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Bias correction of satellite retrievals of orographic precipitation

Satellite remote sensing observations at high spatial and temporal resolutions have advantages for precipitation retrievals. However, it is still challenging to produce reliable precipitation estimates over complex terrain regions. This paper proposes a deep learning framework to correct the bias associated with satellite revivals of orographic precipitation, focusing on the influence of terrain on precipitation estimation. Using precipitation product derived by the NOAA Climate Prediction Center morphing technique (i.e., CMORPH-CRT) in the western United States as an example, the deep learning model is trained and tested, and the ground-based Stage IV rainfall estimates are used as target labels in the training phase. Independent validation results show that the deep learning-based bias correction model can significantly enhance the performance of satellite precipitation products over complex terrain.