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Abstract: Integrated Multi‐Satellite Retrievals for Global Precipitation Measurement (IMERG) is the last generation precipitation data source for a wide array of research, operational, and societal applications. The Global Precipitation Measurement mission provides these global and high‐resolution precipitation estimates through advanced satellite‐based radar and radiometers. The degree of improvement of the new IMERG products needs to be investigated to further advance the algorithm's development and application. This study focuses on systematically and extensively evaluating the uncalibrated Version 3 Late Run IMERG product, which has both backward and forward morphing, and highlights the level of improvement in comparison to its predecessor Version 7 Tropical Rainfall Measurement Mission (TRMM)‐based Multi‐satellite Precipitation Analysis real‐time product. Retrievals from different passive microwave (PMW) and infrared (IR) sensors contributing to IMERG are evaluated over the conterminous United States using ground‐based sensor precipitation estimates derived from the Multi‐Radar Multi‐Sensor system as reference. An error decomposition scheme is implemented to separate the total error into three independent components, hit, miss‐rain, and false‐rain biases, to trace the degree of improvement of the new algorithm. IMERG exhibits definite improvement related to miss‐rain and false‐rain bias reduction and hit rate. The improvement relative to the TRMM ‐IR component is more substantial than relative to the PMW retrieval as a result of the new Kalman smoother and the PMW morphing reducing the use of IR relative to the TRMM‐based Multi‐satellite Precipitation Analysis. Findings of this study confirm the advances of the new generation of multisatellite precipitation relative to its predecessor and highlight areas requiring additional investigation. [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]