Precipitation Nowcasting from Geostationary Satellite: Neural Approaches Trained By Polar Orbiting and Ground-Based Data
RIVISTA ITALIANA DI TELERILEVAMENTO(2010)
摘要
This work explores possible improvements of the Neural Combined Algorithm for Storm Tracking (NeuCAST) proposed in Marzano et al. [2007]. In its single channel version, developed for the Visible-Infrared Imager (VIRI) onboard Meteosat-7. this technique has been successfully applied to the rainfall field nowcast from thermal infrared (TIR) and microwave (MW) passive-sensor imagery aboard, respectively, Geostationary-Earth-Orbit and Low-Earth-Orbit satellites. The multi-channel NeuCAST methodology is here introduced,. It extends the single-channel NeuCAST technique to infrared (IR) multi-channel data available from Meteosat Second Generation (MSG) and MW data from ground based meteorological Radar.
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关键词
Nowcasting,Neural Networks,Precipitation,Geostationary satellite,Meteorological radar
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