Multiscale Performance of Global Blended Satellite Precipitation Products Over Taiwan

Strong land–ocean interactions and complex terrain have been challenging the accuracy of satellite precipitation products (SPPs). To recognize the error patterns of the mainstream SPPs over Taiwan, this study evaluates the temporal and spatial performance of NOAA Climate Prediction Center (CPC) morphing technique (CMORPH V1) and NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG V07 Final) during 2019–2021, with an emphasis on exploring the multi-scale performance of CMORPH and IMERG and their dependence on precipitation intensity and season. The precipitation estimates produced from the operational Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system are employed as ground-based reference data, and various statistical metrics are calculated against this reference grid by grid to evaluate the performance of CMORPH and IMERG at yearly, seasonal and daily scales. A wide range of evaluation metrics such as the relative bias (RB), Pearson correlation coefficient (CC), mean error (ME), normalized mean error (NME), mean absolute error (MAE), normalized mean absolute error (NMAE) and root mean squared error (RMSE) are considered in the study. The verification skills of SPPs at different precipitation intensity thresholds is analyzed through the probability of detection (POD), false alarm ratio (FAR), frequency bias index (FBI) and Heidke skill score (HSS). It is universally observed that both products are prone to underestimate precipitation over Taiwan, especially over high-elevation locations. Overall, CMORPH shows a better capability of capturing the precipitation spatial patterns and IMERG has smaller estimation errors of rainfall intensity than CMORPH over Taiwan.