increasing of global average temperature is influenced by the increasing of CO2 in the atmosphere, which caused climate change. Meanwhile, the forests as carbon sinks that help keep the carbon cycle and climate change mitigation. Climate change caused by rainfall intensity deviations can affect the economy of a region, and even countries. It encourages research on rainfall associated with an area of forest. In this study, the mathematics model that used is a model which describes the global temperatures, forest cover, and seasonal rainfall called the TFR (temperature, forest cover, and rainfall) model. The model will be discretized first, and then it will be estimated by the method of Ensemble Kalman Filter (EnKF). The result shows that the more ensembles used in estimation, the better the result is. Also, the accurateness of simulation result is influenced by measurement variable. If a variable is measurement data, the result of simulation is better.

Quantifying rainfall-derived inflow and infiltration in sanitary sewer systems based on conductivity monitoring

Science.gov (United States)

Zhang, Mingkai; Liu, Yanchen; Cheng, Xun; Zhu, David Z.; Shi, Hanchang; Yuan, Zhiguo

2018-03-01

Quantifying rainfall-derived inflow and infiltration (RDII) in a sanitary sewer is difficult when RDII and overflow occur simultaneously. This study proposes a novel conductivity-based method for estimating RDII. The method separately decomposes rainfall-derived inflow (RDI) and rainfall-induced infiltration (RII) on the basis of conductivity data. Fast Fourier transform was adopted to analyze variations in the flow and water quality during dry weather. Nonlinear curve fitting based on the least squares algorithm was used to optimize parameters in the proposed RDII model. The method was successfully applied to real-life case studies, in which inflow and infiltration were successfully estimated for three typical rainfall events with total rainfall volumes of 6.25 mm (light), 28.15 mm (medium), and 178 mm (heavy). Uncertainties of model parameters were estimated using the generalized likelihood uncertainty estimation (GLUE) method and were found to be acceptable. Compared with traditional flow-based methods, the proposed approach exhibits distinct advantages in estimating RDII and overflow, particularly when the two processes happen simultaneously.

Deterministic Approach for Estimating Critical Rainfall Threshold of Rainfall-induced Landslide in Taiwan

Science.gov (United States)

Chung, Ming-Chien; Tan, Chih-Hao; Chen, Mien-Min; Su, Tai-Wei

2013-04-01

Taiwan is an active mountain belt created by the oblique collision between the northern Luzon arc and the Asian continental margin. The inherent complexities of geological nature create numerous discontinuities through rock masses and relatively steep hillside on the island. In recent years, the increase in the frequency and intensity of extreme natural events due to global warming or climate change brought significant landslides. The causes of landslides in these slopes are attributed to a number of factors. As is well known, rainfall is one of the most significant triggering factors for landslide occurrence. In general, the rainfall infiltration results in changing the suction and the moisture of soil, raising the unit weight of soil, and reducing the shear strength of soil in the colluvium of landslide. The stability of landslide is closely related to the groundwater pressure in response to rainfall infiltration, the geological and topographical conditions, and the physical and mechanical parameters. To assess the potential susceptibility to landslide, an effective modeling of rainfall-induced landslide is essential. In this paper, a deterministic approach is adopted to estimate the critical rainfall threshold of the rainfall-induced landslide. The critical rainfall threshold is defined as the accumulated rainfall while the safety factor of the slope is equal to 1.0. First, the process of deterministic approach establishes the hydrogeological conceptual model of the slope based on a series of in-situ investigations, including geological drilling, surface geological investigation, geophysical investigation, and borehole explorations. The material strength and hydraulic properties of the model were given by the field and laboratory tests. Second, the hydraulic and mechanical parameters of the model are calibrated with the long-term monitoring data. Furthermore, a two-dimensional numerical program, GeoStudio, was employed to perform the modelling practice. Finally

STEP-TRAMM - A modeling interface for simulating localized rainfall induced shallow landslides and debris flow runout pathways

Science.gov (United States)

Or, D.; von Ruette, J.; Lehmann, P.

2017-12-01

Landslides and subsequent debris-flows initiated by rainfall represent a common natural hazard in mountainous regions. We integrated a landslide hydro-mechanical triggering model with a simple model for debris flow runout pathways and developed a graphical user interface (GUI) to represent these natural hazards at catchment scale at any location. The STEP-TRAMM GUI provides process-based estimates of the initiation locations and sizes of landslides patterns based on digital elevation models (SRTM) linked with high resolution global soil maps (SoilGrids 250 m resolution) and satellite based information on rainfall statistics for the selected region. In the preprocessing phase the STEP-TRAMM model estimates soil depth distribution to supplement other soil information for delineating key hydrological and mechanical properties relevant to representing local soil failure. We will illustrate this publicly available GUI and modeling platform to simulate effects of deforestation on landslide hazards in several regions and compare model outcome with satellite based information.

ASSESSMENT OF SATELLITE PRECIPITATION PRODUCTS IN THE PHILIPPINE ARCHIPELAGO

Directory of Open Access Journals (Sweden)

M. D. Ramos

2016-06-01

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

Assessment of Satellite Precipitation Products in the Philippine Archipelago

Science.gov (United States)

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

2016-06-01

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

Evaluating the hydrological consistency of satellite based water cycle components

KAUST Repository

Lopez Valencia, Oliver Miguel

2016-06-15

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

The Impact of a Amazonian Deforestation on Dry-Season Rainfall

Science.gov (United States)

Negri, Andrew J.; Adler, Robert F.; Xu, Liming; Surratt, Jason

2003-01-01

Many modeling studies have concluded that widespread deforestation of Amazonia would lead to decreased rainfall. We analyze geosynchronous infrared satellite data with respect to percent cloudiness, and analyze rain estimates from microwave sensors aboard the Tropical Rainfall Measuring Mission satellite. We conclude that in the dry-season, when the effects of the surface are not overwhelmed by synoptic-scale weather disturbances, shallow cumulus cloudiness, deep convective cloudiness, and rainfall occurrence all are larger over the deforested and non-forested (savanna) regions than over areas of dense jungle. This difference is in response to a local circulation initiated by the differential heating of the region s varying forestation. Analysis of the diurnal cycle of cloudiness reveals a shift in the onset of convection toward afternoon hours in the deforested and towards the morning hours in the savanna regions when compared to the neighboring forested regions. Analysis of 14 years of monthly estimates from the Special Sensor Microwave/Imager data revealed that in only in August was there a pattern of higher monthly rainfall amounts over the deforested region.

Rainfall variability over southern Africa: an overview of current research using satellite and climate model data

Science.gov (United States)

Williams, C.; Kniveton, D.; Layberry, R.

2009-04-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. In this research, satellite-derived rainfall data are used as a basis for undertaking model experiments using a state-of-the-art climate model, run at both high and low spatial resolution. Once the model's ability to reproduce extremes has been assessed, idealised regions of sea surface temperature (SST) anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, a brief overview is given of the authors' research to date, pertaining to southern African rainfall. This covers (i) a description of present-day rainfall variability over southern Africa; (ii) a comparison of model simulated daily rainfall with the satellite-derived dataset; (iii) results from sensitivity testing of the model's domain size; and (iv) results from the idealised SST experiments.

Evaluation of Version-7 TRMM Multi-Satellite Precipitation Analysis Product during the Beijing Extreme Heavy Rainfall Event of 21 July 2012

Directory of Open Access Journals (Sweden)

Yong Huang

2013-12-01

Full Text Available The latest Version-7 (V7 Tropical Rainfall Measuring Mission (TRMM Multi-satellite Precipitation Analysis (TMPA products were released by the National Aeronautics and Space Administration (NASA in December of 2012. Their performance on different climatology, locations, and precipitation types is of great interest to the satellite-based precipitation community. This paper presents a study of TMPA precipitation products (3B42RT and 3B42V7 for an extreme precipitation event in Beijing and its adjacent regions (from 00:00 UTC 21 July 2012 to 00:00 UTC 22 July 2012. Measurements from a dense rain gauge network were used as the ground truth to evaluate the latest TMPA products. Results are summarized as follows. Compared to rain gauge measurements, both 3B42RT and 3B42V7 generally captured the rainfall spatial and temporal pattern, having a moderate spatial correlation coefficient (CC, 0.6 and high CC values (0.88 over the broader Hebei, Beijing and Tianjin (HBT regions, but the rainfall peak is 6 h ahead of gauge observations. Overall, 3B42RT showed higher estimation than 3B42V7 over both HBT and Beijing. At the storm center, both 3B42RT and 3B42V7 presented a relatively large deviation from the temporal variation of rainfall and underestimated the storm by 29.02% and 36.07%, respectively. The current study suggests that the latest TMPA products still have limitations in terms of resolution and accuracy, especially for this type of extreme event within a latitude area on the edge of coverage of TRMM precipitation radar and microwave imager. Therefore, TMPA users should be cautious when 3B42RT and 3B42V7 are used to model, monitor, and forecast both flooding hazards in the Beijing urban area and landslides in the mountainous west and north of Beijing.

The Impact of Amazonian Deforestation on Dry-Season Rainfall

Science.gov (United States)

Negri, Andrew J.; Adler, Robert F.; Xu, Li-Ming; Surratt, Jason; Starr, David OC. (Technical Monitor)

2002-01-01

Many modeling studies have concluded that widespread deforestation of Amazonia would lead to decreased rainfall. We analyze geosynchronous infrared satellite data with respect percent cloudiness, and analyze rain estimates from microwave sensors aboard the Tropical Rainfall Measuring Mission satellite. We conclude that in the dry-season, when the effects of the surface are not overwhelmed by synoptic-scale weather disturbances, deep convective cloudiness, as well as rainfall occurrence, all increase over the deforested and non-forested (savanna) regions. This is in response to a local circulation initiated by the differential heating of the region's varying forestation. Analysis of the diurnal cycle of cloudiness reveals a shift toward afternoon hours in the deforested and savanna regions, compared to the forested regions. Analysis of 14 years of data from the Special Sensor Microwave/Imager data revealed that only in August did rainfall amounts increase over the deforested region.

Topography and Data Mining Based Methods for Improving Satellite Precipitation in Mountainous Areas of China

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Ting Xia

2015-07-01

Full Text Available Topography is a significant factor influencing the spatial distribution of precipitation. This study developed a new methodology to evaluate and calibrate the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA products by merging geographic and topographic information. In the proposed method, firstly, the consistency rule was introduced to evaluate the fitness of satellite rainfall with measurements on the grids with and without ground gauges. Secondly, in order to improve the consistency rate of satellite rainfall, genetic programming was introduced to mine the relationship between the gauge rainfall and location, elevation and TMPA rainfall. The proof experiment and analysis for the mean annual satellite precipitation from 2001–2012, 3B43 (V7 of TMPA rainfall product, was carried out in eight mountainous areas of China. The result shows that the proposed method is significant and efficient both for the assessment and improvement of satellite precipitation. It is found that the satellite rainfall consistency rates in the gauged and ungauged grids are different in the study area. In addition, the mined correlation of location-elevation-TMPA rainfall can noticeably improve the satellite precipitation, both in the context of the new criterion of the consistency rate and the existing criteria such as Bias and RMSD. The proposed method is also efficient for correcting the monthly and mean monthly rainfall of 3B43 and 3B42RT.

Lightning-based propagation of convective rain fields

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S. Dietrich

2011-05-01

Full Text Available This paper describes a new multi-sensor approach for continuously monitoring convective rain cells. It exploits lightning data from surface networks to propagate rain fields estimated from multi-frequency brightness temperature measurements taken by the AMSU/MHS microwave radiometers onboard NOAA/EUMETSAT low Earth orbiting operational satellites. Specifically, the method allows inferring the development (movement, morphology and intensity of convective rain cells from the spatial and temporal distribution of lightning strokes following any observation by a satellite-borne microwave radiometer. Obviously, this is particularly attractive for real-time operational purposes, due to the sporadic nature of the low Earth orbiting satellite measurements and the continuous availability of ground-based lightning measurements – as is the case in most of the Mediterranean region. A preliminary assessment of the lightning-based rainfall propagation algorithm has been successfully made by using two pairs of consecutive AMSU observations, in conjunction with lightning measurements from the ZEUS network, for two convective events. Specifically, we show that the evolving rain fields, which are estimated by applying the algorithm to the satellite-based rainfall estimates for the first AMSU overpass, show an overall agreement with the satellite-based rainfall estimates for the second AMSU overpass.

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

Science.gov (United States)

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

2017-01-01

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

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

Science.gov (United States)

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

2015-01-01

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

The role of regional information in estimation of extreme point rainfalls

DEFF Research Database (Denmark)

Rosbjerg, Dan; Madsen, Henrik

1996-01-01

Previous analysis has shown that inclusion of regional information improves at-site estimation of point rainfalls and makes it possible to obtain estimates at non-monitored sites. The basis for this analysis was a partial duration series (PDS) modelling of individual rainfall observations and use...... point rainfall data into one sample from a common parent distribution and modelling with disregard of either the dependence between stations or the regional heterogeneity. The different models are analysed and compared with respect to the uncertainty of the predicted extreme events....

Reducing bias in rainfall estimates from microwave links by considering variable drop size distribution

Science.gov (United States)

Fencl, Martin; Jörg, Rieckermann; Vojtěch, Bareš

2015-04-01

Commercial microwave links (MWL) are point-to-point radio systems which are used in backhaul networks of cellular operators. For several years, they have been suggested as rainfall sensors complementary to rain gauges and weather radars, because, first, they operate at frequencies where rain drops represent significant source of attenuation and, second, cellular networks almost completely cover urban and rural areas. Usually, path-average rain rates along a MWL are retrieved from the rain-induced attenuation of received MWL signals with a simple model based on a power law relationship. The model is often parameterized based on the characteristics of a particular MWL, such as frequency, polarization and the drop size distribution (DSD) along the MWL. As information on the DSD is usually not available in operational conditions, the model parameters are usually considered constant. Unfortunately, this introduces bias into rainfall estimates from MWL. In this investigation, we propose a generic method to eliminate this bias in MWL rainfall estimates. Specifically, we search for attenuation statistics which makes it possible to classify rain events into distinct groups for which same power-law parameters can be used. The theoretical attenuation used in the analysis is calculated from DSD data using T-Matrix method. We test the validity of our approach on observations from a dedicated field experiment in Dübendorf (CH) with a 1.85-km long commercial dual-polarized microwave link transmitting at a frequency of 38 GHz, an autonomous network of 5 optical distrometers and 3 rain gauges distributed along the path of the MWL. The data is recorded at a high temporal resolution of up to 30s. It is further tested on data from an experimental catchment in Prague (CZ), where 14 MWLs, operating at 26, 32 and 38 GHz frequencies, and reference rainfall from three RGs is recorded every minute. Our results suggest that, for our purpose, rain events can be nicely characterized based on

Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets

Science.gov (United States)

Panagos, Panos; Ballabio, Cristiano; Meusburger, Katrin; Spinoni, Jonathan; Alewell, Christine; Borrelli, Pasquale

2017-05-01

The policy requests to develop trends in soil erosion changes can be responded developing modelling scenarios of the two most dynamic factors in soil erosion, i.e. rainfall erosivity and land cover change. The recently developed Rainfall Erosivity Database at European Scale (REDES) and a statistical approach used to spatially interpolate rainfall erosivity data have the potential to become useful knowledge to predict future rainfall erosivity based on climate scenarios. The use of a thorough statistical modelling approach (Gaussian Process Regression), with the selection of the most appropriate covariates (monthly precipitation, temperature datasets and bioclimatic layers), allowed to predict the rainfall erosivity based on climate change scenarios. The mean rainfall erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha-1 h-1 yr-1 till 2050 showing a relative increase of 18% compared to baseline data (2010). The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April-September). The output results report a pan-European projection of future rainfall erosivity taking into account the uncertainties of the climatic models.

Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets.

Science.gov (United States)

Panagos, Panos; Ballabio, Cristiano; Meusburger, Katrin; Spinoni, Jonathan; Alewell, Christine; Borrelli, Pasquale

2017-05-01

The policy requests to develop trends in soil erosion changes can be responded developing modelling scenarios of the two most dynamic factors in soil erosion, i.e. rainfall erosivity and land cover change. The recently developed Rainfall Erosivity Database at European Scale (REDES) and a statistical approach used to spatially interpolate rainfall erosivity data have the potential to become useful knowledge to predict future rainfall erosivity based on climate scenarios. The use of a thorough statistical modelling approach (Gaussian Process Regression), with the selection of the most appropriate covariates (monthly precipitation, temperature datasets and bioclimatic layers), allowed to predict the rainfall erosivity based on climate change scenarios. The mean rainfall erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha -1  h -1  yr -1 till 2050 showing a relative increase of 18% compared to baseline data (2010). The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April-September). The output results report a pan-European projection of future rainfall erosivity taking into account the uncertainties of the climatic models.

Comparing Approaches to Deal With Non-Gaussianity of Rainfall Data in Kriging-Based Radar-Gauge Rainfall Merging

Science.gov (United States)

Cecinati, F.; Wani, O.; Rico-Ramirez, M. A.

2017-11-01

Merging radar and rain gauge rainfall data is a technique used to improve the quality of spatial rainfall estimates and in particular the use of Kriging with External Drift (KED) is a very effective radar-rain gauge rainfall merging technique. However, kriging interpolations assume Gaussianity of the process. Rainfall has a strongly skewed, positive, probability distribution, characterized by a discontinuity due to intermittency. In KED rainfall residuals are used, implicitly calculated as the difference between rain gauge data and a linear function of the radar estimates. Rainfall residuals are non-Gaussian as well. The aim of this work is to evaluate the impact of applying KED to non-Gaussian rainfall residuals, and to assess the best techniques to improve Gaussianity. We compare Box-Cox transformations with λ parameters equal to 0.5, 0.25, and 0.1, Box-Cox with time-variant optimization of λ, normal score transformation, and a singularity analysis technique. The results suggest that Box-Cox with λ = 0.1 and the singularity analysis is not suitable for KED. Normal score transformation and Box-Cox with optimized λ, or λ = 0.25 produce satisfactory results in terms of Gaussianity of the residuals, probability distribution of the merged rainfall products, and rainfall estimate quality, when validated through cross-validation. However, it is observed that Box-Cox transformations are strongly dependent on the temporal and spatial variability of rainfall and on the units used for the rainfall intensity. Overall, applying transformations results in a quantitative improvement of the rainfall estimates only if the correct transformations for the specific data set are used.

Estimation of local rainfall erosivity using artificial neural network

Directory of Open Access Journals (Sweden)

Paulo Tarso Sanches Oliveira

2011-08-01

Full Text Available The information retrieval of local values of rainfall erosivity is essential for soil loss estimation with the Universal Soil Loss Equation (USLE, and thus is very useful in soil and water conservation planning. In this manner, the objective of this study was to develop an Artificial Neural Network (ANN with the capacity of estimating, with satisfactory accuracy, the rainfall erosivity in any location of the Mato Grosso do Sul state. We used data from rain erosivity, latitude, longitude, altitude of pluviometric and pluviographic stations located in the state to train and test an ANN. After training with various network configurations, we selected the best performance and higher coefficient of determination calculated on the basis of data erosivity of the sample test and the values estimated by ANN. In evaluating the results, the confidence and the agreement indices were used in addition to the coefficient of determination. It was found that it is possible to estimate the rainfall erosivity for any location in the state of Mato Grosso do Sul, in a reliable way, using only data of geographical coordinates and altitude.

Satellite-derived land covers for runoff estimation using SCS-CN method in Chen-You-Lan Watershed, Taiwan

Science.gov (United States)

Zhang, Wen-Yan; Lin, Chao-Yuan

2017-04-01

The Soil Conservation Service Curve Number (SCS-CN) method, which was originally developed by the USDA Natural Resources Conservation Service, is widely used to estimate direct runoff volume from rainfall. The runoff Curve Number (CN) parameter is based on the hydrologic soil group and land use factors. In Taiwan, the national land use maps were interpreted from aerial photos in 1995 and 2008. Rapid updating of post-disaster land use map is limited due to the high cost of production, so the classification of satellite images is the alternative method to obtain the land use map. In this study, Normalized Difference Vegetation Index (NDVI) in Chen-You-Lan Watershed was derived from dry and wet season of Landsat imageries during 2003 - 2008. Land covers were interpreted from mean value and standard deviation of NDVI and were categorized into 4 groups i.e. forest, grassland, agriculture and bare land. Then, the runoff volume of typhoon events during 2005 - 2009 were estimated using SCS-CN method and verified with the measured runoff data. The result showed that the model efficiency coefficient is 90.77%. Therefore, estimating runoff by using the land cover map classified from satellite images is practicable.

