Comprehensive Evaluation and Error-Component Analysis of Four Satellite-Based Precipitation Estimates against Gauged Rainfall over Mainland China

The Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) V06 product has been widely studied, but the errors and source of within IMERG over diverse climate regions still need to be quantified. To this end, final run gauge-calibrated (V06C) uncalibrated (V06UC) products are comprehensively evaluated here against 2088 precipitation gauges acquired between March 2014 June 2018 China. Moreover, V06C V06UC rainfall estimates compared Estimation from Remotely Sensed Imagery using Artificial Neural Networks (PERSIANN)-Climate Data Record (CDR) Climate Prediction Center morphing technique (CMORPH) gauge-satellite blended (BLD) products. Continuous statistical indices two error decomposition schemes used quantify their performance. Key results as follows. (1) Except V06UC’s relatively high underestimation Tibetan Plateau (TP) overestimation Xinjiang (XJ), Northeastern China (DB), Northern (HB) CDR’s severe TP, all four satellite-based can generally capture spatial pattern agree better with gauge observations humid than semi-humid, semi-arid, arid regions. (2) All indicators show that CDR worst performance, whereas BLD is best product. As products, improved estimate. Results calibration algorithm (GCA) in active effect terms r, POD, CSI. (3) Within subregions, demonstrate performance XJ region which exhibits highest FAR lowest POD CSI values among (4) In intensity distribution, summer China, overestimate frequency moderate light events (<25 mm/day) underestimate heavy (>42 mm/day). (5) relative bias ratio (RBR) analysis shows contribution missed tends lower wetter addition, same region, clearly winter. summer, false dominates total error, leading sources Future refinement efforts should focus on decreasing winter improving snow during