Towards Bias Correction of Satellite Precipitation Retrievals in Complex Regions with Deep Learning: A Case Study over Taiwan

Precipitation retrieval biases exist in composite satellite precipitation products (SPPs) due to the limitations of passive microwave (PMW) sensors in resolving shallow and/or heavy rain. The resulting underestimation and overestimation of precipitation intensity and potential precipitation position errors lead to inconsistent and unstable performance of SPPs through different rainfall types at different geophysical locations. This study aims to correct biases and alleviate the effects of uncertainties in precipitation estimates from NOAA Climate Prediction Center (CPC) MORPHing technique (CMORPH) and NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). Specifically, a deep convolutional neural network (CNN) model is employed to capture and correct the precipitation error patterns in CMORPH and IMERG using the ground-based operational Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system product over Taiwan as the training references. The improved performance of satellite precipitation products is quantitatively validated, which demonstrates the capability of the devised deep learning based bias correction approach.