Abstract
AbstractRemote sensing of precipitation is critical for regional, continental, and global water and climate research. This study develops a deep 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 model is for spaceborne Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) using ground radar estimates as training labels. Data from 1 year of observations in Florida during 2009 are utilized to illustrate the application of this hybrid rainfall system. Validation using independent data in 2009, as well as 2‐year comparison against the standard PR products, demonstrates the promising performance and generality of this innovative rainfall algorithm.
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