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.
Precipitation Nowcasting from Geostationary Satellite: Neural Approaches Trained By Polar Orbiting and Ground-Based Data / Giancarlo, Rivolta; Michele De, Rosa; Marzano, FRANK SILVIO. - In: RIVISTA ITALIANA DI TELERILEVAMENTO. - ISSN 1129-8596. - STAMPA. - 48(2010), pp. 91-115. [10.5721/itjrs20104218]
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Titolo: | Precipitation Nowcasting from Geostationary Satellite: Neural Approaches Trained By Polar Orbiting and Ground-Based Data | |
Autori: | ||
Data di pubblicazione: | 2010 | |
Rivista: | ||
Citazione: | Precipitation Nowcasting from Geostationary Satellite: Neural Approaches Trained By Polar Orbiting and Ground-Based Data / Giancarlo, Rivolta; Michele De, Rosa; Marzano, FRANK SILVIO. - In: RIVISTA ITALIANA DI TELERILEVAMENTO. - ISSN 1129-8596. - STAMPA. - 48(2010), pp. 91-115. [10.5721/itjrs20104218] | |
Handle: | http://hdl.handle.net/11573/43379 | |
Appartiene alla tipologia: | 01a Articolo in rivista |