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Quantification of uncertainties associated with satellite precipitation products is a prior requirement for their better applications in earth science studies. An improved scheme is developed in this study to decompose mean bias error (MBE) and mean square error (MSE) into three components, i.e., MBE and MSE associated hits, missed precipitation, and false alarms, respectively, which are weighted by their relative frequencies of occurrence (RFO). The trend of total MBE or MSE is then naturally decomposed into six components according to the chain rule for derivatives. Quantitative estimation of individual contributions to total MBE and MSE is finally derived. The method is applied to validation of Integrated MultisatellitE Retrievals for GPM (IMERG) in Mainland China. MBE associated with false alarms is an important driver for total MBE, while MSE associated with hits accounts for more than 85% of MSE, except in inland semi-arid area. The RFO of false alarms increases, whereas the RFO of missed precipitation decreases. Both factors lead in part to a growing trend for total MBE. Detection of precipitation should be improved in the IMERG algorithm. More specifically, the priority should be to reduce false alarms.

An Improved Assessment Method and Its Application to the Latest IMERG Rainfall Product in Mainland China