Abstract
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.
Highlights
probability of detection (POD) in the inland watershed (IL) watershed is generally smaller than that in other watersheds. Another interesting feature is that POD in the Yungui Plateau is relatively smaller than the surrounding regions, which likely implies that passive microwave (PMW) sensor nighttime precipitation should be improved in the algorithm
Seasonal mean Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) rainfall tends to be smaller than gauged measurements, especially in the summer rainy season, which likely indicates that IMERG underestimates convective rainfall
Precipitation occurrence detection described by POD, false alarm ratio (FAR), and CSI are highly dependent on rainfall amount
Summary
The dual-frequency precipitation radar (DPR) on the GPM core satellite improves the detection capability of light rainfall (0.2 mm h−1 ) and snowfall. It expands coverage over latitude band 65◦ N–65◦ S [4]. These active instruments provide new insights into precipitation and a vital source for calibrating retrievals from other precipitation sensors, e.g., passive microwave (PMW) sensors. The TRMM microwave imager (TMI) and GPM microwave imager (GPI) provide a good reference for the cross-calibration of PWW imagers and sounders onboard other platforms, which spans the temporal and spatial coverage of PMW precipitation retrievals [5,6].
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