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

被引：0

作者：

Rivolta, Giancarlo ^{[1
,2
]}

de Rosa, Michele ^{[1
]}

Marzano, Frank Silvio ^{[1
,2
]}

机构：

[1] Univ Roma La Sapienza, Dipartimento Ingn Elettron, I-00184 Rome, Italy

[2] Univ Aquila, CETEMPS, I-67100 Laquila, Italy

来源：

RIVISTA ITALIANA DI TELERILEVAMENTO | 2010年 / 42卷 / 01期

关键词：

Nowcasting; Neural Networks; Precipitation; Geostationary satellite; Meteorological radar; RAINFALL ESTIMATION; PASSIVE MICROWAVE; NETWORKS; RETRIEVAL; FRAMEWORK; ENSEMBLE; RADAR;

D O I：

暂无

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

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.

引用

页码：91 / 115

页数：25

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