Precipitation Nowcasting from Geostationary Satellite: Neural Approaches Trained By Polar Orbiting and Ground-Based Data

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.



Titolo: Precipitation Nowcasting from Geostationary Satellite: Neural Approaches Trained By Polar Orbiting and Ground-Based Data
Autori: 
MARZANO, FRANK SILVIO
mostra contributor esterni 
Data di pubblicazione: 2010
Rivista: 
RIVISTA ITALIANA DI TELERILEVAMENTO  
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.
Handle: http://hdl.handle.net/11573/43379
Appartiene alla tipologia:01a Articolo in rivista

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