Object-Based Assessment of Satellite Precipitation Products

Directory of Open Access Journals (Sweden)

Jingjing Li

2016-06-01

Full Text Available An object-based verification approach is employed to assess the performance of the commonly used high-resolution satellite precipitation products: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN, Climate Prediction center MORPHing technique (CMORPH, and Tropical Rainfall Measurement Mission (TRMM Multi-Satellite Precipitation Analysis (TMPA 3B42RT. The evaluation of the satellite precipitation products focuses on the skill of depicting the geometric features of the localized precipitation areas. Seasonal variability of the performances of these products against the ground observations is investigated through the examples of warm and cold seasons. It is found that PERSIANN is capable of depicting the orientation of the localized precipitation areas in both seasons. CMORPH has the ability to capture the sizes of the localized precipitation areas and performs the best in the overall assessment for both seasons. 3B42RT is capable of depicting the location of the precipitation areas for both seasons. In addition, all of the products perform better on capturing the sizes and centroids of precipitation areas in the warm season than in the cold season, while they perform better on depicting the intersection area and orientation in the cold season than in the warm season. These products are more skillful on correctly detecting the localized precipitation areas against the observations in the warm season than in the cold season.

Satellite observations of rainfall effect on sea surface salinity in the waters adjacent to Taiwan

Science.gov (United States)

Ho, Chung-Ru; Hsu, Po-Chun; Lin, Chen-Chih; Huang, Shih-Jen

2017-10-01

Changes of oceanic salinity are highly related to the variations of evaporation and precipitation. To understand the influence of rainfall on the sea surface salinity (SSS) in the waters adjacent to Taiwan, satellite remote sensing data from the year of 2012 to 2014 are employed in this study. The daily rain rate data obtained from Special Sensor Microwave Imager (SSM/I), Tropical Rainfall Measuring Mission's Microwave Imager (TRMM/TMI), Advanced Microwave Scanning Radiometer (AMSR), and WindSat Polarimetric Radiometer. The SSS data was derived from the measurements of radiometer instruments onboard the Aquarius satellite. The results show the average values of SSS in east of Taiwan, east of Luzon and South China Sea are 33.83 psu, 34.05 psu, and 32.84 psu, respectively, in the condition of daily rain rate higher than 1 mm/hr. In contrast to the rainfall condition, the average values of SSS are 34.07 psu, 34.26 psu, and 33.09 psu in the three areas, respectively at no rain condition (rain rate less than 1 mm/hr). During the cases of heavy rainfall caused by spiral rain bands of typhoon, the SSS is diluted with an average value of -0.78 psu when the average rain rate is higher than 4 mm/hr. However, the SSS was increased after temporarily decreased during the typhoon cases. A possible reason to explain this phenomenon is that the heavy rainfall caused by the spiral rain bands of typhoon may dilute the sea surface water, but the strong winds can uplift the higher salinity of subsurface water to the sea surface.

Measurement-based perturbation theory and differential equation parameter estimation with applications to satellite gravimetry

Science.gov (United States)

Xu, Peiliang

2018-06-01

The numerical integration method has been routinely used by major institutions worldwide, for example, NASA Goddard Space Flight Center and German Research Center for Geosciences (GFZ), to produce global gravitational models from satellite tracking measurements of CHAMP and/or GRACE types. Such Earth's gravitational products have found widest possible multidisciplinary applications in Earth Sciences. The method is essentially implemented by solving the differential equations of the partial derivatives of the orbit of a satellite with respect to the unknown harmonic coefficients under the conditions of zero initial values. From the mathematical and statistical point of view, satellite gravimetry from satellite tracking is essentially the problem of estimating unknown parameters in the Newton's nonlinear differential equations from satellite tracking measurements. We prove that zero initial values for the partial derivatives are incorrect mathematically and not permitted physically. The numerical integration method, as currently implemented and used in mathematics and statistics, chemistry and physics, and satellite gravimetry, is groundless, mathematically and physically. Given the Newton's nonlinear governing differential equations of satellite motion with unknown equation parameters and unknown initial conditions, we develop three methods to derive new local solutions around a nominal reference orbit, which are linked to measurements to estimate the unknown corrections to approximate values of the unknown parameters and the unknown initial conditions. Bearing in mind that satellite orbits can now be tracked almost continuously at unprecedented accuracy, we propose the measurement-based perturbation theory and derive global uniformly convergent solutions to the Newton's nonlinear governing differential equations of satellite motion for the next generation of global gravitational models. Since the solutions are global uniformly convergent, theoretically speaking

First results from comparison of rainfall estimations by GPM IMERG with rainfall measurements from the WegenerNet high density network

Science.gov (United States)

Oo, Sungmin; Foelsche, Ulrich; Kirchengast, Gottfried; Fuchsberger, Jürgen

2016-04-01

The research level products of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG "Final" run datasets) were compared with rainfall measurements from the WegenerNet high density network as part of ground validation (GV) projects of GPM missions. The WegenerNet network comprises 151 ground level weather stations in an area of 15 km × 20 km in south-eastern Austria (Feldbach region, ˜46.93° N, ˜15.90° E) designed to serve as a long-term monitoring and validation facility for weather and climate research and applications. While the IMERG provides rainfall estimations every half hour at 0.1° resolution, the WegenerNet network measures rainfall every 5 minutes at around 2 km2 resolution and produces 200 m × 200 m gridded datasets. The study was conducted on the domain of the WegenerNet network; eight IMERG grids are overlapped with the network, two of which are entirely covered by the WegenerNet (40 and 39 stations in each grid). We investigated data from April to September of the years 2014 to 2015; the date of first two years after the launch of the GPM Core Observatory. Since the network has a flexibility to work with various spatial and temporal scales, the comparison could be conducted on average-points to pixel basis at both sub-daily and daily timescales. This presentation will summarize the first results of the comparison and future plans to explore the characteristics of errors in the IMERG datasets.

Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones

Science.gov (United States)

Luitel, Beda; Villarini, Gabriele; Vecchi, Gabriel A.

2018-01-01

The goal of this study is the evaluation of the skill of five state-of-the-art numerical weather prediction (NWP) systems [European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC)] in forecasting rainfall from North Atlantic tropical cyclones (TCs). Analyses focus on 15 North Atlantic TCs that made landfall along the U.S. coast over the 2007-2012 period. As reference data we use gridded rainfall provided by the Climate Prediction Center (CPC). We consider forecast lead-times up to five days. To benchmark the skill of these models, we consider rainfall estimates from one radar-based (Stage IV) and four satellite-based [Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH)] rainfall products. Daily and storm total rainfall fields from each of these remote sensing products are compared to the reference data to obtain information about the range of errors we can expect from "observational data." The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale. Considering these skill metrics, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead-times up to 48 h, without a consistently best or worst NWP model.

Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data

Science.gov (United States)

Ovando, Gustavo; Sayago, Silvina; Bocco, Mónica

2018-04-01

Crop models allow simulating the development and yield of the crops, to represent and to evaluate the influence of multiple factors. The DSSAT cropping system model is one of the most widely used and contains CROPGRO module for soybean. This crop has a great importance for many southern countries of Latin America and for Argentina. Solar radiation and rainfall are necessary variables as inputs for crop models; however these data are not as readily available. The satellital products from Clouds and Earth's Radiant Energy System (CERES) and Tropic Rainfall Measurement Mission (TRMM) provide continuous spatial and temporal information of solar radiation and precipitation, respectively. This study evaluates and quantifies the uncertainty in estimating soybean yield using a DSSAT model, when recorded weather data are replaced with CERES and TRMM ones. Different percentages of data replacements, soybean maturity groups and planting dates are considered, for 2006-2016 period in Oliveros (Argentina). Results show that CERES and TRMM products can be used for soybean yield estimation with DSSAT considering that: percentage of data replacement, campaign, planting date and maturity group, determine the amounts and trends of yield errors. Replacements with CERES data up to 30% result in %RMSE lower than 10% in 87% of the cases; while the replacement with TRMM data presents the best statisticals in campaigns with high yields. Simulations based entirely on CERES solar radiation give better results than those with TRMM. In general, similar percentages of replacement show better performance in the estimation of soybean yield for solar radiation than the replacement of precipitation values.

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

Directory of Open Access Journals (Sweden)

Yuxiang He

2018-01-01

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

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

Science.gov (United States)

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

2018-03-01

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

Multi-catchment rainfall-runoff simulation for extreme flood estimation

Science.gov (United States)

Paquet, Emmanuel

2017-04-01

The SCHADEX method (Paquet et al., 2013) is a reference method in France for the estimation of extreme flood for dam design. The method is based on a semi-continuous rainfall-runoff simulation process: hundreds of different rainy events, randomly drawn up to extreme values, are simulated independently in the hydrological conditions of each day when a rainy event has been actually observed. This allows generating an exhaustive set of crossings between precipitation and soil saturation hazards, and to build a complete distribution of flood discharges up to extreme quantiles. The hydrological model used within SCHADEX, the MORDOR model (Garçon, 1996), is a lumped model, which implies that hydrological processes, e.g. rainfall and soil saturation, are supposed to be homogeneous throughout the catchment. Snow processes are nevertheless represented in relation with altitude. This hypothesis of homogeneity is questionable especially as the size of the catchment increases, or in areas of highly contrasted climatology (like mountainous areas). Conversely, modeling the catchment with a fully distributed approach would cause different problems, in particular distributing the rainfall-runoff model parameters trough space, and within the SCHADEX stochastic framework, generating extreme rain fields with credible spatio-temporal features. An intermediate solution is presented here. It provides a better representation of the hydro-climatic diversity of the studied catchment (especially regarding flood processes) while keeping the SCHADEX simulation framework. It consists in dividing the catchment in several, more homogeneous sub-catchments. Rainfall-runoff models are parameterized individually for each of them, using local discharge data if available. A first SCHADEX simulation is done at the global scale, which allows assigning a probability to each simulated event, mainly based on the global areal rainfall drawn for the event (see Paquet el al., 2013 for details). Then the

Development of a multi-sensor based urban discharge forecasting system using remotely sensed data: A case study of extreme rainfall in South Korea

Science.gov (United States)

Yoon, Sunkwon; Jang, Sangmin; Park, Kyungwon

2017-04-01

Extreme weather due to changing climate is a main source of water-related disasters such as flooding and inundation and its damage will be accelerated somewhere in world wide. To prevent the water-related disasters and mitigate their damage in urban areas in future, we developed a multi-sensor based real-time discharge forecasting system using remotely sensed data such as radar and satellite. We used Communication, Ocean and Meteorological Satellite (COMS) and Korea Meteorological Agency (KMA) weather radar for quantitative precipitation estimation. The Automatic Weather System (AWS) and McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) were used for verification of rainfall accuracy. The optimal Z-R relation was applied the Tropical Z-R relationship (Z=32R1.65), it has been confirmed that the accuracy is improved in the extreme rainfall events. In addition, the performance of blended multi-sensor combining rainfall was improved in 60mm/h rainfall and more strong heavy rainfall events. Moreover, we adjusted to forecast the urban discharge using Storm Water Management Model (SWMM). Several statistical methods have been used for assessment of model simulation between observed and simulated discharge. In terms of the correlation coefficient and r-squared discharge between observed and forecasted were highly correlated. Based on this study, we captured a possibility of real-time urban discharge forecasting system using remotely sensed data and its utilization for real-time flood warning. Acknowledgement This research was supported by a grant (13AWMP-B066744-01) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korean government.

A Decadal Historical Satellite Data and Rainfall Trend Analysis (2001–2016 for Flood Hazard Mapping in Sri Lanka

Directory of Open Access Journals (Sweden)

Niranga Alahacoon

2018-03-01

Full Text Available Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016 for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km2 for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km2. The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures.

Rainy Day: A Remote Sensing-Driven Extreme Rainfall Simulation Approach for Hazard Assessment

Science.gov (United States)

Wright, Daniel; Yatheendradas, Soni; Peters-Lidard, Christa; Kirschbaum, Dalia; Ayalew, Tibebu; Mantilla, Ricardo; Krajewski, Witold

2015-04-01

Progress on the assessment of rainfall-driven hazards such as floods and landslides has been hampered by the challenge of characterizing the frequency, intensity, and structure of extreme rainfall at the watershed or hillslope scale. Conventional approaches rely on simplifying assumptions and are strongly dependent on the location, the availability of long-term rain gage measurements, and the subjectivity of the analyst. Regional and global-scale rainfall remote sensing products provide an alternative, but are limited by relatively short (~15-year) observational records. To overcome this, we have coupled these remote sensing products with a space-time resampling framework known as stochastic storm transposition (SST). SST "lengthens" the rainfall record by resampling from a catalog of observed storms from a user-defined region, effectively recreating the regional extreme rainfall hydroclimate. This coupling has been codified in Rainy Day, a Python-based platform for quickly generating large numbers of probabilistic extreme rainfall "scenarios" at any point on the globe. Rainy Day is readily compatible with any gridded rainfall dataset. The user can optionally incorporate regional rain gage or weather radar measurements for bias correction using the Precipitation Uncertainties for Satellite Hydrology (PUSH) framework. Results from Rainy Day using the CMORPH satellite precipitation product are compared with local observations in two examples. The first example is peak discharge estimation in a medium-sized (~4000 square km) watershed in the central United States performed using CUENCAS, a parsimonious physically-based distributed hydrologic model. The second example is rainfall frequency analysis for Saint Lucia, a small volcanic island in the eastern Caribbean that is prone to landslides and flash floods. The distinct rainfall hydroclimates of the two example sites illustrate the flexibility of the approach and its usefulness for hazard analysis in data-poor regions.

Rainfall Erosivity in Europe

DEFF Research Database (Denmark)

Panagos, Panos; Ballabio, Cristiano; Borrelli, Pasquale

2015-01-01

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the Rfactor in the USLE model and its revised version, RUSLE. At national...... and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based...

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.

ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

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Z.-G. Zhou

2016-06-01

Full Text Available 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%.

On the possibility of calibrating urban storm-water drainage models using gauge-based adjusted radar rainfall estimates

OpenAIRE

Ochoa-Rodriguez, S; Wang, L; Simoes, N; Onof, C; Maksimovi?, ?

2013-01-01

24/07/14 meb. Authors did not sign CTA. Traditionally, urban storm water drainage models have been calibrated using only raingauge data, which may result in overly conservative models due to the lack of spatial description of rainfall. With the advent of weather radars, radar rainfall estimates with higher temporal and spatial resolution have become increasingly available and have started to be used operationally for urban storm water model calibration and real time operation. Nonetheless,...

Monsoon onset over Kerala and pre monsoon rainfall peak

Digital Repository Service at National Institute of Oceanography (India)

RameshKumar, M.R.; Shenoi, S.S.C.; Shankar, D.

and the monsoon onset date over Kerala was found to be 0.72, which was statistically significant. Thus, as is felt that the pre monsoon rainfall estimate from the satellite data can be used for predicting the monsoon onset over Kerala coast. The results...

An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa

NARCIS (Netherlands)

Kimani, M.W.; Hoedjes, Johannes Cornelis Bernardus; Su, Z.

2017-01-01

Accurate and consistent rainfall observations are vital for climatological studies in support of better agricultural and water management decision-making and planning. In East Africa, accurate rainfall estimation with an adequate spatial distribution is limited due to sparse rain gauge networks.

The effects of rectification and Global Positioning System errors on satellite image-based estimates of forest area

Science.gov (United States)

Ronald E. McRoberts

2010-01-01

Satellite image-based maps of forest attributes are of considerable interest and are used for multiple purposes such as international reporting by countries that have no national forest inventory and small area estimation for all countries. Construction of the maps typically entails, in part, rectifying the satellite images to a geographic coordinate system, observing...

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

Science.gov (United States)

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

2017-05-01

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

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.

Tropical Rainfall Analysis Using TRMM in Combination With Other Satellite Gauge Data: Comparison with Global Precipitation Climatology Project (GPCP) Results

Science.gov (United States)

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

1999-01-01

This paper describes recent results of using Tropical Rainfall Measuring Mission (TRMM) information as the key calibration tool in a merged analysis on a 1 deg x 1 deg latitude/longitude monthly scale based on multiple satellite sources and raingauge analysis. The procedure used to produce the GPCP data set is a stepwise approach which first combines the satellite low-orbit microwave and geosynchronous IR observations into a "multi-satellite" product and than merges that result with the raingauge analysis. Preliminary results produced with the still-stabilizing TRMM algorithms indicate that TRMM shows tighter spatial gradients in tropical rain maxima with higher peaks in the center of the maxima. The TRMM analyses will be used to evaluate the evolution of the 1998 ENSO variations, again in comparison with the GPCP analyses.

Ceilometer-based Rainfall Rate estimates in the framework of VORTEX-SE campaign: A discussion

Science.gov (United States)

Barragan, Ruben; Rocadenbosch, Francesc; Waldinger, Joseph; Frasier, Stephen; Turner, Dave; Dawson, Daniel; Tanamachi, Robin

2017-04-01

During Spring 2016 the first season of the Verification of the Origins of Rotation in Tornadoes EXperiment-Southeast (VORTEX-SE) was conducted in the Huntsville, AL environs. Foci of VORTEX-SE include the characterization of the tornadic environments specific to the Southeast US as well as societal response to forecasts and warnings. Among several experiments, a research team from Purdue University and from the University of Massachusetts Amherst deployed a mobile S-band Frequency-Modulated Continuous-Wave (FMCW) radar and a co-located Vaisala CL31 ceilometer for a period of eight weeks near Belle Mina, AL. Portable disdrometers (DSDs) were also deployed in the same area by Purdue University, occasionally co-located with the radar and lidar. The NOAA National Severe Storms Laboratory also deployed the Collaborative Lower Atmosphere Mobile Profiling System (CLAMPS) consisting of a Doppler lidar, a microwave radiometer, and an infrared spectrometer. The purpose of these profiling instruments was to characterize the atmospheric boundary layer evolution over the course of the experiment. In this paper we focus on the lidar-based retrieval of rainfall rate (RR) and its limitations using observations from intensive observation periods during the experiment: 31 March and 29 April 2016. Departing from Lewandowski et al., 2009, the RR was estimated by the Vaisala CL31 ceilometer applying the slope method (Kunz and Leeuw, 1993) to invert the extinction caused by the rain. Extinction retrievals are fitted against RR estimates from the disdrometer in order to derive a correlation model that allows us to estimate the RR from the ceilometer in similar situations without a disdrometer permanently deployed. The problem of extinction retrieval is also studied from the perspective of Klett-Fernald-Sasano's (KFS) lidar inversion algorithm (Klett, 1981; 1985), which requires the assumption of an aerosol extinction-to-backscatter ratio (the so-called lidar ratio) and calibration in a

TRMM and Other Sources Rainfall Product (TRMM Product 3B43) V7

Data.gov (United States)

National Aeronautics and Space Administration — The Tropical Rainfall Measuring Mission (TRMM) is a joint U.S.-Japan satellite mission to monitor tropical and subtropical precipitation and to estimate its...

A Study on Fuel Estimation Algorithms for a Geostationary Communication & Broadcasting Satellite

OpenAIRE

Jong Won Eun

2000-01-01

It has been developed to calculate fuel budget for a geostationary communication and broadcasting satellite. It is quite essential that the pre-launch fuel budget estimation must account for the deterministic transfer and drift orbit maneuver requirements. After on-station, the calculation of satellite lifetime should be based on the estimation of remaining fuel and assessment of actual performance. These estimations step from the proper algorithms to produce the prediction of satellite lifet...

A Study on Fuel Estimation Algorithms for a Geostationary Communication & Broadcasting Satellite

Directory of Open Access Journals (Sweden)

Jong Won Eun

2000-12-01

Full Text Available It has been developed to calculate fuel budget for a geostationary communication and broadcasting satellite. It is quite essential that the pre-launch fuel budget estimation must account for the deterministic transfer and drift orbit maneuver requirements. After on-station, the calculation of satellite lifetime should be based on the estimation of remaining fuel and assessment of actual performance. These estimations step from the proper algorithms to produce the prediction of satellite lifetime. This paper concentrates on the fuel estimation method that was studied for calculation of the propellant budget by using the given algorithms. Applications of this method are discussed for a communication and broadcasting satellite.

