Ensemble learning for improving satellite retrievals of orographic precipitation

Abstract

Passive satellite precipitation sensors suffer from resolving heavy rain and/or shallow orographic precipitation systems, thereby restricting the performance of various composite satellite precipitation products. It is crucial to correct the error patterns of satellite precipitation retrievals for improving hydrometeorology applications at different space-time scales. To achieve this goal, this paper proposes a deep learning system with ensemble optimization strategies to quantify the uncertainties of satellite products with an emphasis on orographic precipitation. In particular, a deep convolutional neural network (CNN) is designed, which utilizes the ground-based Stage IV precipitation estimates as target labels to reduce biases involved in the precipitation product derived from the NOAA/Climate Prediction Center morphing technique (CMORPH). In order to boost the performance of the single model, an ensemble strategy is devised along with the deep learning model. The results show that the accuracy of CMORPH has been significantly improved via the proposed methodology, indicating the great potential of machine learning in improving future satellite precipitation retrievals.