NDLI: Rainfall Nowcasting From Multisatellite Passive-Sensor Images Using a Recurrent Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Marzano, F.S. Rivolta, G. Coppola, E. Tomassetti, B. Verdecchia, M.
Copyright Year	1980
Abstract	The term now cast in hydro meteorology reflects the need for timely and accurate predictions of risky environmental situations, which are related to the development of severe meteorological events at short time scales. The objective of this paper is to apply a fully neural-network approach to the rainfall field now casting from infrared (IR) and microwave (MW) passive-sensor imagery aboard, respectively, geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellites. The multisatellite space-time prediction procedure, which is named Neural Combined Algorithm for Storm Tracking (NeuCAST), consists of two consecutive steps. First, the IR radiance field measured from a geostationary satellite radiometer (e.g., Meteosat) is projected ahead in time (e.g., 30 min); second, the projected radiance field is used in estimating the rainfall field by means of an MW-IR combined rain retrieval algorithm exploiting GEO-LEO observations. The NeuCAST methodology is extensively illustrated and discussed in this paper. Its accuracy is quantified by means of quantitative error indexes, which are evaluated on selected case studies of rainfall events in Southern Europe in 2003 and 2005.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	3800
Ending Page	3812
Page Count	13
File Size	1534646
File Format	PDF
ISSN	01962892
Volume Number	45
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Low earth orbit satellites Meteorology Satellite broadcasting Casting Infrared imaging Storms Extraterrestrial measurements Time measurement Microwave radiometry satellite remote sensing Neural network (NN) nowcasting methods passive sensors precipitation retrieval
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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