Deep Learning for Bias Correction of Satellite Retrievals of Orographic Precipitation

The performance of various composite satellite precipitation products is severely limited by their individual passive microwave (PMW)-based retrieval uncertainties because the PMW sensors have difficulties in resolving heavy rain and/or shallow orographic precipitation systems. Characterizing the error structure of PMW retrievals is crucial to improving precipitation mapping at different space–time scales. To this end, this article introduces a machine learning framework to quantify the uncertainties associated with satellite precipitation products with an emphasis on orographic precipitation. A deep convolutional neural network (CNN) is designed, which utilizes the ground-based Stage IV precipitation estimates as target labels in the training phase, to reduce biases involved in the precipitation product derived using the NOAA/Climate Prediction Center morphing technique (CMORPH). The products before and after bias correction are evaluated using four independent precipitation events over the coastal mountain region in the western United States, and the impact of topography on satellite-based precipitation retrievals is quantified. Experimental results show that the orographic gradients have a strong impact on precipitation retrievals in complex terrain regions. The accuracy of CMORPH is dramatically enhanced after applying the proposed machine learning-based bias correction technique. Using Stage IV data as references, the overall correlation (CC), normalized mean error (NME), and normalized mean absolute error (NMAE) of CMORPH are improved from 0.55, 32%, 63%, to 0.88, −2%, 39%, respectively, after bias correction for the independent case studies presented in this article. Such a machine learning scheme also has great potential for improved fusion of other or future satellite precipitation retrievals.