Estimation of Areal Mean Rainfall in Remote Areas Using B-SHADE Model

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Tao Zhang

2016-01-01

Full Text Available This study presented a method to estimate areal mean rainfall (AMR using a Biased Sentinel Hospital Based Area Disease Estimation (B-SHADE model, together with biased rain gauge observations and Tropical Rainfall Measuring Mission (TRMM data, for remote areas with a sparse and uneven distribution of rain gauges. Based on the B-SHADE model, the best linear unbiased estimation of AMR could be obtained. A case study was conducted for the Three-River Headwaters region in the Tibetan Plateau of China, and its performance was compared with traditional methods. The results indicated that B-SHADE obtained the least estimation biases, with a mean error and root mean square error of −0.63 and 3.48 mm, respectively. For the traditional methods including arithmetic average, Thiessen polygon, and ordinary kriging, the mean errors were 7.11, −1.43, and 2.89 mm, which were up to 1027.1%, 127.0%, and 358.3%, respectively, greater than for the B-SHADE model. The root mean square errors were 10.31, 4.02, and 6.27 mm, which were up to 196.1%, 15.5%, and 80.0%, respectively, higher than for the B-SHADE model. The proposed technique can be used to extend the AMR record to the presatellite observation period, when only the gauge data are available.

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

Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

Science.gov (United States)

Atencia, A.; Llasat, M. C.; Garrote, L.; Mediero, L.

2010-10-01

The performance of distributed hydrological models depends on the resolution, both spatial and temporal, of the rainfall surface data introduced. The estimation of quantitative precipitation from meteorological radar or satellite can improve hydrological model results, thanks to an indirect estimation at higher spatial and temporal resolution. In this work, composed radar data from a network of three C-band radars, with 6-minutal temporal and 2 × 2 km2 spatial resolution, provided by the Catalan Meteorological Service, is used to feed the RIBS distributed hydrological model. A Window Probability Matching Method (gage-adjustment method) is applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation in both convective and stratiform Z/R relations used over Catalonia. Once the rainfall field has been adequately obtained, an advection correction, based on cross-correlation between two consecutive images, was introduced to get several time resolutions from 1 min to 30 min. Each different resolution is treated as an independent event, resulting in a probable range of input rainfall data. This ensemble of rainfall data is used, together with other sources of uncertainty, such as the initial basin state or the accuracy of discharge measurements, to calibrate the RIBS model using probabilistic methodology. A sensitivity analysis of time resolutions was implemented by comparing the various results with real values from stream-flow measurement stations.

Parameter estimation in stochastic rainfall-runoff models

DEFF Research Database (Denmark)

Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur

2006-01-01

A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...

A scattering-based over-land rainfall retrieval algorithm for South Korea using GCOM-W1/AMSR-2 data

Science.gov (United States)

Kwon, Young-Joo; Shin, Hayan; Ban, Hyunju; Lee, Yang-Won; Park, Kyung-Ae; Cho, Jaeil; Park, No-Wook; Hong, Sungwook

2017-08-01

Heavy summer rainfall is a primary natural disaster affecting lives and properties in the Korean Peninsula. This study presents a satellite-based rainfall rate retrieval algorithm for the South Korea combining polarization-corrected temperature ( PCT) and scattering index ( SI) data from the 36.5 and 89.0 GHz channels of the Advanced microwave Scanning Radiometer 2 (AMSR-2) onboard the Global Change Observation Mission (GCOM)-W1 satellite. The coefficients for the algorithm were obtained from spatial and temporal collocation data from the AMSR-2 and groundbased automatic weather station rain gauges from 1 July - 30 August during the years, 2012-2015. There were time delays of about 25 minutes between the AMSR-2 observations and the ground raingauge measurements. A new linearly-combined rainfall retrieval algorithm focused on heavy rain for the PCT and SI was validated using ground-based rainfall observations for the South Korea from 1 July - 30 August, 2016. The validation presented PCT and SI methods showed slightly improved results for rainfall > 5 mm h-1 compared to the current ASMR-2 level 2 data. The best bias and root mean square error (RMSE) for the PCT method at AMSR-2 36.5 GHz were 2.09 mm h-1 and 7.29 mm h-1, respectively, while the current official AMSR-2 rainfall rates show a larger bias and RMSE (4.80 mm h-1 and 9.35 mm h-1, respectively). This study provides a scatteringbased over-land rainfall retrieval algorithm for South Korea affected by stationary front rain and typhoons with the advantages of the previous PCT and SI methods to be applied to a variety of spaceborne passive microwave radiometers.

Incorporation of Satellite Data and Uncertainty in a Nationwide Groundwater Recharge Model in New Zealand

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Rogier Westerhoff

2018-01-01

Full Text Available A nationwide model of groundwater recharge for New Zealand (NGRM, as described in this paper, demonstrated the benefits of satellite data and global models to improve the spatial definition of recharge and the estimation of recharge uncertainty. NGRM was inspired by the global-scale WaterGAP model but with the key development of rainfall recharge calculation on scales relevant to national- and catchment-scale studies (i.e., a 1 km × 1 km cell size and a monthly timestep in the period 2000–2014 provided by satellite data (i.e., MODIS-derived evapotranspiration, AET and vegetation in combination with national datasets of rainfall, elevation, soil and geology. The resulting nationwide model calculates groundwater recharge estimates, including their uncertainty, consistent across the country, which makes the model unique compared to all other New Zealand estimates targeted towards groundwater recharge. At the national scale, NGRM estimated an average recharge of 2500 m 3 /s, or 298 mm/year, with a model uncertainty of 17%. Those results were similar to the WaterGAP model, but the improved input data resulted in better spatial characteristics of recharge estimates. Multiple uncertainty analyses led to these main conclusions: the NGRM model could give valuable initial estimates in data-sparse areas, since it compared well to most ground-observed lysimeter data and local recharge models; and the nationwide input data of rainfall and geology caused the largest uncertainty in the model equation, which revealed that the satellite data could improve spatial characteristics without significantly increasing the uncertainty. Clearly the increasing volume and availability of large-scale satellite data is creating more opportunities for the application of national-scale models at the catchment, and smaller, scales. This should result in improved utility of these models including provision of initial estimates in data-sparse areas. Topics for future

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

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Bruno Bonemberger da Silva

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

Evaluation of NWP-based Satellite Precipitation Error Correction with Near-Real-Time Model Products and Flood-inducing Storms

Science.gov (United States)

Zhang, X.; Anagnostou, E. N.; Schwartz, C. S.

2017-12-01

Satellite precipitation products tend to have significant biases over complex terrain. Our research investigates a statistical approach for satellite precipitation adjustment based solely on numerical weather simulations. This approach has been evaluated in two mid-latitude (Zhang et al. 2013*1, Zhang et al. 2016*2) and three topical mountainous regions by using the WRF model to adjust two high-resolution satellite products i) National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center morphing technique (CMORPH) and ii) Global Satellite Mapping of Precipitation (GSMaP). Results show the adjustment effectively reduces the satellite underestimation of high rain rates, which provides a solid proof-of-concept for continuing research of NWP-based satellite correction. In this study we investigate the feasibility of using NCAR Real-time Ensemble Forecasts*3 for adjusting near-real-time satellite precipitation datasets over complex terrain areas in the Continental United States (CONUS) such as Olympic Peninsula, California coastal mountain ranges, Rocky Mountains and South Appalachians. The research will focus on flood-inducing storms occurred from May 2015 to December 2016 and four satellite precipitation products (CMORPH, GSMaP, PERSIANN-CCS and IMERG). The error correction performance evaluation will be based on comparisons against the gauge-adjusted Stage IV precipitation data. *1 Zhang, Xinxuan, et al. "Using NWP simulations in satellite rainfall estimation of heavy precipitation events over mountainous areas." Journal of Hydrometeorology 14.6 (2013): 1844-1858. *2 Zhang, Xinxuan, et al. "Hydrologic Evaluation of NWP-Adjusted CMORPH Estimates of Hurricane-Induced Precipitation in the Southern Appalachians." Journal of Hydrometeorology 17.4 (2016): 1087-1099. *3 Schwartz, Craig S., et al. "NCAR's experimental real-time convection-allowing ensemble prediction system." Weather and Forecasting 30.6 (2015): 1645-1654.

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

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Guanghua Wei

2017-12-01

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

Analysis on the Critical Rainfall Value For Predicting Large Scale Landslides Caused by Heavy Rainfall In Taiwan.

Science.gov (United States)

Tsai, Kuang-Jung; Chiang, Jie-Lun; Lee, Ming-Hsi; Chen, Yie-Ruey

2017-04-01

Analysis on the Critical Rainfall Value For Predicting Large Scale Landslides Caused by Heavy Rainfall In Taiwan. Kuang-Jung Tsai 1, Jie-Lun Chiang 2,Ming-Hsi Lee 2, Yie-Ruey Chen 1, 1Department of Land Management and Development, Chang Jung Christian Universityt, Tainan, Taiwan. 2Department of Soil and Water Conservation, National Pingtung University of Science and Technology, Pingtung, Taiwan. ABSTRACT The accumulated rainfall amount was recorded more than 2,900mm that were brought by Morakot typhoon in August, 2009 within continuous 3 days. Very serious landslides, and sediment related disasters were induced by this heavy rainfall event. The satellite image analysis project conducted by Soil and Water Conservation Bureau after Morakot event indicated that more than 10,904 sites of landslide with total sliding area of 18,113ha were found by this project. At the same time, all severe sediment related disaster areas are also characterized based on their disaster type, scale, topography, major bedrock formations and geologic structures during the period of extremely heavy rainfall events occurred at the southern Taiwan. Characteristics and mechanism of large scale landslide are collected on the basis of the field investigation technology integrated with GPS/GIS/RS technique. In order to decrease the risk of large scale landslides on slope land, the strategy of slope land conservation, and critical rainfall database should be set up and executed as soon as possible. Meanwhile, study on the establishment of critical rainfall value used for predicting large scale landslides induced by heavy rainfall become an important issue which was seriously concerned by the government and all people live in Taiwan. The mechanism of large scale landslide, rainfall frequency analysis ,sediment budge estimation and river hydraulic analysis under the condition of extremely climate change during the past 10 years would be seriously concerned and recognized as a required issue by this

Impact of Satellite Remote Sensing Data on Simulations of Coastal Circulation and Hypoxia on the Louisiana Continental Shelf

OpenAIRE

Dong S. Ko; Richard W. Gould; Bradley Penta; John C. Lehrter

2016-01-01

We estimated surface salinity flux and solar penetration from satellite data, and performed model simulations to examine the impact of including the satellite estimates on temperature, salinity, and dissolved oxygen distributions on the Louisiana continental shelf (LCS) near the annual hypoxic zone. Rainfall data from the Tropical Rainfall Measurement Mission (TRMM) were used for the salinity flux, and the diffuse attenuation coefficient (Kd) from Moderate Resolution Imaging Spectroradiometer...

Finite Element Method Application in Areal Rainfall Estimation Case Study; Mashhad Plain Basin

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M. Irani

2016-10-01

Full Text Available Introduction: The hydrological models are very important tools for planning and management of water resources. These models can be used for identifying basin and nature problems and choosing various managements. Precipitation is based on these models. Calculations of rainfall would be affected by displacement and region factor such as topography, etc. Estimating areal rainfall is one of the basic needs in meteorological, water resources and others studies. There are various methods for the estimation of rainfall, which can be evaluated by using statistical data and mathematical terms. In hydrological analysis, areal rainfall is so important because of displacement of precipitation. Estimating areal rainfall is divided to three methods: 1- graphical. 2-topographical. 3-numerical. This paper represented calculating mean precipitation (daily, monthly and annual using Galerkin’s method (numerical method and it was compared with other methods such as kriging, IDW, Thiessen and arithmetic mean. In this study, there were 42 actual gauges and thirteen dummies in Mashhad plain basin which is calculated by Galerkin’s method. The method included the use of interpolation functions, allowing an accurate representation of shape and relief of catchment with numerical integration performed by Gaussian quadrature and represented the allocation of weights to stations. Materials and Methods:The estimation of areal rainfall (daily, monthly,… is the basic need for meteorological project. In this field ,there are various methods that one of them is finite element method. Present study aimed to estimate areal rainfall with a 16-year period (1997-2012 by using Galerkin method ( finite element in Mashhad plain basin for 42 station. Therefore, it was compared with other usual methods such as arithmetic mean, Thiessen, Kriging and IDW. The analysis of Thiessen, Kriging and IDW were in ArcGIS10.0 software environment and finite element analysis did by using of Matlab

Rainfall Modification by Urban Areas: New Perspectives from TRMM

Science.gov (United States)

Shepherd, J. Marshall; Pierce, Harold F.; Negri, Andrew

2002-01-01

Data from the Tropical Rainfall Measuring Mission's (TRMM) Precipitation Radar (PR) were employed to identify warm season rainfall (1998-2000) patterns around Atlanta, Montgomery, Nashville, San Antonio, Waco, and Dallas. Results reveal an average increase of -28% in monthly rainfall rates within 30-60 kilometers downwind of the metropolis with a modest increase of 5.6% over the metropolis. Portions of the downwind area exhibit increases as high as 51%. The percentage changes are relative to an upwind control area. It was also found that maximum rainfall rates in the downwind impact area exceeded the mean value in the upwind control area by 48% - 116%. The maximum value was generally found at an average distance of 39 km from the edge of the urban center or 64 km from the center of the city. Results are consistent with METROMEX studies of St. Louis almost two decades ago and with more recent studies near Atlanta. Future work is extending the investigation to Phoenix, Arizona, an arid U.S. city, and several international cities like Mexico City, Johannesburg, and Brasilia. The study establishes the possibility of utilizing satellite-based rainfall estimates for examining rainfall modification by urban areas on global scales and over longer time periods. Such research has implications for weather forecasting, urban planning, water resource management, and understanding human impact on the environment and climate.

Determining the Uncertainties in Prescribed Burn Emissions Through Comparison of Satellite Estimates to Ground-based Estimates and Air Quality Model Evaluations in Southeastern US

Science.gov (United States)

Odman, M. T.; Hu, Y.; Russell, A. G.

2016-12-01

Prescribed burning is practiced throughout the US, and most widely in the Southeast, for the purpose of maintaining and improving the ecosystem, and reducing the wildfire risk. However, prescribed burn emissions contribute significantly to the of trace gas and particulate matter loads in the atmosphere. In places where air quality is already stressed by other anthropogenic emissions, prescribed burns can lead to major health and environmental problems. Air quality modeling efforts are under way to assess the impacts of prescribed burn emissions. Operational forecasts of the impacts are also emerging for use in dynamic management of air quality as well as the burns. Unfortunately, large uncertainties exist in the process of estimating prescribed burn emissions and these uncertainties limit the accuracy of the burn impact predictions. Prescribed burn emissions are estimated by using either ground-based information or satellite observations. When there is sufficient local information about the burn area, the types of fuels, their consumption amounts, and the progression of the fire, ground-based estimates are more accurate. In the absence of such information satellites remain as the only reliable source for emission estimation. To determine the level of uncertainty in prescribed burn emissions, we compared estimates derived from a burn permit database and other ground-based information to the estimates by the Biomass Burning Emissions Product derived from a constellation of NOAA and NASA satellites. Using these emissions estimates we conducted simulations with the Community Multiscale Air Quality (CMAQ) model and predicted trace gas and particulate matter concentrations throughout the Southeast for two consecutive burn seasons (2015 and 2016). In this presentation, we will compare model predicted concentrations to measurements at monitoring stations and evaluate if the differences are commensurate with our emission uncertainty estimates. We will also investigate if

The assessment of Global Precipitation Measurement estimates over the Indian subcontinent

Science.gov (United States)

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

2017-08-01

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

Evaluating a slope-stability model for shallow rain-induced landslides using gage and satellite data

Science.gov (United States)

Yatheendradas, S.; Kirschbaum, D.; Baum, Rex L.; Godt, Jonathan W.

2014-01-01

Improving prediction of landslide early warning systems requires accurate estimation of the conditions that trigger slope failures. This study tested a slope-stability model for shallow rainfall-induced landslides by utilizing rainfall information from gauge and satellite records. We used the TRIGRS model (Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis) for simulating the evolution of the factor of safety due to rainfall infiltration. Using a spatial subset of a well-characterized digital landscape from an earlier study, we considered shallow failure on a slope adjoining an urban transportation roadway near the Seattle area in Washington, USA.We ran the TRIGRS model using high-quality rain gage and satellite-based rainfall data from the Tropical Rainfall Measuring Mission (TRMM). Preliminary results with parameterized soil depth values suggest that the steeper slope values in this spatial domain have factor of safety values that are extremely close to the failure limit within an extremely narrow range of values, providing multiple false alarms. When the soil depths were constrained using a back analysis procedure to ensure that slopes were stable under initial condtions, the model accurately predicted the timing and location of the landslide observation without false alarms over time for gage rain data. The TRMM satellite rainfall data did not show adequately retreived rainfall peak magnitudes and accumulation over the study period, and as a result failed to predict the landslide event. These preliminary results indicate that more accurate and higher-resolution rain data (e.g., the upcoming Global Precipitation Measurement (GPM) mission) are required to provide accurate and reliable landslide predictions in ungaged basins.

Event-based stochastic point rainfall resampling for statistical replication and climate projection of historical rainfall series

DEFF Research Database (Denmark)

Thorndahl, Søren; Korup Andersen, Aske; Larsen, Anders Badsberg

2017-01-01

Continuous and long rainfall series are a necessity in rural and urban hydrology for analysis and design purposes. Local historical point rainfall series often cover several decades, which makes it possible to estimate rainfall means at different timescales, and to assess return periods of extreme...... includes climate changes projected to a specific future period. This paper presents a framework for resampling of historical point rainfall series in order to generate synthetic rainfall series, which has the same statistical properties as an original series. Using a number of key target predictions...... for the future climate, such as winter and summer precipitation, and representation of extreme events, the resampled historical series are projected to represent rainfall properties in a future climate. Climate-projected rainfall series are simulated by brute force randomization of model parameters, which leads...

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.

TRMM 3-Hourly 0.25 deg. TRMM and Other-GPI Calibration Rainfall Data V7

Data.gov (United States)

National Aeronautics and Space Administration — The Tropical Rainfall Measuring Mission (TRMM) is a joint U.S.-Japan satellite mission to monitor tropical and subtropical precipitation and to estimate its...

Dry/Wet Conditions Monitoring Based on TRMM Rainfall Data and Its Reliability Validation over Poyang Lake Basin, China

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Xianghu Li

2013-11-01

Full Text Available Local dry/wet conditions are of great concern in regional water resource and floods/droughts disaster risk management. Satellite-based precipitation products have greatly improved their accuracy and applicability and are expected to offer an alternative to ground rain gauges data. This paper investigated the capability of Tropical Rainfall Measuring Mission (TRMM rainfall data for monitoring the temporal and spatial variation of dry/wet conditions in Poyang Lake basin during 1998–2010, and validated its reliability with rain gauges data from 14 national meteorological stations in the basin. The results show that: (1 the daily TRMM rainfall data does not describe the occurrence and contribution rates of precipitation accurately, but monthly TRMM data have a good linear relationship with rain gauges rainfall data; (2 both the Z index and Standardized Precipitation Index (SPI based on monthly TRMM rainfall data oscillate around zero and show a consistent interannual variability as compared with rain gauges data; (3 the spatial pattern of moisture status, either in dry months or wet months, based on both the Z index and SPI using TRMM data, agree with the observed rainfall. In conclusion, the monthly TRMM rainfall data can be used for monitoring the variation and spatial distribution of dry/wet conditions in Poyang Lake basin.

Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications

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L.-P. Wang

2015-09-01

Full Text Available Gauge-based radar rainfall adjustment techniques have been widely used to improve the applicability of radar rainfall estimates to large-scale hydrological modelling. However, their use for urban hydrological applications is limited as they were mostly developed based upon Gaussian approximations and therefore tend to smooth off so-called "singularities" (features of a non-Gaussian field that can be observed in the fine-scale rainfall structure. Overlooking the singularities could be critical, given that their distribution is highly consistent with that of local extreme magnitudes. This deficiency may cause large errors in the subsequent urban hydrological modelling. To address this limitation and improve the applicability of adjustment techniques at urban scales, a method is proposed herein which incorporates a local singularity analysis into existing adjustment techniques and allows the preservation of the singularity structures throughout the adjustment process. In this paper the proposed singularity analysis is incorporated into the Bayesian merging technique and the performance of the resulting singularity-sensitive method is compared with that of the original Bayesian (non singularity-sensitive technique and the commonly used mean field bias adjustment. This test is conducted using as case study four storm events observed in the Portobello catchment (53 km2 (Edinburgh, UK during 2011 and for which radar estimates, dense rain gauge and sewer flow records, as well as a recently calibrated urban drainage model were available. The results suggest that, in general, the proposed singularity-sensitive method can effectively preserve the non-normality in local rainfall structure, while retaining the ability of the original adjustment techniques to generate nearly unbiased estimates. Moreover, the ability of the singularity-sensitive technique to preserve the non-normality in rainfall estimates often leads to better reproduction of the urban

Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications

Science.gov (United States)

Wang, L.-P.; Ochoa-Rodríguez, S.; Onof, C.; Willems, P.

2015-09-01

Gauge-based radar rainfall adjustment techniques have been widely used to improve the applicability of radar rainfall estimates to large-scale hydrological modelling. However, their use for urban hydrological applications is limited as they were mostly developed based upon Gaussian approximations and therefore tend to smooth off so-called "singularities" (features of a non-Gaussian field) that can be observed in the fine-scale rainfall structure. Overlooking the singularities could be critical, given that their distribution is highly consistent with that of local extreme magnitudes. This deficiency may cause large errors in the subsequent urban hydrological modelling. To address this limitation and improve the applicability of adjustment techniques at urban scales, a method is proposed herein which incorporates a local singularity analysis into existing adjustment techniques and allows the preservation of the singularity structures throughout the adjustment process. In this paper the proposed singularity analysis is incorporated into the Bayesian merging technique and the performance of the resulting singularity-sensitive method is compared with that of the original Bayesian (non singularity-sensitive) technique and the commonly used mean field bias adjustment. This test is conducted using as case study four storm events observed in the Portobello catchment (53 km2) (Edinburgh, UK) during 2011 and for which radar estimates, dense rain gauge and sewer flow records, as well as a recently calibrated urban drainage model were available. The results suggest that, in general, the proposed singularity-sensitive method can effectively preserve the non-normality in local rainfall structure, while retaining the ability of the original adjustment techniques to generate nearly unbiased estimates. Moreover, the ability of the singularity-sensitive technique to preserve the non-normality in rainfall estimates often leads to better reproduction of the urban drainage system

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

Science.gov (United States)

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

2014-02-01

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

Determination of mean rainfall from the Special Sensor Microwave/Imager (SSM/I) using a mixed lognormal distribution

Science.gov (United States)

Berg, Wesley; Chase, Robert

1992-01-01

Global estimates of monthly, seasonal, and annual oceanic rainfall are computed for a period of one year using data from the Special Sensor Microwave/Imager (SSM/I). Instantaneous rainfall estimates are derived from brightness temperature values obtained from the satellite data using the Hughes D-matrix algorithm. The instantaneous rainfall estimates are stored in 1 deg square bins over the global oceans for each month. A mixed probability distribution combining a lognormal distribution describing the positive rainfall values and a spike at zero describing the observations indicating no rainfall is used to compute mean values. The resulting data for the period of interest are fitted to a lognormal distribution by using a maximum-likelihood. Mean values are computed for the mixed distribution and qualitative comparisons with published historical results as well as quantitative comparisons with corresponding in situ raingage data are performed.

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

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

2006-01-01

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

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

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

2004-01-01

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

Estimation of typhoon rainfall in GaoPing River: A Multivariate Maximum Entropy Method

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Pei-Jui, Wu; Hwa-Lung, Yu

2016-04-01

The heavy rainfall from typhoons is the main factor of the natural disaster in Taiwan, which causes the significant loss of human lives and properties. Statistically average 3.5 typhoons invade Taiwan every year, and the serious typhoon, Morakot in 2009, impacted Taiwan in recorded history. Because the duration, path and intensity of typhoon, also affect the temporal and spatial rainfall type in specific region , finding the characteristics of the typhoon rainfall type is advantageous when we try to estimate the quantity of rainfall. This study developed a rainfall prediction model and can be divided three parts. First, using the EEOF(extended empirical orthogonal function) to classify the typhoon events, and decompose the standard rainfall type of all stations of each typhoon event into the EOF and PC(principal component). So we can classify the typhoon events which vary similarly in temporally and spatially as the similar typhoon types. Next, according to the classification above, we construct the PDF(probability density function) in different space and time by means of using the multivariate maximum entropy from the first to forth moment statistically. Therefore, we can get the probability of each stations of each time. Final we use the BME(Bayesian Maximum Entropy method) to construct the typhoon rainfall prediction model , and to estimate the rainfall for the case of GaoPing river which located in south of Taiwan.This study could be useful for typhoon rainfall predictions in future and suitable to government for the typhoon disaster prevention .

Improvement of Statistical Typhoon Rainfall Forecasting with ANN-Based Southwest Monsoon Enhancement

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Tsung-Yi Pan

2011-01-01

Full Text Available Typhoon Morakot 2009, with significant southwest monsoon flow, produced a record-breaking rainfall of 2361 mm in 48 hours. This study hopes to improve a statistical typhoon rainfall forecasting method used over the mountain region of Taiwan via an artificial neural network based southwest monsoon enhancement (ANNSME model. Rainfall data collected at two mountain weather stations, ALiShan and YuShan, are analyzed to establish the relation to the southwest monsoon moisture flux which is calculated at a designated sea area southwest of Taiwan. The results show that the moisture flux, with southwest monsoon flow, transported water vapor during the landfall periods of Typhoons Mindulle, Bilis, Fungwong, Kalmaegi, Haitaing and Morakot. Based on the moisture flux, a linear regression is used to identify an effective value of moisture flux as the threshold flux which can enhance mountain rainfall in southwestern Taiwan. In particular, a feedforward neural network (FNN is applied to estimate the residuals from the linear model to the differences between simulated rainfalls by a typhoon rainfall climatology model (TRCM and observations. Consequently, the ANNSME model integrates the effective moisture flux, linear rainfall model and the FNN for residuals. Even with very limited training cases, our results indicate that the ANNSME model is robust and suitable for improvement of TRCM rainfall prediction. The improved prediction of the total rainfall and of the multiple rainfall peaks is important for emergency operation.

FLASH-FLOOD MODELLING WITH ARTIFICIAL NEURAL NETWORKS USING RADAR RAINFALL ESTIMATES

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Dinu Cristian

2017-09-01

Full Text Available The use of artificial neural networks (ANNs in modelling the hydrological processes has become a common approach in the last two decades, among side the traditional methods. In regard to the rainfall-runoff modelling, in both traditional and ANN models the use of ground rainfall measurements is prevalent, which can be challenging in areas with low rain gauging station density, especially in catchments where strong focused rainfall can generate flash-floods. The weather radar technology can prove to be a solution for such areas by providing rain estimates with good time and space resolution. This paper presents a comparison between different ANN setups using as input both ground and radar observations for modelling the rainfall-runoff process for Bahluet catchment, with focus on a flash-flood observed in the catchment.

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

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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

Rainfall erosivity factor estimation in Republic of Moldova

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Castraveš, Tudor; Kuhn, Nikolaus

2017-04-01

Rainfall erosivity represents a measure of the erosive force of rainfall. Typically, it is expressed as variable such as the R factor in the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1965, 1978) or its derivates. The rainfall erosivity index for a rainfall event (EI30) is calculated from the total kinetic energy and maximum 30 minutes intensity of individual events. However, these data are often unavailable for wide regions and countries. Usually, there are three issues regarding precipitation data: low temporal resolution, low spatial density and limited access to the data. This is especially true for some of postsoviet countries from Eastern Europe, such as Republic of Moldova, where soil erosion is a real and persistent problem (Summer, 2003) and where soils represents the main natural resource of the country. Consequently, researching and managing soil erosion is particularly important. The purpose of this study is to develop a model based on commonly available rainfall data, such as event, daily or monthly amounts, to calculate rainfall erosivity for the territory of Republic of Moldova. Rainfall data collected during 1994-2015 period at 15 meteorological stations in the Republic of Moldova, with 10 minutes temporal resolution, were used to develop and calibrate a model to generate an erosivity map of Moldova. References 1. Summer, W., (2003). Soil erosion in the Republic of Moldova — the importance of institutional arrangements. Erosion Prediction in Ungauged Basins: Integrating Methods and Techniques (Proceedings of symposium HS01 held during IUGG2003 at Sapporo. July 2003). IAHS Publ. no. 279. 2. Wischmeier, W.H., and Smith, D.D. (1965). Predicting rainfall-erosion losses from cropland east of the Rocky Mountains. Agr. Handbook No. 282, U.S. Dept. Agr., Washington, DC 3. Wischmeier, W.H., and Smith, D.D. (1978). Predicting rainfall erosion losses. Agr. handbook No. 537, U.S. Dept. of Agr., Science and Education Administration.

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

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Basile Pauthier

2016-01-01

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

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

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

2016-01-01

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

Spatial dependence of extreme rainfall

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Radi, Noor Fadhilah Ahmad; Zakaria, Roslinazairimah; Satari, Siti Zanariah; Azman, Muhammad Az-zuhri

2017-05-01

This study aims to model the spatial extreme daily rainfall process using the max-stable model. The max-stable model is used to capture the dependence structure of spatial properties of extreme rainfall. Three models from max-stable are considered namely Smith, Schlather and Brown-Resnick models. The methods are applied on 12 selected rainfall stations in Kelantan, Malaysia. Most of the extreme rainfall data occur during wet season from October to December of 1971 to 2012. This period is chosen to assure the available data is enough to satisfy the assumption of stationarity. The dependence parameters including the range and smoothness, are estimated using composite likelihood approach. Then, the bootstrap approach is applied to generate synthetic extreme rainfall data for all models using the estimated dependence parameters. The goodness of fit between the observed extreme rainfall and the synthetic data is assessed using the composite likelihood information criterion (CLIC). Results show that Schlather model is the best followed by Brown-Resnick and Smith models based on the smallest CLIC's value. Thus, the max-stable model is suitable to be used to model extreme rainfall in Kelantan. The study on spatial dependence in extreme rainfall modelling is important to reduce the uncertainties of the point estimates for the tail index. If the spatial dependency is estimated individually, the uncertainties will be large. Furthermore, in the case of joint return level is of interest, taking into accounts the spatial dependence properties will improve the estimation process.

Underwater Acoustic Measurements to Estimate Wind and Rainfall in the Mediterranean Sea

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Sara Pensieri

2015-01-01

Full Text Available Oceanic ambient noise measurements can be analyzed to obtain qualitative and quantitative information about wind and rainfall phenomena over the ocean filling the existing gap of reliable meteorological observations at sea. The Ligurian Sea Acoustic Experiment was designed to collect long-term synergistic observations from a passive acoustic recorder and surface sensors (i.e., buoy mounted rain gauge and anemometer and weather radar to support error analysis of rainfall rate and wind speed quantification techniques developed in past studies. The study period included combination of high and low wind and rainfall episodes and two storm events that caused two floods in the vicinity of La Spezia and in the city of Genoa in 2011. The availability of high resolution in situ meteorological data allows improving data processing technique to detect and especially to provide effective estimates of wind and rainfall at sea. Results show a very good correspondence between estimates provided by passive acoustic recorder algorithm and in situ observations for both rainfall and wind phenomena and demonstrate the potential of using measurements provided by passive acoustic instruments in open sea for early warning of approaching coastal storms, which for the Mediterranean coastal areas constitutes one of the main causes of recurrent floods.

Satellite-Based Sunshine Duration for Europe

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Bodo Ahrens

2013-06-01

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

Estimation and comparision of curve numbers based on dynamic land use land cover change, observed rainfall-runoff data and land slope

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Deshmukh, Dhananjay Suresh; Chaube, Umesh Chandra; Ekube Hailu, Ambaye; Aberra Gudeta, Dida; Tegene Kassa, Melaku

2013-06-01

The CN represents runoff potential is estimated using three different methods for three watersheds namely Barureva, Sher and Umar watershed located in Narmada basin. Among three watersheds, Sher watershed has gauging site for the runoff measurements. The CN computed from the observed rainfall-runoff events is termed as CN(PQ), land use and land cover (LULC) is termed as CN(LU) and the CN based on land slope is termed as SACN2. The estimated annual CN(PQ) varies from 69 to 87 over the 26 years data period with median 74 and average 75. The range of CN(PQ) from 70 to 79 are most significant values and these truly represent the AMC II condition for the Sher watershed. The annual CN(LU) was computed for all three watersheds using GIS and the years are 1973, 1989 and 2000. Satellite imagery of MSS, TM and ETM+ sensors are available for these years and obtained from the Global Land Cover Facility Data Center of Maryland University USA. The computed CN(LU) values show rising trend with the time and this trend is attributed to expansion of agriculture area in all watersheds. The predicted values of CN(LU) with time (year) can be used to predict runoff potential under the effect of change in LULC. Comparison of CN(LU) and CN(PQ) values shows close agreement and it also validates the classification of LULC. The estimation of slope adjusted SA-CN2 shows the significant difference over conventional CN for the hilly forest lands. For the micro watershed planning, SCS-CN method should be modified to incorporate the effect of change in land use and land cover along with effect of land slope.

Rainfall Estimation Using Specific Differential Phase for the First Operational Polarimetric Radar in Korea

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Cheol-Hwan You

2014-01-01

Full Text Available To assess the performance of rainfall estimation using specific differential phase observed by Bislsan radar, the first polarimetric radar in Korea, three rainfall cases occurring in 2011 were selected, each caused by different conditions: the first is the Changma front and typhoon, the second is only the Changma front, and the third is only a typhoon. For quantitative use of specific differential phase (KDP, a data quality algorithm was developed for differential phase shift (ΦDP, composed of two steps; the first involves removal of scattered noise and the second is unfolding of ΦDP. This order of the algorithm is necessary so as not to remove unfolded areas, which are the real meteorological target. All noise was removed and the folded ΦDP were unfolded successfully for this study. RKDP relations for S-band radar were calculated for 84,754 samples of observed drop size distribution (DSD using different drop shape assumptions. The relation for the Bringi drop shape showed the best statistics: 0.28 for normalized error, and 6.7 mm for root mean square error for rainfall heavier than 10 mm h-1. Because the drop shape assumption affects the accuracy of rainfall estimation differently for different rainfall types, such characteristics should be taken into account to estimate rainfall more accurately using polarimetric variables.

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

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Sanggyun Lee

2016-08-01

Full Text Available Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011–2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM-bird data collected over two days during the CryoSat Validation experiment (CryoVex field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE = 0.29 m compared to thickness estimation based on existing lead detection methods (RMSE = 0.86–0.93 m. Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011–2013 and rebounded in 2014.

Estimation of Rainfall Associated with Typhoons over the Ocean Using TRMM/TMI and Numerical Models

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Nan-Ching Yeh

2015-11-01

Full Text Available This study quantitatively estimated the precipitation associated with a typhoon in the northwestern Pacific Ocean by using a physical algorithm which included the Weather Research and Forecasting model, Radiative Transfer for TIROS Operational Vertical Sounder model, and data from the Tropical Rainfall Measuring Mission (TRMM/TRMM Microwave Imager (TMI and TRMM/Precipitation Radar (PR. First, a prior probability distribution function (PDF was constructed using over three million rain rate retrievals from the TRMM/PR data for the period 2002–2010 over the northwestern Pacific Ocean. Subsequently, brightness temperatures for 15 typhoons that occurred over the northwestern Pacific Ocean were simulated using a microwave radiative transfer model and a conditional PDF was obtained for these typhoons. The aforementioned physical algorithm involved using a posterior PDF. A posterior PDF was obtained by combining the prior and conditional PDFs. Finally, the rain rate associated with a typhoon was estimated by inputting the observations of the TMI (attenuation indices at 10, 19, 37 GHz into the posterior PDF (lookup table. Results based on rain rate retrievals indicated that rainband locations with the heaviest rainfall showed qualitatively similar horizontal distributions. The correlation coefficient and root-mean-square error of the rain rate estimation were 0.63 and 4.45 mm·h−1, respectively. Furthermore, the correlation coefficient and root-mean-square error for convective rainfall were 0.78 and 7.25 mm·h−1, respectively, and those for stratiform rainfall were 0.58 and 9.60 mm·h−1, respectively. The main contribution of this study is introducing an approach to quickly and accurately estimate the typhoon precipitation, and remove the need for complex calculations.

Curve Number Estimation for a Small Urban Catchment from Recorded Rainfall-Runoff Events

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Banasik Kazimierz

2014-12-01

Full Text Available Runoff estimation is a key component in various hydrological considerations. Estimation of storm runoff is especially important for the effective design of hydraulic and road structures, for the flood flow management, as well as for the analysis of land use changes, i.e. urbanization or low impact development of urban areas. The curve number (CN method, developed by Soil Conservation Service (SCS of the U.S. Department of Agriculture for predicting the flood runoff depth from ungauged catchments, has been in continuous use for ca. 60 years. This method has not been extensively tested in Poland, especially in small urban catchments, because of lack of data. In this study, 39 rainfall-runoff events, collected during four years (2009–2012 in a small (A=28.7 km2, urban catchment of Służew Creek in southwest part of Warsaw were used, with the aim of determining the CNs and to check its applicability to ungauged urban areas. The parameters CN, estimated empirically, vary from 65.1 to 95.0, decreasing with rainfall size and, when sorted rainfall and runoff separately, reaching the value from 67 to 74 for large rainfall events.

Validity and feasibility of a satellite imagery-based method for rapid estimation of displaced populations.

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Checchi, Francesco; Stewart, Barclay T; Palmer, Jennifer J; Grundy, Chris

2013-01-23

Estimating the size of forcibly displaced populations is key to documenting their plight and allocating sufficient resources to their assistance, but is often not done, particularly during the acute phase of displacement, due to methodological challenges and inaccessibility. In this study, we explored the potential use of very high resolution satellite imagery to remotely estimate forcibly displaced populations. Our method consisted of multiplying (i) manual counts of assumed residential structures on a satellite image and (ii) estimates of the mean number of people per structure (structure occupancy) obtained from publicly available reports. We computed population estimates for 11 sites in Bangladesh, Chad, Democratic Republic of Congo, Ethiopia, Haiti, Kenya and Mozambique (six refugee camps, three internally displaced persons' camps and two urban neighbourhoods with a mixture of residents and displaced) ranging in population from 1,969 to 90,547, and compared these to "gold standard" reference population figures from census or other robust methods. Structure counts by independent analysts were reasonably consistent. Between one and 11 occupancy reports were available per site and most of these reported people per household rather than per structure. The imagery-based method had a precision relative to reference population figures of layout. For each site, estimates were produced in 2-5 working person-days. In settings with clearly distinguishable individual structures, the remote, imagery-based method had reasonable accuracy for the purposes of rapid estimation, was simple and quick to implement, and would likely perform better in more current application. However, it may have insurmountable limitations in settings featuring connected buildings or shelters, a complex pattern of roofs and multi-level buildings. Based on these results, we discuss possible ways forward for the method's development.

Toward Continental-scale Rainfall Monitoring Using Commercial Microwave Links From Cellular Communication Networks

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Uijlenhoet, R.; Leijnse, H.; Overeem, A.

2017-12-01

Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal's attenuation between transmitter and receiver. We have previously shown how one such a network can be used to retrieve the space-time dynamics of rainfall for an entire country (The Netherlands, ˜35,500 km2), based on an unprecedented number of links (˜2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrated the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent. The presentation will focus on the potential for upscaling this technique to continental-scale rainfall monitoring in Europe. In addition, several examples of recent applications of this technique on other continents (South America, Africa, Asia and Australia) will be given.

Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

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Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng

2016-05-01

Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

USING THE FOURNIER INDEXES IN ESTIMATING RAINFALL EROSIVITY. CASE STUDY - THE SECAŞUL MARE BASIN

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M. COSTEA

2012-03-01

Full Text Available Using the Fournier Index in Estimating Rainfall Erosivity. Case Study - The Secaşul Mare Basin. Climatic aggressiveness is one of the most important factors in relief dynamic. Of all climatic parameters, rainfall is directly involved in versant dynamic, in the loss of soil quality and through pluvial denudation and the processes associated with it, through the erosivity of torrential rain. We analyzed rainfall aggressiveness based on monthly and annual average values through the Fournier's index (1970 and Fournier's index modified by Arnoldus (1980. They have the advantage that they can be used not only for evaluating the land susceptibility to erosion and the calculation of erodibility of land and soil losses, but also in assessing land susceptibility to sliding (Aghiruş, 2010. The literature illustrates the successful use of this index which provides a summary assessment of the probability of rainfall with significant erosive effects. The results obtained allow observation of differences in space and time of the distribution of this index.

Radar–rain-gauge rainfall estimation for hydrological applications in small catchments

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S. Gabriele

2017-07-01

Full Text Available The accurate evaluation of the precipitation's time–spatial structure is a critical step for rainfall–runoff modelling. Particularly for small catchments, the variability of rainfall can lead to mismatched results. Large errors in flow evaluation may occur during convective storms, responsible for most of the flash floods in small catchments in the Mediterranean area. During such events, we may expect large spatial and temporal variability. Therefore, using rain-gauge measurements only can be insufficient in order to adequately depict extreme rainfall events. In this work, a double-level information approach, based on rain gauges and weather radar measurements, is used to improve areal rainfall estimations for hydrological applications. In order to highlight the effect that precipitation fields with different level of spatial details have on hydrological modelling, two kinds of spatial rainfall fields were computed for precipitation data collected during 2015, considering both rain gauges only and their merging with radar information. The differences produced by these two precipitation fields in the computation of the areal mean rainfall accumulation were evaluated considering 999 basins of the region Calabria, southern Italy. Moreover, both of the two precipitation fields were used to carry out rainfall–runoff simulations at catchment scale for main precipitation events that occurred during 2015 and the differences between the scenarios obtained in the two cases were analysed. A representative case study is presented in detail.

Impact of Satellite Remote Sensing Data on Simulations of ...

Science.gov (United States)

We estimated surface salinity flux and solar penetration from satellite data, and performed model simulations to examine the impact of including the satellite estimates on temperature, salinity, and dissolved oxygen distributions on the Louisiana continental shelf (LCS) near the annual hypoxic zone. Rainfall data from the Tropical Rainfall Measurement Mission (TRMM) were used for the salinity flux, and the diffuse attenuation coefficient (Kd) from Moderate Resolution Imaging Spectroradiometer (MODIS) were used for solar penetration. Improvements in the model results in comparison with in situ observations occurred when the two types of satellite data were included. Without inclusion of the satellite-derived surface salinity flux, realistic monthly variability in the model salinity fields was observed, but important inter-annual variability wasmissed. Without inclusion of the satellite-derived light attenuation, model bottom water temperatures were too high nearshore due to excessive penetration of solar irradiance. In general, these salinity and temperature errors led to model stratification that was too weak, and the model failed to capture observed spatial and temporal variability in water-column vertical stratification. Inclusion of the satellite data improved temperature and salinity predictions and the vertical stratification was strengthened, which improved prediction of bottom-water dissolved oxygen. The model-predicted area of bottom-water hypoxia on the

Re-assessing Rainwater Harvesting Volume by CHIRPS Satellite in Semarang Settlement Area

Science.gov (United States)

Prihanto, Yosef; Koestoer, Raldi H.; Sutjiningsih, Dwita

2017-12-01

Semarang City is one of the most influential coastal cities in Java Island. The city is facing increasingly-high water demand due to its development and water problems due to climate change. The spatial physiography and landscape of Semarang City are also exposed the city to water security problem. Hence, rainwater harvesting treatment is an urgent effort to meet the city’s water needs. However, planning, implementation and management of rainwater harvesting are highly depended on multitemporal rainfall data. It has not yet been fully compiled due to limited rain stations. This study aims to examine the extent to which CHIRPS satellite data can be utilized in estimating volume of rainwater harvesting 16 sub-districts in Semarang and determine the water security status. This study uses descriptive statistical method based on spatial analyses. Such method was developed through spatial modeling for rainfall using isohyetal model. The parameters used are rainfall, residential rooftop area, administrative area, population, physiographic and altitude units. Validation is carried out by using monthly 10 rain stations data. The results show level of validity by utilizing CHIRPS Satellite data and mapping rainfall distribution. This study also produces a potential map of distribution rainfall volume that can be harvested in 16 sub-districts of Semarang.

Erosivity factor in the Universal Soil Loss Equation estimated from Finnish rainfall data

Directory of Open Access Journals (Sweden)

Maximilian Posch

1993-07-01

Full Text Available Continuous rainfall data recorded for many years at 8 stations in Finland were used to estimate rainfall erosivity, a quantity needed for soil loss predictions with the Universal Soil Loss Equation (USLE. The obtained erosivity values were then used to determine the 2 parameters of a power-law function describing the relationship between daily precipitation and erosivity. This function is of importance in erosion modeling at locations where no breakpoint rainfall data are available. The parameters of the power-law were estimated both by linear regression of the log-transformed data and by non-linear least-square fitting of the original data. Results indicate a considerable seasonal (monthly variation of the erosivity, whereas the spatial variation over Finland is rather small.

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.

Engineering of an Extreme Rainfall Detection System using Grid Computing

Directory of Open Access Journals (Sweden)

Olivier Terzo

2012-10-01

Full Text Available This paper describes a new approach for intensive rainfall data analysis. ITHACA's Extreme Rainfall Detection System (ERDS is conceived to provide near real-time alerts related to potential exceptional rainfalls worldwide, which can be used by WFP or other humanitarian assistance organizations to evaluate the event and understand the potentially floodable areas where their assistance is needed. This system is based on precipitation analysis and it uses rainfall data from satellite at worldwide extent. This project uses the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis dataset, a NASA-delivered near real-time product for current rainfall condition monitoring over the world. Considering the great deal of data to process, this paper presents an architectural solution based on Grid Computing techniques. Our focus is on the advantages of using a distributed architecture in terms of performances for this specific purpose.

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

Directory of Open Access Journals (Sweden)

Julien Morel

2014-07-01

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

Areal rainfall estimation using moving cars - computer experiments including hydrological modeling

OpenAIRE

Rabiei, Ehsan; Haberlandt, Uwe; Sester, Monika; Fitzner, Daniel; Wallner, Markus

2016-01-01

The need for high temporal and spatial resolution precipitation data for hydrological analyses has been discussed in several studies. Although rain gauges provide valuable information, a very dense rain gauge network is costly. As a result, several new ideas have been emerged to help estimating areal rainfall with higher temporal and spatial resolution. Rabiei et al. (2013) observed that moving cars, called RainCars (RCs), can potentially be a new source of data for measuring rainfall amounts...

Satellite based hydroclimatic understanding of evolution of Dengue and Zika virus

Science.gov (United States)

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

2017-12-01

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

Geostationary satellite estimation of biomass burning in Amazonia during BASE-A

International Nuclear Information System (INIS)

Menzel, W.P.; Cutrim, E.C.; Prins, E.M.

1991-01-01

This chapter presents the results of using Geostationary Operational Environmental Satellite (GOES) Visible Infrared Spin Scan Radiometer Atmospheric Sounder (VAS) infrared window (3.9 and 11.2 microns) data to monitor biomass burning several times per day in Amazonia. The technique of Matson and Dozier using two window channels was adapted to GOES VAS infrared data to estimate the size and temperature of fires associated with deforestation in the vicinity of Alta Floresta, Brazil, during the Biomass Burning Airborne and Spaceborne Experiment - Amazonia (BASE-A). Although VAS data do not offer the spatial resolution available with AVHRR data 97 km versus 1 km, respectively, this decreased resolution does not seem to hinder the ability of the VAS instrument to detect fires; in some cases it proves to be advantageous in that saturation does not occur as often. VAS visible data are additionally helpful in verifying that the hot spots sensed in the infrared are actually related to fires. Furthermore, the fire plumes can be tracked in time to determine their motion and extent. In this way, the GOES satellite offers a unique ability to monitor diurnal variations in fire activity and transport of related aerosols

An Experimental System for a Global Flood Prediction: From Satellite Precipitation Data to a Flood Inundation Map

Science.gov (United States)

Adler, Robert

2007-01-01

Floods impact more people globally than any other type of natural disaster. It has been established by experience that the most effective means to reduce the property damage and life loss caused by floods is the development of flood early warning systems. However, advances for such a system have been constrained by the difficulty in estimating rainfall continuously over space (catchment-. national-, continental-. or even global-scale areas) and time (hourly to daily). Particularly, insufficient in situ data, long delay in data transmission and absence of real-time data sharing agreements in many trans-boundary basins hamper the development of a real-time system at the regional to global scale. In many countries around the world, particularly in the tropics where rainfall and flooding co-exist in abundance, satellite-based precipitation estimation may be the best source of rainfall data for those data scarce (ungauged) areas and trans-boundary basins. Satellite remote sensing data acquired and processed in real time can now provide the space-time information on rainfall fluxes needed to monitor severe flood events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models, which can be parameterized by a tailored geospatial database. An example that is a key to this progress is NASA's contribution to the Tropical Rainfall Measuring Mission (TRMM), launched in November 1997. Hence, in an effort to evolve toward a more hydrologically-relevant flood alert system, this talk articulates a module-structured framework for quasi-global flood potential naming, that is 'up to date' with the state of the art on satellite rainfall estimation and the improved geospatial datasets. The system is modular in design with the flexibility that permits changes in the model structure and in the choice of components. Four major components included in the system are: 1) multi-satellite precipitation estimation; 2) characterization of

Assessment of the use of remotely sensed rainfall products for runoff ...

African Journals Online (AJOL)

The main objective of this research is to compare the performance of SWAT model using rainfall input data from remotely sensed and ground measured data for Gilgel abbay catchment. Based on the results obtained, it can be said that SWAT model yields good results for the satellite rainfall input data when compared to in ...

SAFARI 2000 FEWS 10-day Rainfall Estimate, 8-Km, 1999-2001

Data.gov (United States)

National Aeronautics and Space Administration — ABSTRACT: The U.S. Agency for International Development (USAID) Famine Early Warning System (FEWS) has been supporting the production of 10-day Rainfall Estimate...

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.

Methane Emissions from Bangladesh: Bridging the Gap Between Ground-based and Space-borne Estimates

Science.gov (United States)

Peters, C.; Bennartz, R.; Hornberger, G. M.

2015-12-01

Gaining an understanding of methane (CH4) emission sources and atmospheric dispersion is an essential part of climate change research. Large-scale and global studies often rely on satellite observations of column CH4 mixing ratio whereas high-spatial resolution estimates rely on ground-based measurements. Extrapolation of ground-based measurements on, for example, rice paddies to broad region scales is highly uncertain because of spatio-temporal variability. We explore the use of ground-based river stage measurements and independent satellite observations of flooded area along with satellite measurements of CH4 mixing ratio to estimate the extent of methane emissions. Bangladesh, which comprises most of the Ganges Brahmaputra Meghna (GBM) delta, is a region of particular interest for studying spatio-temporal variation of methane emissions due to (1) broadscale rice cultivation and (2) seasonal flooding and atmospheric convection during the monsoon. Bangladesh and its deltaic landscape exhibit a broad range of environmental, economic, and social circumstances that are relevant to many nations in South and Southeast Asia. We explore the seasonal enhancement of CH4 in Bangladesh using passive remote sensing spectrometer CH4 products from the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) and the Atmospheric Infrared Sounder (AIRS). The seasonal variation of CH4 is compared to independent estimates of seasonal flooding from water gauge stations and space-based passive microwave water-to-land fractions from the Tropical Rainfall Measuring Mission Microwave Imager (TRMM-TMI). Annual cycles in inundation (natural and anthropogenic) and atmospheric CH4 concentrations show highly correlated seasonal signals. NOAA's HYSPLIT model is used to determine atmospheric residence time of ground CH4 fluxes. Using the satellite observations, we can narrow the large uncertainty in extrapolation of ground-based CH4 emission estimates from rice paddies

Quantification of the spatial variability of rainfall based on a dense network of rain gauges

DEFF Research Database (Denmark)

Pedersen, Lisbeth; Jensen, Niels Einar; Christiansen, Lasse Engbo

2010-01-01

The spatial variability of rainfall within a single Local Area Weather Radar (LAWR) pixel of 500 x 500 m is quantified based on data from two locations. The work was motivated by the need to quantify the variability on this scale in order to provide an estimate of the uncertainty of using a single...... from an earlier campaign in 2003. The fact that the 20072008 dataset was almost four times larger than the original dataset from 2003 motivated this extended study. Two methods were used to describe the variability: the coefficient of variation and the spatial correlation structure of the rainfall......% prediction interval for a given rainfall depth is estimated and can be used to address the uncertainty of using a single rain gauge to represent the rainfall within a 500 x 500 m area. (C) 2009 Elsevier B.V. All rights reserved....

Historical analysis of interannual rainfall variability and trends in southeastern Brazil based on observational and remotely sensed data

Science.gov (United States)

Vásquez P., Isela L.; de Araujo, Lígia Maria Nascimento; Molion, Luiz Carlos Baldicero; de Araujo Abdalad, Mariana; Moreira, Daniel Medeiros; Sanchez, Arturo; Barbosa, Humberto Alves; Rotunno Filho, Otto Corrêa

2018-02-01

The Brazilian Southeast is considered a humid region. It is also prone to landslides and floods, a result of significant increases in rainfall during spring and summer caused by the South Atlantic Convergence Zone (SACZ). Recently, however, the region has faced a striking rainfall shortage, raising serious concerns regarding water availability. The present work endeavored to explain the meteorological drought that has led to hydrological imbalance and water scarcity in the region. Hodrick-Prescott smoothing and wavelet transform techniques were applied to long-term hydrologic and sea surface temperature (SST)—based climate indices monthly time series data in an attempt to detect cycles and trends that could help explain rainfall patterns and define a framework for improving the predictability of extreme events in the region. Historical observational hydrologic datasets available include monthly precipitation amounts gauged since 1888 and 1940 and stream flow measured since the 1930s. The spatial representativeness of rain gauges was tested against gridded rainfall satellite estimates from 2000 to 2015. The analyses revealed variability in four time scale domains—infra-annual, interannual, quasi-decadal and inter-decadal or multi-decadal. The strongest oscillations periods revealed were: for precipitation—8 months, 2, 8 and 32 years; for Pacific SST in the Niño-3.4 region—6 months, 2, 8 and 35.6 years, for North Atlantic SST variability—6 months, 2, 8 and 32 years and for Pacific Decadal Oscillation (PDO) index—6.19 months, 2.04, 8.35 and 27.31 years. Other periodicities less prominent but still statistically significant were also highlighted.

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

Directory of Open Access Journals (Sweden)

Samuel A. Andam‑Akorful

2013-07-01

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

Improved spatial mapping of rainfall events with spaceborne SAR imagery

Science.gov (United States)

Ulaby, F. T.; Brisco, B.; Dobson, C.

1983-01-01

The Seasat satellite acquired the first spaceborne synthetic-aperture radar (SAR) images of the earth's surface, in 1978, at a frequency of 1.275 GHz (L-band) in a like-polarization mode at incidence angles of 23 + or - 3 deg. Although this may not be the optimum system configuration for radar remote sensing of soil moisture, interpretation of two Seasat images of Iowa demonstrates the sensitivity of microwave backscatter to soil moisture content. In both scenes, increased image brightness, which represents more radar backscatter, can be related to previous rainfall activity in the two areas. Comparison of these images with ground-based rainfall observations illustrates the increased spatial coverage of the rainfall event that can be obtained from the satellite SAR data. These data can then be color-enhanced by a digital computer to produce aesthetically pleasing output products for the user community.

Predicting extreme rainfall events over Jeddah, Saudi Arabia: Impact of data assimilation with conventional and satellite observations

KAUST Repository

Viswanadhapalli, Yesubabu

2015-08-20

The impact of variational data assimilation for predicting two heavy rainfall events that caused devastating floods in Jeddah, Saudi Arabia is studied using the Weather Research and Forecasting (WRF) model. On 25 November 2009 and 26 January 2011, the city was deluged with more than double the annual rainfall amount caused by convective storms. We used a high resolution, two-way nested domain WRF model to simulate the two rainfall episodes. Simulations include control runs initialized with National Center for Environmental Prediction (NCEP) Global Forecasting System (GFS) data and 3-Dimensional Variational (3DVAR) data assimilation experiments conducted by assimilating NCEP prepbufr and radiance observations. Observations from Automated Weather Stations (AWS), synoptic charts, radar reflectivity and satellite pictures from the Presidency of Meteorology and Environment (PME), Jeddah, Saudi Arabia are used to assess the forecasting results. To evaluate the impact of the different assimilated observational datasets on the simulation of the major flooding event of 2009, we conducted 3DVAR experiments assimilating individual sources and a combination of all data sets. Results suggest that while the control run had a tendency to predict the storm earlier than observed, the assimilation of profile observations greatly improved the model\\'s thermodynamic structure and lead to better representation of simulated rainfall both in timing and amount. The experiment with assimilation of all available observations compared best with observed rainfall in terms of timing of the storm and rainfall distribution, demonstrating the importance of assimilating different types of observations. Retrospective experiments with and without data assimilation, for three different model lead times (48, 72 and 96-h), were performed to examine the skill of WRF model to predict the heavy rainfall events. Quantitative rainfall analysis of these simulations suggests that 48-h lead time runs with

Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products

International Nuclear Information System (INIS)

Qin, Jun; Chen, Zhuoqi; Yang, Kun; Liang, Shunlin; Tang, Wenjun

2011-01-01

Global solar radiation (GSR) is required in a large number of fields. Many parameterization schemes are developed to estimate it using routinely measured meteorological variables, since GSR is directly measured at a limited number of stations. Even so, meteorological stations are sparse, especially, in remote areas. Satellite signals (radiance at the top of atmosphere in most cases) can be used to estimate continuous GSR in space. However, many existing remote sensing products have a relatively coarse spatial resolution and these inversion algorithms are too complicated to be mastered by experts in other research fields. In this study, the artificial neural network (ANN) is utilized to build the mathematical relationship between measured monthly-mean daily GSR and several high-level remote sensing products available for the public, including Moderate Resolution Imaging Spectroradiometer (MODIS) monthly averaged land surface temperature (LST), the number of days in which the LST retrieval is performed in 1 month, MODIS enhanced vegetation index, Tropical Rainfall Measuring Mission satellite (TRMM) monthly precipitation. After training, GSR estimates from this ANN are verified against ground measurements at 12 radiation stations. Then, comparisons are performed among three GSR estimates, including the one presented in this study, a surface data-based estimate, and a remote sensing product by Japan Aerospace Exploration Agency (JAXA). Validation results indicate that the ANN-based method presented in this study can estimate monthly-mean daily GSR at a spatial resolution of about 5 km with high accuracy.

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.

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

Science.gov (United States)

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

2015-04-01

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

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

Science.gov (United States)

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

2017-12-01

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

Developments in radar and remote-sensing methods for measuring and forecasting rainfall.

Science.gov (United States)

Collier, C G

2002-07-15

Over the last 25 years or so, weather-radar networks have become an integral part of operational meteorological observing systems. While measurements of rainfall made using radar systems have been used qualitatively by weather forecasters, and by some operational hydrologists, acceptance has been limited as a consequence of uncertainties in the quality of the data. Nevertheless, new algorithms for improving the accuracy of radar measurements of rainfall have been developed, including the potential to calibrate radars using the measurements of attenuation on microwave telecommunications links. Likewise, ways of assimilating these data into both meteorological and hydrological models are being developed. In this paper we review the current accuracy of radar estimates of rainfall, pointing out those approaches to the improvement of accuracy which are likely to be most successful operationally. Comment is made on the usefulness of satellite data for estimating rainfall in a flood-forecasting context. Finally, problems in coping with the error characteristics of all these data using both simple schemes and more complex four-dimensional variational analysis are being addressed, and are discussed briefly in this paper.

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.

