Improving Inter‐Satellite Tropical Cyclone Precipitation Consistency by Deep Learning

Abstract The Global Precipitation Measurement mission satellite constellation comprises multiple satellites that produce widely used global tropical cyclone precipitation estimates. The quality of precipitation estimates among these satellites varies significantly due to differences in mission purpose, channel availability, and spatial resolution. Currently, the histogram matching approach is widely adopted to improve precipitation consistency among these satellites, which is effective at lowering overall bias, but often at the expense of other accuracy metrics. With point‐wise precipitation being the single predictor in this current method, it is oblivious to any spatial patterns. This study presents a deep learning approach to enhance the consistency of precipitation estimates using data from 12 satellites. Results show that the deep learning approach significantly outperforms the traditional histogram matching approach by simultaneously improving all accuracy metrics by considering spatial pattern information. The deep learning approach is particularly effective when the original satellite precipitation estimates show clear non‐linear error features.