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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Series: | Annales Geophysicae |
Online Access: | https://www.ann-geophys.net/28/289/2010/angeo-28-289-2010.pdf |
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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 |
work_keys_str_mv |
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