Effects of sample size on estimation of rainfall extremes at high temperatures

Science.gov (United States)

Boessenkool, Berry; Bürger, Gerd; Heistermann, Maik

2017-09-01

High precipitation quantiles tend to rise with temperature, following the so-called Clausius-Clapeyron (CC) scaling. It is often reported that the CC-scaling relation breaks down and even reverts for very high temperatures. In our study, we investigate this reversal using observational climate data from 142 stations across Germany. One of the suggested meteorological explanations for the breakdown is limited moisture supply. Here we argue that, instead, it could simply originate from undersampling. As rainfall frequency generally decreases with higher temperatures, rainfall intensities as dictated by CC scaling are less likely to be recorded than for moderate temperatures. Empirical quantiles are conventionally estimated from order statistics via various forms of plotting position formulas. They have in common that their largest representable return period is given by the sample size. In small samples, high quantiles are underestimated accordingly. The small-sample effect is weaker, or disappears completely, when using parametric quantile estimates from a generalized Pareto distribution (GPD) fitted with L moments. For those, we obtain quantiles of rainfall intensities that continue to rise with temperature.

Effects of sample size on estimation of rainfall extremes at high temperatures

Directory of Open Access Journals (Sweden)

B. Boessenkool

2017-09-01

Full Text Available High precipitation quantiles tend to rise with temperature, following the so-called Clausius–Clapeyron (CC scaling. It is often reported that the CC-scaling relation breaks down and even reverts for very high temperatures. In our study, we investigate this reversal using observational climate data from 142 stations across Germany. One of the suggested meteorological explanations for the breakdown is limited moisture supply. Here we argue that, instead, it could simply originate from undersampling. As rainfall frequency generally decreases with higher temperatures, rainfall intensities as dictated by CC scaling are less likely to be recorded than for moderate temperatures. Empirical quantiles are conventionally estimated from order statistics via various forms of plotting position formulas. They have in common that their largest representable return period is given by the sample size. In small samples, high quantiles are underestimated accordingly. The small-sample effect is weaker, or disappears completely, when using parametric quantile estimates from a generalized Pareto distribution (GPD fitted with L moments. For those, we obtain quantiles of rainfall intensities that continue to rise with temperature.

Estimating Global Impervious Surface based on Social-economic Data and Satellite Observations

Science.gov (United States)

Zeng, Z.; Zhang, K.; Xue, X.; Hong, Y.

2016-12-01

Impervious surface areas around the globe are expanding and significantly altering the surface energy balance, hydrology cycle and ecosystem services. Many studies have underlined the importance of impervious surface, r from hydrological modeling to contaminant transport monitoring and urban development estimation. Therefore accurate estimation of the global impervious surface is important for both physical and social sciences. Given the limited coverage of high spatial resolution imagery and ground survey, using satellite remote sensing and geospatial data to estimate global impervious areas is a practical approach. Based on the previous work of area-weighted imperviousness for north branch of the Chicago River provided by HDR, this study developed a method to determine the percentage of impervious surface using latest global land cover categories from multi-source satellite observations, population density and gross domestic product (GDP) data. Percent impervious surface at 30-meter resolution were mapped. We found that 1.33% of the CONUS (105,814 km2) and 0.475% of the land surface (640,370km2) are impervious surfaces. To test the utility and practicality of the proposed method, National Land Cover Database (NLCD) 2011 percent developed imperviousness for the conterminous United States was used to evaluate our results. The average difference between the derived imperviousness from our method and the NLCD data across CONUS is 1.14%, while difference between our results and the NLCD data are within ±1% over 81.63% of the CONUS. The distribution of global impervious surface map indicates that impervious surfaces are primarily concentrated in China, India, Japan, USA and Europe where are highly populated and/or developed. This study proposes a straightforward way of mapping global imperviousness, which can provide useful information for hydrologic modeling and other applications.

Estimation of storm runoff loads based on rainfall-related variables ...

African Journals Online (AJOL)

2004-11-19

Nov 19, 2004 ... ... rainfall-related variables and power law models – Case study in Alexandra ... and appropriate technology for treating runoff and grey-water. To achieve this ... schools, and other open spaces take up 20% of the area. If the.

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

Science.gov (United States)

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

2014-01-01

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

A multi-source satellite data approach for modelling Lake Turkana water level: calibration and validation using satellite altimetry data

Directory of Open Access Journals (Sweden)

N. M. Velpuri

2012-01-01

Full Text Available Lake Turkana is one of the largest desert lakes in the world and is characterized by high degrees of inter- and intra-annual fluctuations. The hydrology and water balance of this lake have not been well understood due to its remote location and unavailability of reliable ground truth datasets. Managing surface water resources is a great challenge in areas where in-situ data are either limited or unavailable. In this study, multi-source satellite-driven data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, and a digital elevation dataset were used to model Lake Turkana water levels from 1998 to 2009. Due to the unavailability of reliable lake level data, an approach is presented to calibrate and validate the water balance model of Lake Turkana using a composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data. Model validation results showed that the satellite-driven water balance model can satisfactorily capture the patterns and seasonal variations of the Lake Turkana water level fluctuations with a Pearson's correlation coefficient of 0.90 and a Nash-Sutcliffe Coefficient of Efficiency (NSCE of 0.80 during the validation period (2004–2009. Model error estimates were within 10% of the natural variability of the lake. Our analysis indicated that fluctuations in Lake Turkana water levels are mainly driven by lake inflows and over-the-lake evaporation. Over-the-lake rainfall contributes only up to 30% of lake evaporative demand. During the modelling time period, Lake Turkana showed seasonal variations of 1–2 m. The lake level fluctuated in the range up to 4 m between the years 1998–2009. This study demonstrated the usefulness of satellite altimetry data to calibrate and validate the satellite-driven hydrological model for Lake Turkana without using any in-situ data. Furthermore, for Lake Turkana, we identified and outlined opportunities and challenges of using a calibrated

Evaluation of X-band polarimetric radar estimation of rainfall and rain drop size distribution parameters in West Africa

Science.gov (United States)

Koffi, A. K.; Gosset, M.; Zahiri, E.-P.; Ochou, A. D.; Kacou, M.; Cazenave, F.; Assamoi, P.

2014-06-01

As part of the African Monsoon Multidisciplinary Analysis (AMMA) field campaign an X-band dual-polarization Doppler radar was deployed in Benin, West-Africa, in 2006 and 2007, together with a reinforced rain gauge network and several optical disdrometers. Based on this data set, a comparative study of several rainfall estimators that use X-band polarimetric radar data is presented. In tropical convective systems as encountered in Benin, microwave attenuation by rain is significant and quantitative precipitation estimation (QPE) at X-band is a challenge. Here, several algorithms based on the combined use of reflectivity, differential reflectivity and differential phase shift are evaluated against rain gauges and disdrometers. Four rainfall estimators were tested on twelve rainy events: the use of attenuation corrected reflectivity only (estimator R(ZH)), the use of the specific phase shift only R(KDP), the combination of specific phase shift and differential reflectivity R(KDP,ZDR) and an estimator that uses three radar parameters R(ZH,ZDR,KDP). The coefficients of the power law relationships between rain rate and radar variables were adjusted either based on disdrometer data and simulation, or on radar-gauges observations. The three polarimetric based algorithms with coefficients predetermined on observations outperform the R(ZH) estimator for rain rates above 10 mm/h which explain most of the rainfall in the studied region. For the highest rain rates (above 30 mm/h) R(KDP) shows even better scores, and given its performances and its simplicity of implementation, is recommended. The radar based retrieval of two parameters of the rain drop size distribution, the normalized intercept parameter NW and the volumetric median diameter Dm was evaluated on four rainy days thanks to disdrometers. The frequency distributions of the two parameters retrieved by the radar are very close to those observed with the disdrometer. NW retrieval based on a combination of ZH

Modelling of extreme rainfall events in Peninsular Malaysia based on annual maximum and partial duration series

Science.gov (United States)

Zin, Wan Zawiah Wan; Shinyie, Wendy Ling; Jemain, Abdul Aziz

2015-02-01

In this study, two series of data for extreme rainfall events are generated based on Annual Maximum and Partial Duration Methods, derived from 102 rain-gauge stations in Peninsular from 1982-2012. To determine the optimal threshold for each station, several requirements must be satisfied and Adapted Hill estimator is employed for this purpose. A semi-parametric bootstrap is then used to estimate the mean square error (MSE) of the estimator at each threshold and the optimal threshold is selected based on the smallest MSE. The mean annual frequency is also checked to ensure that it lies in the range of one to five and the resulting data is also de-clustered to ensure independence. The two data series are then fitted to Generalized Extreme Value and Generalized Pareto distributions for annual maximum and partial duration series, respectively. The parameter estimation methods used are the Maximum Likelihood and the L-moment methods. Two goodness of fit tests are then used to evaluate the best-fitted distribution. The results showed that the Partial Duration series with Generalized Pareto distribution and Maximum Likelihood parameter estimation provides the best representation for extreme rainfall events in Peninsular Malaysia for majority of the stations studied. Based on these findings, several return values are also derived and spatial mapping are constructed to identify the distribution characteristic of extreme rainfall in Peninsular Malaysia.

Estimation and prediction of maximum daily rainfall at Sagar Island using best fit probability models

Science.gov (United States)

Mandal, S.; Choudhury, B. U.

2015-07-01

Sagar Island, setting on the continental shelf of Bay of Bengal, is one of the most vulnerable deltas to the occurrence of extreme rainfall-driven climatic hazards. Information on probability of occurrence of maximum daily rainfall will be useful in devising risk management for sustaining rainfed agrarian economy vis-a-vis food and livelihood security. Using six probability distribution models and long-term (1982-2010) daily rainfall data, we studied the probability of occurrence of annual, seasonal and monthly maximum daily rainfall (MDR) in the island. To select the best fit distribution models for annual, seasonal and monthly time series based on maximum rank with minimum value of test statistics, three statistical goodness of fit tests, viz. Kolmogorove-Smirnov test (K-S), Anderson Darling test ( A 2 ) and Chi-Square test ( X 2) were employed. The fourth probability distribution was identified from the highest overall score obtained from the three goodness of fit tests. Results revealed that normal probability distribution was best fitted for annual, post-monsoon and summer seasons MDR, while Lognormal, Weibull and Pearson 5 were best fitted for pre-monsoon, monsoon and winter seasons, respectively. The estimated annual MDR were 50, 69, 86, 106 and 114 mm for return periods of 2, 5, 10, 20 and 25 years, respectively. The probability of getting an annual MDR of >50, >100, >150, >200 and >250 mm were estimated as 99, 85, 40, 12 and 03 % level of exceedance, respectively. The monsoon, summer and winter seasons exhibited comparatively higher probabilities (78 to 85 %) for MDR of >100 mm and moderate probabilities (37 to 46 %) for >150 mm. For different recurrence intervals, the percent probability of MDR varied widely across intra- and inter-annual periods. In the island, rainfall anomaly can pose a climatic threat to the sustainability of agricultural production and thus needs adequate adaptation and mitigation measures.

Evaluation of critical storm duration rainfall estimates used in flood ...

African Journals Online (AJOL)

The results showed that the RMLA&SI approach can be considered as the preferred DDF relationship in future design flood estimations. The results also showed that a direct relationship exists between the catchment area and TC, thus ARFs can be explicitly expressed in terms of only the catchment area. Keywords: Rainfall ...

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.

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.

Experiences of citizen-based reporting of rainfall events using lab-generated videos

Science.gov (United States)

Alfonso, Leonardo; Chacon, Juan

2016-04-01

Hydrologic studies rely on the availability of good-quality precipitation estimates. However, in remote areas of the world and particularly in developing countries, ground-based measurement networks are either sparse or nonexistent. This creates difficulties in the estimation of precipitation, which limits the development of hydrologic forecasting and early warning systems for these regions. The EC-FP7 WeSenseIt project aims at exploring the involvement of citizens in the observation of the water cycle with innovative sensor technologies, including mobile telephony. In particular, the project explores the use of a smartphone applications to facilitate the reporting water-related situations. Apart from the challenge of using such information for scientific purposes, the citizen engagement is one of the most important issues to address. To this end effortless methods for reporting need to be developed in order to involve as many people as possible in these experiments. A potential solution to overcome these drawbacks, consisting on lab-controlled rainfall videos have been produced to help mapping the extent and distribution of rainfall fields with minimum effort [1]. In addition, the quality of the collected rainfall information has also been studied [2] by means of different experiments with students. The present research shows the latest results of the application of this method and evaluates the experiences in some cases. [1] Alfonso, L., J. Chacón, and G. Peña-Castellanos (2015), Allowing Citizens to Effortlessly Become Rainfall Sensors, in 36th IAHR World Congress edited, The Hague, the Netherlands [2] Cortes-Arevalo, J., J. Chacón, L. Alfonso, and T. Bogaard (2015), Evaluating data quality collected by using a video rating scale to estimate and report rainfall intensity, in 36th IAHR World Congress edited, The Hague, the Netherlands

Evaluation of the Performance of Three Satellite Precipitation Products over Africa

Directory of Open Access Journals (Sweden)

Aleix Serrat-Capdevila

2016-10-01

Full Text Available We present an evaluation of daily estimates from three near real-time quasi-global Satellite Precipitation Products—Tropical Rainfall Measuring Mission (TRMM Multi-satellite Precipitation Analysis (TMPA, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN, and Climate Prediction Center (CPC Morphing Technique (CMORPH—over the African continent, using the Global Precipitation Climatology Project one Degree Day (GPCP-1dd as a reference dataset for years 2001 to 2013. Different types of errors are characterized for each season as a function of spatial classifications (latitudinal bands, climatic zones and topography and in relationship with the main rain-producing mechanisms in the continent: the Intertropical Convergence Zone (ITCZ and the East African Monsoon. A bias correction of the satellite estimates is applied using a probability density function (pdf matching approach, with a bias analysis as a function of rain intensity, season and latitude. The effects of bias correction on different error terms are analyzed, showing an almost elimination of the mean and variance terms in most of the cases. While raw estimates of TMPA show higher efficiency, all products have similar efficiencies after bias correction. PERSIANN consistently shows the smallest median errors when it correctly detects precipitation events. The areas with smallest relative errors and other performance measures follow the position of the ITCZ oscillating seasonally over the equator, illustrating the close relationship between satellite estimates and rainfall regime.

Radar rainfall estimation of stratiform winter precipitation in the Belgian Ardennes

NARCIS (Netherlands)

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

2011-01-01

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

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

Science.gov (United States)

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

Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel

Science.gov (United States)

Zhang, Wenmin; Brandt, Martin; Tong, Xiaoye; Tian, Qingjiu; Fensholt, Rasmus

2018-01-01

Climate change in drylands has caused alterations in the seasonal distribution of rainfall including increased heavy-rainfall events, longer dry spells, and a shifted timing of the wet season. Yet the aboveground net primary productivity (ANPP) in drylands is usually explained by annual-rainfall sums, disregarding the influence of the seasonal distribution of rainfall. This study tested the importance of rainfall metrics in the wet season (onset and cessation of the wet season, number of rainy days, rainfall intensity, number of consecutive dry days, and heavy-rainfall events) for growing season ANPP. We focused on the Sahel and northern Sudanian region (100-800 mm yr-1) and applied daily satellite-based rainfall estimates (CHIRPS v2.0) and growing-season-integrated normalized difference vegetation index (NDVI; MODIS) as a proxy for ANPP over the study period: 2001-2015. Growing season ANPP in the arid zone (100-300 mm yr-1) was found to be rather insensitive to variations in the seasonal-rainfall metrics, whereas vegetation in the semi-arid zone (300-700 mm yr-1) was significantly impacted by most metrics, especially by the number of rainy days and timing (onset and cessation) of the wet season. We analysed critical breakpoints for all metrics to test if vegetation response to changes in a given rainfall metric surpasses a threshold beyond which vegetation functioning is significantly altered. It was shown that growing season ANPP was particularly negatively impacted after > 14 consecutive dry days and that a rainfall intensity of ˜ 13 mm day-1 was detected for optimum growing season ANPP. We conclude that the number of rainy days and the timing of the wet season are seasonal-rainfall metrics that are decisive for favourable vegetation growth in the semi-arid Sahel and need to be considered when modelling primary productivity from rainfall in the drylands of the Sahel and elsewhere.

A MEMS-based Adaptive AHRS for Marine Satellite Tracking Antenna

DEFF Research Database (Denmark)

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

2015-01-01

Satellite tracking is a challenging task for marine applications. An attitude determination system should estimate the wave disturbances on the ship body accurately. To achieve this, an Attitude Heading Reference System (AHRS) based on Micro-Electro-Mechanical Systems (MEMS) sensors, composed...... of three-axis gyroscope, accelerometer and magnetometer, is developed for Marine Satellite Tracking Antenna (MSTA). In this paper, the attitude determination algorithm is improved using an adaptive mechanism that tunes the attitude estimator parameters based on an estimation of ship motion frequency...

Evaluation of short-period rainfall estimates from Kalpana-1 satellite ...

Indian Academy of Sciences (India)

This study evaluates the 3-hourly precipitation estimates by this technique as well ... product) and surface raingauge data for stations over the Indian region for ... in case of IMSRA estimates, is also validated independently with respect to ... consistent with the nature and development of pre- ... Raingauge networks, though.

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.

Groundwater Estimation Using Remote Sensing Data on a Catchment Scale in New Zealand

Science.gov (United States)

Westerhoff, R.; Mu, Q.

2014-12-01

Long-term time series of satellite evapotranspiration (ET) were trialled for their additional value in aquifer characterisation on the catchment scale in New Zealand. In a simple chain-of-events approach yearly natural groundwater recharge was calculated with a 1x1km resolution. The chain consisted of (1) rainfall; (2) runoff due to slope; (3) actual ET; (4) soil permeability and water holding capacity; and (5) hydraulic conductivity of the deeper geology. As ET is a large part of the water balance (in New Zealand on average appr. 50% of rainfall), high resolution and high quality ET data is important for estimating groundwater recharge. Most global satellite data already embed a pseudo-model with coarse, global, input data. An example is ET data from the MODIS MOD16 product: although the spatial footprint of the satellite data is 1x1 km, input data to calculate ET contains global meteorology data. These data do not capture the extreme diversity in the New Zealand climate, where yearly rainfall and ET can change considerably over small distances. However, enough national ground-observed data are available to improve the MOD16 data. We improved monthly MOD16 ET by using the satellite data pattern as an interpolator between approximately 80 ground stations. Simple least squares fitting gave the best result. The added value of satellite data is obvious: the corrected MOD16 ET data have much higher spatial resolution and vegetation cover and growth is taken into account better.We then used national data to estimate 1x1km natural groundwater recharge: the corrected MOD16 PET and AET, in-situ based precipitation models; soil maps; geology maps; and (satellite-based) elevation. Validation with lysimeters and existing sub-catchment model output data looks promising, and further improvement with satellite soil moisture to estimate monthly recharge is underway. This work was done in the SMART Aquifer Characterisation (SAC) programme, a six-year research project funded by the

A multi-source satellite data approach for modelling Lake Turkana water level: Calibration and validation using satellite altimetry data

Science.gov (United States)

Velpuri, N.M.; Senay, G.B.; Asante, K.O.

2012-01-01

Lake Turkana is one of the largest desert lakes in the world and is characterized by high degrees of interand intra-annual fluctuations. The hydrology and water balance of this lake have not been well understood due to its remote location and unavailability of reliable ground truth datasets. Managing surface water resources is a great challenge in areas where in-situ data are either limited or unavailable. In this study, multi-source satellite-driven data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, and a digital elevation dataset were used to model Lake Turkana water levels from 1998 to 2009. Due to the unavailability of reliable lake level data, an approach is presented to calibrate and validate the water balance model of Lake Turkana using a composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data. Model validation results showed that the satellitedriven water balance model can satisfactorily capture the patterns and seasonal variations of the Lake Turkana water level fluctuations with a Pearson's correlation coefficient of 0.90 and a Nash-Sutcliffe Coefficient of Efficiency (NSCE) of 0.80 during the validation period (2004-2009). Model error estimates were within 10% of the natural variability of the lake. Our analysis indicated that fluctuations in Lake Turkana water levels are mainly driven by lake inflows and over-the-lake evaporation. Over-the-lake rainfall contributes only up to 30% of lake evaporative demand. During the modelling time period, Lake Turkana showed seasonal variations of 1-2m. The lake level fluctuated in the range up to 4m between the years 1998-2009. This study demonstrated the usefulness of satellite altimetry data to calibrate and validate the satellite-driven hydrological model for Lake Turkana without using any in-situ data. Furthermore, for Lake Turkana, we identified and outlined opportunities and challenges of using a calibrated satellite-driven water balance

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

Directory of Open Access Journals (Sweden)

Hua Zhang

2016-09-01

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

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

Science.gov (United States)

Xie, Pingping; Joyce, Robert; Wu, Shaorong

2015-04-01

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

Radar rainfall estimation of stratiform winter precipitation in the Belgian Ardennes

Science.gov (United States)

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

2011-02-01

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

Satellite altimetry based rating curves throughout the entire Amazon basin

Science.gov (United States)

Paris, A.; Calmant, S.; Paiva, R. C.; Collischonn, W.; Silva, J. S.; Bonnet, M.; Seyler, F.

