Event-Based Bias Correction of the GPM IMERG V06 Product by Random Forest Method over Mainland China

Abstract

The Global Precipitation Measurement (GPM) IMERG V06 product showed excellent performance in detecting precipitation, but still have room to improve. This study proposed an event-based bias correction strategy through random forest (RF) method to improve accuracy of the GPM IMERG V06 product over mainland China. Results showed that, over mainland China, biases caused by ‘hits’ events are most responsible for the errors of the GPM product, and ‘falseAlarms’ events took the least responsibility for that because of the small GPM values for ‘falseAlarms’ events. Compared with the raw GPM product, the bias-corrected GPM product showed better performance in both fitting observed precipitation values and in detecting precipitation events, thus the event-based bias-strategy in this study is credible. After bias correction, the ability of the bias-corrected GPM product was significantly improved for ‘hits’ events, but showed slight deterioration in RMSE and MAE and significant improvements in FAR and CSI for ‘falseAlarms’ events. This is because the established RF classification model tends to learn characteristics of the event with larger proportion, and then performed better in correctly identifying the event with larger proportion in the subregion.