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

A quantitative and physical understanding of satellite rainfall uncertainties provides meaningful guidance on improving algorithms to advance hydrologic prediction. The aim of this study is to characterize satellite rainfall errors and their impact on hydrologic fluxes based on fundamental governing factors that dictate the accuracy of passive remote sensing of precipitation. These governing factors are land features-comprising topography (elevation)-and climate type, representing the average ambient atmospheric conditions. First, the study examines satellite rainfall errors of three major products, 3B42RT, Climate prediction center MORHing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), by breaking the errors down into independent components (hit, miss-rain, and false-rain biases) and investigating their contribution to runoff and soil moisture errors. The uncertainties of three satellite rainfall products are explored for five regions of the Mississippi River basin that are categorized grid cell by grid cell (at the native spatial resolution of satellite products) based on topography and climate. It is found that total and hit biases dictate the temporal trend of soil moisture and runoff errors, respectively. Miss-rain and hit biases are the leading errors in the 3B42RT and CMORPH products, respectively, whereas false-rain bias is a pervasive problem of the PERSIANN product. For 3B42RT and CMORPH, about 50%-60% of grid cells are influenced by the total bias during winter and 60%-70% of grid cells during summer. For PERSIANN, about 70%-80% of the grid cells are marked by total bias during the summer and winter seasons. False-rain bias gradually increases from lowland to highland regions universally for all three satellite rainfall products. Overall, the study reveals that satellite rainfall uncertainty is dependent more on topography than the climate of the region. This study's results indicate that it is now worthwhile to assimilate the static knowledge of topography in the satellite estimation of precipitation to minimize the uncertainty in anticipation of the Global Precipitation Measurement mission. [ABSTRACT FROM AUTHOR]