2013-05-01

The Amazonian basin is the largest hydrological basin all over the world. In the recent past years, the basin has experienced an unusual succession of extreme draughts and floods, which origin is still a matter of debate. Yet, the amount of data available is poor, both over time and space scales, due to factor like basin's size, access difficulty and so on. One of the major locks is to get discharge series distributed over the entire basin. Satellite altimetry can be used to improve our knowledge of the hydrological stream flow conditions in the basin, through rating curves. Rating curves are mathematical relationships between stage and discharge at a given place. The common way to determine the parameters of the relationship is to compute the non-linear regression between the discharge and stage series. In this study, the discharge data was obtained by simulation through the entire basin using the MGB-IPH model with TRMM Merge input rainfall data and assimilation of gage data, run from 1998 to 2010. The stage dataset is made of ~800 altimetry series at ENVISAT and JASON-2 virtual stations. Altimetry series span between 2002 and 2010. In the present work we present the benefits of using stochastic methods instead of probabilistic ones to determine a dataset of rating curve parameters which are consistent throughout the entire Amazon basin. The rating curve parameters have been computed using a parameter optimization technique based on Markov Chain Monte Carlo sampler and Bayesian inference scheme. This technique provides an estimate of the best parameters for the rating curve, but also their posterior probability distribution, allowing the determination of a credibility interval for the rating curve. Also is included in the rating curve determination the error over discharges estimates from the MGB-IPH model. These MGB-IPH errors come from either errors in the discharge derived from the gage readings or errors in the satellite rainfall estimates. The present

Estimating the Mass of the Milky Way Using the Ensemble of Classical Satellite Galaxies

Science.gov (United States)

Patel, Ekta; Besla, Gurtina; Sohn, Sangmo Tony; Mandel, Kaisey

2018-06-01

High precision proper motions are currently available for approximately 20% of the Milky Way's known satellite galaxies. Often, the 6D phase space information of each satellite is used separately to constrain the mass of the MW. In this talk, I will discuss the Bayesian framework outlined in Patel et al. 2017b to make inferences of the MW's mass using satellite properties such as specific orbital angular momentum, rather than just position and velocity. By extending this framework from one satellite to a population of satellites, we can now form simultaneous MW mass estimates using the Illustris-Dark cosmological simulation that are unbiased by high speed satellites such as Leo I (Patel et al., submitted). Our resulting MW mass estimates reduce the current factor of two uncertainty in the mass range of the MW and show promising signs for improvement as upcoming ground- and space-based observatories obtain proper motions for additional MW satellite galaxies.

A technique to obtain a multiparameter radar rainfall algorithm using the probability matching procedure

International Nuclear Information System (INIS)

Gorgucci, E.; Scarchilli, G.

1997-01-01

The natural cumulative distributions of rainfall observed by a network of rain gauges and a multiparameter radar are matched to derive multiparameter radar algorithms for rainfall estimation. The use of multiparameter radar measurements in a statistical framework to estimate rainfall is resented in this paper, The techniques developed in this paper are applied to the radar and rain gauge measurement of rainfall observed in central Florida and central Italy. Conventional pointwise estimates of rainfall are also compared. The probability matching procedure, when applied to the radar and surface measurements, shows that multiparameter radar algorithms can match the probability distribution function better than the reflectivity-based algorithms. It is also shown that the multiparameter radar algorithm derived matching the cumulative distribution function of rainfall provides more accurate estimates of rainfall on the ground in comparison to any conventional reflectivity-based algorithm

Estimation of absorbed photosynthetically active radiation and vegetation net production efficiency using satellite data

International Nuclear Information System (INIS)

Hanan, N.P.; Prince, S.D.; Begue, A.

1995-01-01

The amount of photosynthetically active radiation (PAR) absorbed by green vegetation is an important determinant of photosynthesis and growth. Methods for the estimation of fractional absorption of PAR (iff PAR ) for areas greater than 1 km 2 using satellite data are discussed, and are applied to sites in the Sahel that have a sparse herb layer and tree cover of less than 5%. Using harvest measurements of seasonal net production, net production efficiencies are calculated. Variation in estimates of seasonal PAR absorption (APAR) caused by the atmospheric correction method and relationship between surface reflectances and iff PAR is considered. The use of maximum value composites of satellite NDVI to reduce the effect of the atmosphere is shown to produce inaccurate APAR estimates. In this data set, however, atmospheric correction using average optical depths was found to give good approximations of the fully corrected data. A simulation of canopy radiative transfer using the SAIL model was used to derive a relationship between canopy NDVI and iff PAR . Seasonal APAR estimates assuming a 1:1 relationship between iff PAR and NDVI overestimated the SAIL modeled results by up to 260%. The use of a modified 1:1 relationship, where iff PAR was assumed to be linearly related to NDVI scaled between minimum (soil) and maximum (infinite canopy) values, underestimated the SAIL modeled results by up to 35%. Estimated net production efficiencies (ϵ n , dry matter per unit APAR) fell in the range 0.12–1.61 g MJ −1 for above ground production, and in the range 0.16–1.88 g MJ −1 for total production. Sites with lower rainfall had reduced efficiencies, probably caused by physiological constraints on photosynthesis during dry conditions. (author)

Uncertainty of Flood Forecasting Based on Radar Rainfall Data Assimilation

Directory of Open Access Journals (Sweden)

Xinchi Chen

2016-01-01

Full Text Available Precipitation is the core data input to hydrological forecasting. The uncertainty in precipitation forecast data can lead to poor performance of predictive hydrological models. Radar-based precipitation measurement offers advantages over ground-based measurement in the quantitative estimation of temporal and spatial aspects of precipitation, but errors inherent in this method will still act to reduce the performance. Using data from White Lotus River of Hubei Province, China, five methods were used to assimilate radar rainfall data transformed from the classified Z-R relationship, and the postassimilation data were compared with precipitation measured by rain gauges. The five sets of assimilated rainfall data were then used as input to the Xinanjiang model. The effect of precipitation data input error on runoff simulation was analyzed quantitatively by disturbing the input data using the Breeding of Growing Modes method. The results of practical application demonstrated that the statistical weight integration and variational assimilation methods were superior. The corresponding performance in flood hydrograph prediction was also better using the statistical weight integration and variational methods compared to the others. It was found that the errors of radar rainfall data disturbed by the Breeding of Growing Modes had a tendency to accumulate through the hydrological model.

Critical Phenomena of Rainfall in Ecuador

Science.gov (United States)

Serrano, Sh.; Vasquez, N.; Jacome, P.; Basile, L.

2014-02-01

Self-organized criticality (SOC) is characterized by a power law behavior over complex systems like earthquakes and avalanches. We study rainfall using data of one day, 3 hours and 10 min temporal resolution from INAMHI (Instituto Nacional de Meteorologia e Hidrologia) station at Izobamba, DMQ (Metropolitan District of Quito), satellite data over Ecuador from Tropical Rainfall Measure Mission (TRMM,) and REMMAQ (Red Metropolitana de Monitoreo Atmosferico de Quito) meteorological stations over, respectively. Our results show a power law behavior of the number of rain events versus mm of rainfall measured for the high resolution case (10 min), and as the resolution decreases this behavior gets lost. This statistical property is the fingerprint of a self-organized critical process (Peter and Christensen, 2002) and may serve as a benchmark for models of precipitation based in phase transitions between water vapor and precipitation (Peter and Neeling, 2006).

Estimation of PV energy production based on satellite data

Science.gov (United States)

Mazurek, G.

2015-09-01

Photovoltaic (PV) technology is an attractive source of power for systems without connection to power grid. Because of seasonal variations of solar radiation, design of such a power system requires careful analysis in order to provide required reliability. In this paper we present results of three-year measurements of experimental PV system located in Poland and based on polycrystalline silicon module. Irradiation values calculated from results of ground measurements have been compared with data from solar radiation databases employ calculations from of satellite observations. Good convergence level of both data sources has been shown, especially during summer. When satellite data from the same time period is available, yearly and monthly production of PV energy can be calculated with 2% and 5% accuracy, respectively. However, monthly production during winter seems to be overestimated, especially in January. Results of this work may be helpful in forecasting performance of similar PV systems in Central Europe and allow to make more precise forecasts of PV system performance than based only on tables with long time averaged values.

Estimating Global Ecosystem Isohydry/Anisohydry Using Active and Passive Microwave Satellite Data

Science.gov (United States)

Li, Yan; Guan, Kaiyu; Gentine, Pierre; Konings, Alexandra G.; Meinzer, Frederick C.; Kimball, John S.; Xu, Xiangtao; Anderegg, William R. L.; McDowell, Nate G.; Martinez-Vilalta, Jordi; Long, David G.; Good, Stephen P.

2017-12-01

The concept of isohydry/anisohydry describes the degree to which plants regulate their water status, operating from isohydric with strict regulation to anisohydric with less regulation. Though some species level measures of isohydry/anisohydry exist at a few locations, ecosystem-scale information is still largely unavailable. In this study, we use diurnal observations from active (Ku-Band backscatter from QuikSCAT) and passive (X-band vegetation optical depth (VOD) from Advanced Microwave Scanning Radiometer on EOS Aqua) microwave satellite data to estimate global ecosystem isohydry/anisohydry. Here diurnal observations from both satellites approximate predawn and midday plant canopy water contents, which are used to estimate isohydry/anisohydry. The two independent estimates from radar backscatter and VOD show reasonable agreement at low and middle latitudes but diverge at high latitudes. Grasslands, croplands, wetlands, and open shrublands are more anisohydric, whereas evergreen broadleaf and deciduous broadleaf forests are more isohydric. The direct validation with upscaled in situ species isohydry/anisohydry estimates indicates that the VOD-based estimates have much better agreement than the backscatter-based estimates. The indirect validation with prior knowledge suggests that both estimates are generally consistent in that vegetation water status of anisohydric ecosystems more closely tracks environmental fluctuations of water availability and demand than their isohydric counterparts. However, uncertainties still exist in the isohydry/anisohydry estimate, primarily arising from the remote sensing data and, to a lesser extent, from the methodology. The comprehensive assessment in this study can help us better understand the robustness, limitation, and uncertainties of the satellite-derived isohydry/anisohydry estimates. The ecosystem isohydry/anisohydry has the potential to reveal new insights into spatiotemporal ecosystem response to droughts.

Estimation of solar radiation over Cambodia from long-term satellite data

Energy Technology Data Exchange (ETDEWEB)

Janjai, S.; Pankaew, P.; Laksanaboonsong, J. [Solar Energy Research Laboratory, Department of Physics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000 (Thailand); Kitichantaropas, P. [Department of Alternative Energy Development and Efficiency, Ministry of Energy, 17 Rama 1 Road, Patumwan, Bangkok 10330 (Thailand)

2011-04-15

In this work, monthly average daily global solar irradiation over Cambodia was estimated from a long-term satellite data. A 14-year period (1995-2008) of visible channel data from GMS5, GOES9 and MTSAT-1R satellites were used to provide earth-atmospheric reflectivity. A satellite-based solar radiation model developed for a tropical environment was used to estimate surface solar radiation. The model relates the satellite-derived earth-atmospheric reflectivity to absorption and scattering coefficients of various atmospheric constituents. The absorption of solar radiation due to water vapour was calculated from precipitable water derived from ambient relative humidity and temperature. Ozone data from the TOMS and OMI satellite data were employed to compute the solar radiation absorption by ozone. The depletion of radiation due to aerosols was estimated from the visibility data. Five new solar radiation measuring stations were established at Cambodian cities, namely Siem Reap (13.87 N, 103.85 E), Kompong Thom (12.68 N, 104.88 E), Phnom Penh (11.55 N, 104.83 E), Sihanouke Ville (10.67 N, 103.63 E) and Kampot (10.70 N, 104.28 E). Global solar radiation measured at these stations was used to validate the model. The validation was also carried out by using solar radiation measured at four Thai meteorological stations. These stations are situated near the Cambodian border. Monthly average daily global irradiation from these stations was compared with that calculated from the model. The measured and calculated irradiation is in good agreement, with the root mean square difference of 6.3%, with respect to the mean values. After the validation, the model was used to calculate monthly average daily global solar irradiation over Cambodia. Based on this satellite-derived irradiation, solar radiation maps for Cambodia were generated. These maps show that solar radiation climate of this country is strongly influenced by the monsoons. A solar radiation database was also generated

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

Directory of Open Access Journals (Sweden)

K. Aalstad

2018-01-01

Full Text Available With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD at the 1 km scale by assimilating fractional snow-covered area (fSCA satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation ensemble smoother with multiple data assimilation (ES-MDA scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e. and 0.13, respectively. The ES-MDA either

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

Science.gov (United States)

Aalstad, Kristoffer; Westermann, Sebastian; Vikhamar Schuler, Thomas; Boike, Julia; Bertino, Laurent

2018-01-01

With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1 km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least

Integrating Global Satellite-Derived Data Products as a Pre-Analysis for Hydrological Modelling Studies: A Case Study for the Red River Basin

Directory of Open Access Journals (Sweden)

Gijs Simons

2016-03-01

Full Text Available With changes in weather patterns and intensifying anthropogenic water use, there is an increasing need for spatio-temporal information on water fluxes and stocks in river basins. The assortment of satellite-derived open-access information sources on rainfall (P and land use/land cover (LULC is currently being expanded with the application of actual evapotranspiration (ETact algorithms on the global scale. We demonstrate how global remotely sensed P and ETact datasets can be merged to examine hydrological processes such as storage changes and streamflow prior to applying a numerical simulation model. The study area is the Red River Basin in China in Vietnam, a generally challenging basin for remotely sensed information due to frequent cloud cover. Over this region, several satellite-based P and ETact products are compared, and performance is evaluated using rain gauge records and longer-term averaged streamflow. A method is presented for fusing multiple satellite-derived ETact estimates to generate an ensemble product that may be less susceptible, on a global basis, to errors in individual modeling approaches. Subsequently, monthly satellite-derived rainfall and ETact are combined to assess the water balance for individual subcatchments and types of land use, defined using a global land use classification improved based on auxiliary satellite data. It was found that a combination of TRMM rainfall and the ensemble ETact product is consistent with streamflow records in both space and time. It is concluded that monthly storage changes, multi-annual streamflow and water yield per LULC type in the Red River Basin can be successfully assessed based on currently available global satellite-derived products.

Dynamic Hydrological Modeling in Drylands with TRMM Based Rainfall

Directory of Open Access Journals (Sweden)

Elena Tarnavsky

2013-12-01

Full Text Available This paper introduces and evaluates DryMOD, a dynamic water balance model of the key hydrological process in drylands that is based on free, public-domain datasets. The rainfall model of DryMOD makes optimal use of spatially disaggregated Tropical Rainfall Measuring Mission (TRMM datasets to simulate hourly rainfall intensities at a spatial resolution of 1-km. Regional-scale applications of the model in seasonal catchments in Tunisia and Senegal characterize runoff and soil moisture distribution and dynamics in response to varying rainfall data inputs and soil properties. The results highlight the need for hourly-based rainfall simulation and for correcting TRMM 3B42 rainfall intensities for the fractional cover of rainfall (FCR. Without FCR correction and disaggregation to 1 km, TRMM 3B42 based rainfall intensities are too low to generate surface runoff and to induce substantial changes to soil moisture storage. The outcomes from the sensitivity analysis show that topsoil porosity is the most important soil property for simulation of runoff and soil moisture. Thus, we demonstrate the benefit of hydrological investigations at a scale, for which reliable information on soil profile characteristics exists and which is sufficiently fine to account for the heterogeneities of these. Where such information is available, application of DryMOD can assist in the spatial and temporal planning of water harvesting according to runoff-generating areas and the runoff ratio, as well as in the optimization of agricultural activities based on realistic representation of soil moisture conditions.

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.

Constraining continuous rainfall simulations for derived design flood estimation

Science.gov (United States)

Woldemeskel, F. M.; Sharma, A.; Mehrotra, R.; Westra, S.

2016-11-01

Stochastic rainfall generation is important for a range of hydrologic and water resources applications. Stochastic rainfall can be generated using a number of models; however, preserving relevant attributes of the observed rainfall-including rainfall occurrence, variability and the magnitude of extremes-continues to be difficult. This paper develops an approach to constrain stochastically generated rainfall with an aim of preserving the intensity-durationfrequency (IFD) relationships of the observed data. Two main steps are involved. First, the generated annual maximum rainfall is corrected recursively by matching the generated intensity-frequency relationships to the target (observed) relationships. Second, the remaining (non-annual maximum) rainfall is rescaled such that the mass balance of the generated rain before and after scaling is maintained. The recursive correction is performed at selected storm durations to minimise the dependence between annual maximum values of higher and lower durations for the same year. This ensures that the resulting sequences remain true to the observed rainfall as well as represent the design extremes that may have been developed separately and are needed for compliance reasons. The method is tested on simulated 6 min rainfall series across five Australian stations with different climatic characteristics. The results suggest that the annual maximum and the IFD relationships are well reproduced after constraining the simulated rainfall. While our presentation focusses on the representation of design rainfall attributes (IFDs), the proposed approach can also be easily extended to constrain other attributes of the generated rainfall, providing an effective platform for post-processing of stochastic rainfall generators.

Assessment of global precipitation measurement satellite products over Saudi Arabia

Science.gov (United States)

Mahmoud, Mohammed T.; Al-Zahrani, Muhammad A.; Sharif, Hatim O.

2018-04-01

Most hydrological analysis and modeling studies require reliable and accurate precipitation data for successful simulations. However, precipitation measurements should be more representative of the true precipitation distribution. Many approaches and techniques are used to collect precipitation data. Recently, hydrometeorological and climatological applications of satellite precipitation products have experienced a significant improvement with the emergence of the latest satellite products, namely, the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) products, which can be utilized to estimate and analyze precipitation data. This study focuses on the validation of the IMERG early, late and final run rainfall products using ground-based rain gauge observations throughout Saudi Arabia for the period from October 2015 to April 2016. The accuracy of each IMERG product is assessed using six statistical performance measures to conduct three main evaluations, namely, regional, event-based and station-based evaluations. The results indicate that the early run product performed well in the middle and eastern parts as well as some of the western parts of the country; meanwhile, the satellite estimates for the other parts fluctuated between an overestimation and an underestimation. The late run product showed an improved accuracy over the southern and western parts; however, over the northern and middle parts, it showed relatively high errors. The final run product revealed significantly improved precipitation estimations and successfully obtained higher accuracies over most parts of the country. This study provides an early assessment of the performance of the GPM satellite products over the Middle East. The study findings can be used as a beneficial reference for the future development of the IMERG algorithms.

Development and validation of satellite-based estimates of surface visibility

Science.gov (United States)

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

2016-02-01

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

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 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.

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

Science.gov (United States)

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

2017-12-01

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

Statistical Modelling of Extreme Rainfall in Taiwan

NARCIS (Netherlands)

L-F. Chu (Lan-Fen); M.J. McAleer (Michael); C-C. Chang (Ching-Chung)

2012-01-01

textabstractIn this paper, the annual maximum daily rainfall data from 1961 to 2010 are modelled for 18 stations in Taiwan. We fit the rainfall data with stationary and non-stationary generalized extreme value distributions (GEV), and estimate their future behaviour based on the best fitting model.

Statistical Modelling of Extreme Rainfall in Taiwan

NARCIS (Netherlands)

L. Chu (LanFen); M.J. McAleer (Michael); C-H. Chang (Chu-Hsiang)

2013-01-01

textabstractIn this paper, the annual maximum daily rainfall data from 1961 to 2010 are modelled for 18 stations in Taiwan. We fit the rainfall data with stationary and non-stationary generalized extreme value distributions (GEV), and estimate their future behaviour based on the best fitting model.

