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

Giancarlo, Rivolta; Michele De Rosa,; Frank Silvio Marzano,

doi:10.5721/itjrs20104218

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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.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/43379

Titolo:	Precipitation Nowcasting from Geostationary Satellite: Neural Approaches Trained By Polar Orbiting and Ground-Based Data
Autori interni:	MARZANO, FRANK SILVIO
Data di pubblicazione:	2010
Rivista:	RIVISTA ITALIANA DI TELERILEVAMENTO
Abstract:	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.
Handle:	http://hdl.handle.net/11573/43379
Appare nelle tipologie:	01.a Pubblicazione su Rivista

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