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Providing error information associated with existing satellite precipitation estimates is crucial to advancing applications in hydrologic modeling. In this study, we present a method of estimating the square difference prediction of satellite precipitation (hereafter used synonymously with “error variance”) using regression model for three satellite precipitation products (3B42RT, CMORPH, and PERSIANN-CCS) using easily available geophysical features and satellite precipitation rate. Building on a suite of recent studies that have developed the error variance models, the goal of this work is to explore how well the method works around the world in diverse geophysical settings. Topography, climate, and seasons are considered as the governing factors to segregate the satellite precipitation uncertainty and fit a nonlinear regression equation as a function of satellite precipitation rate. The error variance models were tested on USA, Asia, Middle East, and Mediterranean region. Rain-gauge based precipitation product was used to validate the error variance of satellite precipitation products. The regression approach yielded good performance skill with high correlation between simulated and observed error variances. The correlation ranged from 0.46 to 0.98 during the independent validation period. In most cases (~ 85% of the scenarios), the correlation was higher than 0.72. The error variance models also captured the spatial distribution of observed error variance adequately for all study regions while producing unbiased residual error. The approach is promising for regions where missed precipitation is not a common occurrence in satellite precipitation estimation. Our study attests that transferability of model estimators (which help to estimate the error variance) from one region to another is practically possible by leveraging the similarity in geophysical features. Therefore, the quantitative picture of satellite precipitation error over ungauged regions can be discerned even in the absence of ground truth data. [ABSTRACT FROM AUTHOR]

The present study addresses the estimation of error variance (mean square error, MSE) of three satellite rainfall products: i) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product of 3B42RT; ii) Climate Prediction Center (CPC) Morph (CMORPH); and iii) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Nonlinear regression model is used to fit the response variable (satellite rainfall error variance) with explanatory variable (satellite rainfall rate) by grouping them as function of three key geophysical features: topography, climate, and season. The results of the study suggest that the error variance of a rainfall product is strongly correlated with rainfall rate and can be expressed as a power-law function. The geophysical feature based error classification analysis helps in achieving superior accuracy for prognostic error variance quantification in the absence of ground truth data. The multiple correlation coefficients between the estimated and observed error variance over an independent validation region (Upper Mississippi River basin) and time period (2007-2010) are found to be 0.75, 0.86, and 0.87 for 3B42RT, CMORPH, and PERSIANN-CCS products, respectively. In another validation region (Arkansas-Red River basin), the correlation coefficients are 0.59, 0.89, and 0.92 for the same products, respectively. Results of the assessment of error variance models reveal that the type of error component present in a satellite rainfall product directly impacts the accuracy of estimated error variance. The model estimates the error variance more accurately when the precipitation error components are mostly hit bias or false precipitation, while for a product with extensive missed precipitation, the accuracy of estimated error variance is significantly compromised. The study clearly demonstrates the feasibility of quantifying the error variance of satellite rainfall products in a spatially and temporally varying manner using readily available geophysical features and rainfall rate. The study is a path finder to a globally applicable and operationally feasible methodology for error variance estimation at high spatial and temporal scales for advancing satellite rainfall applications in ungauged basins. [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]

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]