PROPOSAL OF A NEW SATELLITE RAINFALL HIDREDV2 BASED ON DEEP LEARNING USING THE GEOSTATIONARY METEOROLOGICAL SATELLITE HIMAWARI

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  • 静止気象衛星ひまわりを用いた深層学習による新たな衛星雨量HiDREDv2の提案

Abstract

<p> In developing countries where meteorological observation networks are not sufficiently deployed, the provision of accurate satellite rainfall using satellite products is expected not only for disaster prevention, but also to expand the range of application of technology and research requiring observed rainfall. The aim of this study is to propose a new satellite rainfall estimation algorithm HiDREDv2 by using a fully convolutional neural network, on the geostationary meteorological satellite Himawari. In this study, a model suitable for meteorological phenomena is constructed by combining the features of each of the existing models. The accuracy of this model for 6-hour rainfall accumulation was 13.26 for RMSE and 0.69 for FSS. The accuracy of the model was significantly improved over GSMaP for the 4-day accumulated rainfall during the heavy rainfall period due to the heavy rainfall in July 2008.</p>

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