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

and monthly water mass formation rates for different SST and SSS ranges are presented. The formation peaks are remapped geographically, to analyze the extent of the formation area. Water mass formation derived from SMOS and OSTIA compares well with the results obtained from in-situ data, although slight differences in magnitude and peak location occur. Known water masses can then be identified. Ongoing/future work aims at extending this study along different avenues by: 1) expand systematically the spatial and temporal domain of the study to additional ocean basins and to the entire time period of available SSS observations from SMOS/Aquarius; 2) perform a thorough error propagation to assess how errors in satellite SSS and SST translate into errors in water masses formation rates and geographical areas extent; and 3) explore the different options to connect the surface information to the vertical buoyancy structure to assess potential density instability (e.g., Turner angle). References [1] Sabia, R., M. Klockmann, D. Fernández-Prieto, and C. Donlon (2014), A first estimation of SMOS-based ocean surface T-S diagrams, J. Geophys. Res. Oceans, 119, 7357-7371, doi:10.1002/2014JC010120. [2] Klockmann, M., R. Sabia, D. Fernández-Prieto, C. Donlon, J. Font; Towards an estimation of water masses formation areas from SMOS-based T-S diagrams; EGU general assembly 2014, April 27-May 2, 2014. [3] Klockmann, M., R. Sabia, D. Fernández-Prieto, C. Donlon, Linking satellite SSS and SST to water mass formation; Ocean salinity science and salinity remote sensing workshop, Exeter, UK, November 26-28, 2014. [4] Font, J., A. Camps, A. Borges, M. Martín-Neira, J. Boutin, N. Reul, Y. H. Kerr, A. Hahne, and S. Mecklenburg, "SMOS: The challenging sea surface salinity measurement from space," Proceedings of the IEEE, vol. 98, pp. 649-665, 2010. [5] Le Vine, D.M.; Lagerloef, G.S.E.; Torrusio, S.E.; "Aquarius and Remote Sensing of Sea Surface Salinity from Space," Proceedings of the IEEE

Estimating the spin axis orientation of the Echostar-2 box-wing geosynchronous satellite

Science.gov (United States)

Earl, Michael A.; Somers, Philip W.; Kabin, Konstantin; Bédard, Donald; Wade, Gregg A.

2018-04-01

For the first time, the spin axis orientation of an inactive box-wing geosynchronous satellite has been estimated from ground-based optical photometric observations of Echostar-2's specular reflections. Recent photometric light curves obtained of Echostar-2 over four years suggest that unusually bright and brief specular reflections were occurring twice within an observed spin period. These bright and brief specular reflections suggested two satellite surfaces with surface normals separated by approximately 180°. The geometry between the satellite, the Sun, and the observing location at the time of each of the brightest observed reflections, was used to estimate Echostar-2's equatorial spin axis orientation coordinates. When considering prograde and retrograde rotation, Echostar-2's spin axis orientation was estimated to have been located within 30° of either equatorial coordinate pole. Echostar-2's spin axis was observed to have moved approximately 180° in right ascension, within a time span of six months, suggesting a roughly one year spin axis precession period about the satellite's angular momentum vector.

The Impact of Model and Rainfall Forcing Errors on Characterizing Soil Moisture Uncertainty in Land Surface Modeling

Science.gov (United States)

Maggioni, V.; Anagnostou, E. N.; Reichle, R. H.

2013-01-01

The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

Radar rainfall image repair techniques

Directory of Open Access Journals (Sweden)

Stephen M. Wesson

2004-01-01

Full Text Available There are various quality problems associated with radar rainfall data viewed in images that include ground clutter, beam blocking and anomalous propagation, to name a few. To obtain the best rainfall estimate possible, techniques for removing ground clutter (non-meteorological echoes that influence radar data quality on 2-D radar rainfall image data sets are presented here. These techniques concentrate on repairing the images in both a computationally fast and accurate manner, and are nearest neighbour techniques of two sub-types: Individual Target and Border Tracing. The contaminated data is estimated through Kriging, considered the optimal technique for the spatial interpolation of Gaussian data, where the 'screening effect' that occurs with the Kriging weighting distribution around target points is exploited to ensure computational efficiency. Matrix rank reduction techniques in combination with Singular Value Decomposition (SVD are also suggested for finding an efficient solution to the Kriging Equations which can cope with near singular systems. Rainfall estimation at ground level from radar rainfall volume scan data is of interest and importance in earth bound applications such as hydrology and agriculture. As an extension of the above, Ordinary Kriging is applied to three-dimensional radar rainfall data to estimate rainfall rate at ground level. Keywords: ground clutter, data infilling, Ordinary Kriging, nearest neighbours, Singular Value Decomposition, border tracing, computation time, ground level rainfall estimation

The impact of Amazonian deforestation on Amazon basin rainfall

Science.gov (United States)

Spracklen, D. V.; Garcia-Carreras, L.

2015-11-01

We completed a meta-analysis of regional and global climate model simulations (n = 96) of the impact of Amazonian deforestation on Amazon basin rainfall. Across all simulations, mean (±1σ) change in annual mean Amazon basin rainfall was -12 ± 11%. Variability in simulated rainfall was not explained by differences in model resolution or surface parameters. Across all simulations we find a negative linear relationship between rainfall and deforestation extent, although individual studies often simulate a nonlinear response. Using the linear relationship, we estimate that deforestation in 2010 has reduced annual mean rainfall across the Amazon basin by 1.8 ± 0.3%, less than the interannual variability in observed rainfall. This may explain why a reduction in Amazon rainfall has not consistently been observed. We estimate that business-as-usual deforestation (based on deforestation rates prior to 2004) would lead to an 8.1 ± 1.4% reduction in annual mean Amazon basin rainfall by 2050, greater than natural variability.

Rainfall measurement using cell phone links: classification of wet and dry periods using geostationary satellites

NARCIS (Netherlands)

van Delden, A.J.; van het Schip, T.I.; Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko; Meirink, J.F.

2017-01-01

Commercial cellular telecommunication networks can be used for rainfall estimation by measur- ing the attenuation of electromagnetic signals transmitted between antennas from microwave links. However, as the received link signal may also decrease during dry periods, a method to separate wet and dry

Weather radar rainfall data in urban hydrology

DEFF Research Database (Denmark)

Thorndahl, Søren; Einfalt, Thomas; Willems, Patrick

2017-01-01

Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances in radar hardware, data processing, numerical models, and emerging fields within urban hydrology...... necessitate an updated review of the state of the art in such radar rainfall data and applications. Three key areas with significant advances over the past decade have been identified: (1) temporal and spatial resolution of rainfall data required for different types of hydrological applications, (2) rainfall...... estimation, radar data adjustment and data quality, and (3) nowcasting of radar rainfall and real-time applications. Based on these three fields of research, the paper provides recommendations based on an updated overview of shortcomings, gains, and novel developments in relation to urban hydrological...

Toward a Framework for Systematic Error Modeling of NASA Spaceborne Radar with NOAA/NSSL Ground Radar-Based National Mosaic QPE

Science.gov (United States)

Kirstettier, Pierre-Emmanual; Honh, Y.; Gourley, J. J.; Chen, S.; Flamig, Z.; Zhang, J.; Howard, K.; Schwaller, M.; Petersen, W.; Amitai, E.

2011-01-01

Characterization of the error associated to satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving space-born passive and active microwave measurement") for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. We focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a three-month data sample in the southern part of US. The primary contribution of this study is the presentation of the detailed steps required to derive trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relics on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors arc revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall rate estimates from other sensors onboard low-earth orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.

Using satellite data on meteorological and vegetation characteristics and soil surface humidity in the Land Surface Model for the vast territory of agricultural destination

Science.gov (United States)

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

2017-04-01

vegetation cover (taken for vegetation temperature) Ta and efficient radiation temperature Ts.eff, as well as land surface emissivity E, normalized difference vegetation index NDVI, vegetation cover fraction B, and leaf area index LAI. The SEVIRI-based retrievals have included precipitation, LST Tls and Ta, E at daylight and nighttime, LAI (daily), and B. From the MSU-MR data there have been retrieved values of all the same characteristics as from the AVHRR data. The MSU-MR-based daily and monthly sums of precipitation have been calculated using the developed earlier and modified Multi Threshold Method (MTM) intended for the cloud detection and identification of its types around the clock as well as allocation of precipitation zones and determination of instantaneous maximum rainfall intensities for each pixel at that the transition from assessing rainfall intensity to estimating their daily values is a key element of the MTM. Measurement data from 3 IR MSU-MR channels (3.8, 11 i 12 μm) as well as their differences have been used in the MTM as predictors. Controlling the correctness of the MSU-MR-derived rainfall estimates has been carried out when comparing with analogous AVHRR- and SEVIRI-based retrievals and with precipitation amounts measured at the agricultural meteorological station of the study region. Probability of rainfall zones determination from the MSU-MR data, to match against the actual ones, has been 75-85% as well as for the AVHRR and SEVIRI data. The time behaviors of satellite-derived and ground-measured daily and monthly precipitation sums for vegetation season and yeaŗ correspondingly, have been in good agreement with each other although the first ones have been smoother than the latter. Discrepancies have existed for a number of local maxima for which satellite-derived precipitation estimates have been less than ground-measured values. It may be due to the different spatial scales of areal satellite-derived and point ground-based estimates. Some

Estimation of groundwater recharge via percolation outputs from a rainfall/runoff model for the Verlorenvlei estuarine system, west coast, South Africa

Science.gov (United States)

Watson, Andrew; Miller, Jodie; Fleischer, Melanie; de Clercq, Willem

2018-03-01

Wetlands are conservation priorities worldwide, due to their high biodiversity and productivity, but are under threat from agricultural and climate change stresses. To improve the water management practices and resource allocation in these complex systems, a modelling approach has been developed to estimate potential recharge for data poor catchments using rainfall data and basic assumptions regarding soil and aquifer properties. The Verlorenvlei estuarine lake (RAMSAR #525) on the west coast of South Africa is a data poor catchment where rainfall records have been supplemented with farmer's rainfall records. The catchment has multiple competing users. To determine the ecological reserve for the wetlands, the spatial and temporal distribution of recharge had to be well constrained using the J2000 rainfall/runoff model. The majority of rainfall occurs in the mountains (±650 mm/yr) and considerably less in the valley (±280 mm/yr). Percolation was modelled as ∼3.6% of rainfall in the driest parts of the catchment, ∼10% of rainfall in the moderately wet parts of the catchment and ∼8.4% but up to 28.9% of rainfall in the wettest parts of the catchment. The model results are representative of rainfall and water level measurements in the catchment, and compare well with water table fluctuation technique, although estimates are dissimilar to previous estimates within the catchment. This is most likely due to the daily timestep nature of the model, in comparison to other yearly average methods. These results go some way in understanding the fact that although most semi-arid catchments have very low yearly recharge estimates, they are still capable of sustaining high biodiversity levels. This demonstrates the importance of incorporating shorter term recharge event modeling for improving recharge estimates.

Location-Based Rainfall Nowcasting Service for Public

Science.gov (United States)

Woo, Wang-chun

2013-04-01

The Hong Kong Observatory has developed the "Short-range Warning of Intense Rainstorms in Localized Systems (SWIRLS)", a radar-based rainfall nowcasting system originally to support forecasters in rainstorm warning and severe weather forecasting such as hail, lightning and strong wind gusts in Hong Kong. The system has since been extended to provide rainfall nowcast service direct for the public in recent years. Following the launch of "Rainfall Nowcast for the Pearl River Delta Region" service provided via a Geographical Information System (GIS) platform in 2008, a location-based rainfall nowcast service served through "MyObservatory", a smartphone app for iOS and Android developed by the Observatory, debuted in September 2012. The new service takes advantage of the capability of smartphones to detect own locations and utilizes the quantitative precipitation forecast (QPF) from SWIRLS to provide location-based rainfall nowcast to the public. The conversion of radar reflectivity data (at 2 or 3 km above ground) to rainfall in SWIRLS is based on the Z-R relationship (Z=aRb) with dynamical calibration of the coefficients a and b determined using real-time rain gauge data. Adopting the "Multi-scale Optical-flow by Variational Analysis (MOVA)" scheme to track the movement of radar echoes and Semi-Lagrangian Advection (SLA) scheme to extrapolate their movement, the system is capable of producing QPF for the next six hours in a grid of 480 x 480 that covers a domain of 256 km x 256 km once every 6 minutes. Referencing the closest point in a resampled 2-km grid over the territory of Hong Kong, a prediction as to whether there will be rainfall exceeding 0.5 mm in every 30 minute intervals for the next two hours at users' own or designated locations are made available to the users in both textual and graphical format. For those users who have opted to receive notifications, a message would pop up on the user's phone whenever rain is predicted in the next two hours in a user

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.

Duration-frequency relationships of heavy rainfall in Santa Catarina, Brazil

Directory of Open Access Journals (Sweden)

Álvaro José Back

2012-06-01

Full Text Available The purpose of this study was to adjust equations that establish relationships between rainfall events with different duration and data from weather stations in the state of Santa Catarina, Brazil. In this study, the relationships between different duration heavy rainfalls from 13 weather stations of Santa Catarina were analyzed. From series of maximum annual rainfalls, and using the Gumbel-Chow distribution, the maximum rainfall for durations between 5 min and 24 h were estimated considering return periods from 2 to 100 years. The data fit to the Gumbel-Chow model was verified by the Kolmogorov-Smirnov test at 5 % significance. The coefficients of Bell's equation were adjusted to estimate the relationship between rainfall duration t (min and the return period T (y in relation to the maximum rainfall with a duration of 1 hour and a 10 year return period. Likewise, the coefficients of Bell's equation were adjusted based on the maximum rainfall with a duration of 1 day and a 10 year return period. The results showed that these relationships are viable to estimate short-duration rainfall events at locations where there are no rainfall records.

Harmonizing estimates of forest land area from national-level forest inventory and satellite imagery

Science.gov (United States)

Bonnie Ruefenacht; Mark D. Nelson; Mark Finco

2009-01-01

Estimates of forest land area are derived both from national-level forest inventories and satellite image-based map products. These estimates can differ substantially within subregional extents (e.g., states or provinces) primarily due to differences in definitions of forest land between inventory- and image-based approaches. We present a geospatial modeling approach...

Rainfall Estimation and Performance Characterization Using an X-band Dual-Polarization Radar in the San Francisco Bay Area

Science.gov (United States)

Cifelli, R.; Chen, H.; Chandra, C. V.

2016-12-01

The San Francisco Bay area is home to over 5 million people. In February 2016, the area also hosted the NFL Super bowl, bringing additional people and focusing national attention to the region. Based on the El Nino forecast, public officials expressed concern for heavy rainfall and flooding with the potential for threats to public safety, costly flood damage to infrastructure, negative impacts to water quality (e.g., combined sewer overflows) and major disruptions in transportation. Mitigation of the negative impacts listed above requires accurate precipitation monitoring (quantitative precipitation estimation-QPE) and prediction (including radar nowcasting). The proximity to terrain and maritime conditions as well as the siting of existing NEXRAD radars are all challenges in providing accurate, short-term near surface rainfall estimates in the Bay area urban region. As part of a collaborative effort between the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory, Colorado State University (CSU), and Santa Clara Valley Water District (SCVWD), an X-band dual-polarization radar was deployed in Santa Clara Valley in February of 2016 to provide support for the National Weather Service during the Super Bowl and NOAA's El Nino Rapid Response field campaign. This high-resolution radar was deployed on the roof of one of the buildings at the Penitencia Water Treatment Plant. The main goal was to provide detailed precipitation information for use in weather forecasting and assists the water district in their ability to predict rainfall and streamflow with real-time rainfall data over Santa Clara County especially during a potentially large El Nino year. The following figure shows the radar's coverage map, as well as sample reflectivity observations on March 06, 2016, at 00:04UTC. This paper presents results from a pilot study from February, 2016 to May, 2016 demonstrating the use of X-band weather radar for quantitative precipitation

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

Science.gov (United States)

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

2018-04-01

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

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

Science.gov (United States)

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

2013-01-01

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

Use of satellite data to estimate radiation and evaporation for northwest Mexico

International Nuclear Information System (INIS)

Stewart, J.B.; Watts, C.J.; Rodriguez, J.C.; Bruin, H.A.R. de; Berg, A.R. van den; Garatuza-Payán, J.

1999-01-01

Incoming solar radiation was estimated from visible band data obtained by the GOES satellite over northwest Mexico. Comparisons against ground-based measurements of incoming solar radiation showed good agreement, particularly in months with low cloud cover. The data from an automatic weather station installed within the Yaqui Valley Irrigation Scheme was used to estimate potential evaporation from a formula based on incoming solar radiation and climatological values of temperature. The success of this formula was assessed by comparison against potential evaporation estimated using the Penman and Penman–Monteith formulae and measurements of net radiation. (author)

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

DEFF Research Database (Denmark)

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

2016-01-01

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

Estimates of peak flood discharge for 21 sites in the Front Range in Colorado in response to extreme rainfall in September 2013

Science.gov (United States)

Moody, John A.

2016-03-21

Extreme rainfall in September 2013 caused destructive floods in part of the Front Range in Boulder County, Colorado. Erosion from these floods cut roads and isolated mountain communities for several weeks, and large volumes of eroded sediment were deposited downstream, which caused further damage of property and infrastructures. Estimates of peak discharge for these floods and the associated rainfall characteristics will aid land and emergency managers in the future. Several methods (an ensemble) were used to estimate peak discharge at 21 measurement sites, and the ensemble average and standard deviation provided a final estimate of peak discharge and its uncertainty. Because of the substantial erosion and deposition of sediment, an additional estimate of peak discharge was made based on the flow resistance caused by sediment transport effects.Although the synoptic-scale rainfall was extreme (annual exceedance probability greater than 1,000 years, about 450 millimeters in 7 days) for these mountains, the resulting peak discharges were not. Ensemble average peak discharges per unit drainage area (unit peak discharge, [Qu]) for the floods were 1–2 orders of magnitude less than those for the maximum worldwide floods with similar drainage areas and had a wide range of values (0.21–16.2 cubic meters per second per square kilometer [m3 s-1 km-2]). One possible explanation for these differences was that the band of high-accumulation, high-intensity rainfall was narrow (about 50 kilometers wide), oriented nearly perpendicular to the predominant drainage pattern of the mountains, and therefore entire drainage areas were not subjected to the same range of extreme rainfall. A linear relation (coefficient of determination [R2]=0.69) between Qu and the rainfall intensity (ITc, computed for a time interval equal to the time-of-concentration for the drainage area upstream from each site), had the form: Qu=0.26(ITc-8.6), where the coefficient 0.26 can be considered to be an

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

DEFF Research Database (Denmark)

Brandt, Martin Stefan; Mbow, Cheikh; Diouf, Abdoul A.

2015-01-01

After a dry period with prolonged droughts in the 1970s and 1980s, recent scientific outcome suggests that the decades of abnormally dry conditions in the Sahel have been reversed by positive anomalies in rainfall. Various remote sensing studies observed a positive trend in vegetation greenness...... over the last decades which is known as the re-greening of the Sahel. However, little investment has been made in including long-term ground-based data collections to evaluate and better understand the biophysical mechanisms behind these findings. Thus, deductions on a possible increment in biomass...... remain speculative. Our aim is to bridge these gaps and give specifics on the biophysical background factors of the re-greening Sahel. Therefore, a trend analysis was applied on long time series (1987-2013) of satellite-based vegetation and rainfall data, as well as on ground-observations of leaf biomass...

Congo Basin rainfall climatology: can we believe the climate models?

Science.gov (United States)

Washington, Richard; James, Rachel; Pearce, Helen; Pokam, Wilfried M; Moufouma-Okia, Wilfran

2013-01-01

The Congo Basin is one of three key convective regions on the planet which, during the transition seasons, dominates global tropical rainfall. There is little agreement as to the distribution and quantity of rainfall across the basin with datasets differing by an order of magnitude in some seasons. The location of maximum rainfall is in the far eastern sector of the basin in some datasets but the far western edge of the basin in others during March to May. There is no consistent pattern to this rainfall distribution in satellite or model datasets. Resolving these differences is difficult without ground-based data. Moisture flux nevertheless emerges as a useful variable with which to study these differences. Climate models with weak (strong) or even divergent moisture flux over the basin are dry (wet). The paper suggests an approach, via a targeted field campaign, for generating useful climate information with which to confront rainfall products and climate models.

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.

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.