EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experiment

This paper presents a new rainfall estimation method, EPSAT-SG which is a frame for method design. The first implementation has been carried out to meet the requirement of the AMMA database on a West African domain. The rainfall estimation relies on two intermediate products: a rainfall probabil...

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Main Authors: J. C. Bergès, I. Jobard, F. Chopin, R. Roca
Format: Article
Language:English
Published: Copernicus Publications 2010-01-01
Series:Annales Geophysicae
Online Access:https://www.ann-geophys.net/28/289/2010/angeo-28-289-2010.pdf
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spelling doaj-5c5d76a47a0b4d859ea924332b8343032020-11-24T20:49:00ZengCopernicus PublicationsAnnales Geophysicae0992-76891432-05762010-01-012828930810.5194/angeo-28-289-2010EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experimentJ. C. Bergès0I. Jobard1I. Jobard2F. Chopin3R. Roca4PRODIG, Université Paris 1, 75005 Paris, FranceLMD, IPSL/CNRS, Ecole Polytechnique, 91128 Palaiseau, FranceUniv. Paris-Sud, 92296 Chatenay, FranceLMD, IPSL/CNRS, Ecole Polytechnique, 91128 Palaiseau, FranceLMD, IPSL/CNRS, Ecole Polytechnique, 91128 Palaiseau, FranceThis paper presents a new rainfall estimation method, EPSAT-SG which is a frame for method design. The first implementation has been carried out to meet the requirement of the AMMA database on a West African domain. The rainfall estimation relies on two intermediate products: a rainfall probability and a rainfall potential intensity. The first one is computed from MSG/SEVIRI by a feed forward neural network. First evaluation results show better properties than direct precipitation intensity assessment by geostationary satellite infra-red sensors. The second product can be interpreted as a conditional rainfall intensity and, in the described implementation, it is extracted from GPCP-1dd. Various implementation options are discussed and comparison of this embedded product with 3B42 estimates demonstrates the importance of properly managing the temporal discontinuity. The resulting accumulated rainfall field can be presented as a GPCP downscaling. A validation based on ground data supplied by AGRHYMET (Niamey) indicates that the estimation error has been reduced in this process. The described method could be easily adapted to other geographical area and operational environment.https://www.ann-geophys.net/28/289/2010/angeo-28-289-2010.pdf
collection DOAJ
language English
format Article
sources DOAJ
author J. C. Bergès
I. Jobard
I. Jobard
F. Chopin
R. Roca
spellingShingle J. C. Bergès
I. Jobard
I. Jobard
F. Chopin
R. Roca
EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experiment
Annales Geophysicae
author_facet J. C. Bergès
I. Jobard
I. Jobard
F. Chopin
R. Roca
author_sort J. C. Bergès
title EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experiment
title_short EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experiment
title_full EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experiment
title_fullStr EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experiment
title_full_unstemmed EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experiment
title_sort epsat-sg: a satellite method for precipitation estimation; its concepts and implementation for the amma experiment
publisher Copernicus Publications
series Annales Geophysicae
issn 0992-7689
1432-0576
publishDate 2010-01-01
description This paper presents a new rainfall estimation method, EPSAT-SG which is a frame for method design. The first implementation has been carried out to meet the requirement of the AMMA database on a West African domain. The rainfall estimation relies on two intermediate products: a rainfall probability and a rainfall potential intensity. The first one is computed from MSG/SEVIRI by a feed forward neural network. First evaluation results show better properties than direct precipitation intensity assessment by geostationary satellite infra-red sensors. The second product can be interpreted as a conditional rainfall intensity and, in the described implementation, it is extracted from GPCP-1dd. Various implementation options are discussed and comparison of this embedded product with 3B42 estimates demonstrates the importance of properly managing the temporal discontinuity. The resulting accumulated rainfall field can be presented as a GPCP downscaling. A validation based on ground data supplied by AGRHYMET (Niamey) indicates that the estimation error has been reduced in this process. The described method could be easily adapted to other geographical area and operational environment.
url https://www.ann-geophys.net/28/289/2010/angeo-28-289-2010.pdf
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