Abstract
Remote sensing of precipitation is critical for regional, continental, and global weather, water, and climate research. This study develops a machine learning mechanism to link between point-wise rain gauge measurements, ground-based and spaceborne radar reflectivity observations. Two neural network models are designed to construct a hybrid rainfall system, where the ground radar is used to bridge the scale gaps between rain gauge and satellite. The first model is trained for ground radar using rain gauge data as target labels, whereas the second one is for spaceborne radar using ground radar estimates as training labels. Data from TRMM Precipitation Radar (PR) and GPM Dual-frequency Precipitation Radar (DPR) are utilized to illustrate the application of this hybrid rainfall system. Validation using independent ground-based observations as well as the standard PR and DPR products demonstrates the promising performance and generality of this innovative machine learning algorithm.
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