Warm-Season Microwave Integrated Retrieval System Precipitation Improvement Using Machine Learning Methods

Abstract

This study compares the performance of five selected machine learning models regarding precipitation climatology during the warm season in 2022 and 2023 over the Continental U.S. Input features included retrieved products from the Microwave Integrated Retrieval System (MiRS) based on NOAA-20 ATMS data. The radar-based instantaneous Multi-Radar Multi-Sensor System precipitation was used for model training and validation. Among the models, three used a U-Net architecture and two used a Deep Neural Network (DNN) architecture. The U-Net models all significantly outperformed DNN models for the evaluated metrics. While the DNN architecture can only learn from local inputs, the U-Net also has the capability to learn from neighborhood spatial patterns. As such, the DNN overcorrected the precipitation amounts that MiRS had overestimated, leading to net underestimation, but also failed to improve the overall performance relative to the original MiRS estimates. The U-Net not only corrected MiRS overestimation in the central U.S., but also improved the MiRS dry bias over the Southeast.Of the five experiments, that which used the MiRS retrieved column-integrated hydrometeors of graupel water path, rain water path, cloud liquid water, total precipitable water and geolocation information demonstrated the best performance, improving the MiRS spatial correlation coefficient from 0.75 to 0.89 and reducing the mean bias percentage from 11.95% to -6.33% for 2022 accumulated precipitation. This suggests that applying an appropriate architecture and input features provides an opportunity to determine more accurate physical and statistical relationships which can include spatial and regional dependence, leading to improved microwave-based precipitation estimates.