Machine Learning Approaches for Improving Near-Real-Time IMERG Rainfall Estimates by Integrating Cloud Properties from NOAA CDR PATMOS-x.

The Global Precipitation Measurement (GPM) mission provides satellite precipitation products with an unprecedented spatiotemporal resolution and spatial coverage. However, its near-real-time (NRT) product still suffers from low accuracy. This study aims to improve the early run of the Integrated Multisatellite Retrievals for GPM (IMERG) by using four machine learning approaches, that is, support vector machine (SVM), random forest (RF), artificial neural network (ANN), and extreme gradient boosting (XGB). The cloud properties are selected as the predictors in addition to the original IMERG in these approaches. All of the four approaches show similar improvement, with 53%–60% reduction of root-mean-square error (RMSE) compared with the original IMERG in a humid area, that is, the Dongjiang River basin (DJR) in southeastern China. The improvements are even greater in a semiarid area, that is, the Fenhe River basin (FHR) in central China, where the RMSE reduction ranges from 63% to 66%. The products generated by the machine learning methods perform similarly to or even outperform the final run of IMERG. Feature importance analysis, a technique to evaluate input features based on how useful they are in predicting a target variable, indicates that the cloud height and the brightness temperature are the most useful information in improving satellite precipitation products, followed by the atmospheric reflectivity and the surface temperature. This study shows that a more accurate NRT precipitation product can be produced by combining machine learning approaches and cloud information, which is of importance for hydrological applications that require NRT precipitation information including flood monitoring. [ABSTRACT FROM AUTHOR]