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Auteur Alexis Berne |
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Affiner la recherche Interroger des sources externesStochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network / Jussi Leinonen in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)
Titre : Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network Type de document : Article/Communication Auteurs : Jussi Leinonen, Auteur ; Daniele Nerini, Auteur ; Alexis Berne, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7211 - 7223 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] données météorologiques
[Termes IGN] épaisseur de nuage
[Termes IGN] image à basse résolution
[Termes IGN] image GOES
[Termes IGN] modèle atmosphérique
[Termes IGN] précipitation
[Termes IGN] processus stochastique
[Termes IGN] réduction d'échelle
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal convolutif
[Termes IGN] SuisseRésumé : (auteur) Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as “downscaling” in the atmospheric sciences: improving the spatial resolution of low-resolution images. The ability of conditional GANs to generate an ensemble of solutions for a given input lends itself naturally to stochastic downscaling, but the stochastic nature of GANs is not usually considered in super-resolution applications. Here, we introduce a recurrent, stochastic super-resolution GAN that can generate ensembles of time-evolving high-resolution atmospheric fields for an input consisting of a low-resolution sequence of images of the same field. We test the GAN using two data sets: one consisting of radar-measured precipitation from Switzerland; the other of cloud optical thickness derived from the Geostationary Earth Observing Satellite 16 (GOES-16). We find that the GAN can generate realistic, temporally consistent super-resolution sequences for both data sets. The statistical properties of the generated ensemble are analyzed using rank statistics, a method adapted from ensemble weather forecasting; these analyses indicate that the GAN produces close to the correct amount of variability in its outputs. As the GAN generator is fully convolutional, it can be applied after training to input images larger than the images used to train it. It is also able to generate time series much longer than the training sequences, as demonstrated by applying the generator to a three-month data set of the precipitation radar data. The source code to our GAN is available at https://github.com/jleinonen/downscaling-rnn-gan. Numéro de notice : A2021-645 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3032790 Date de publication en ligne : 02/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3032790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98349
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7211 - 7223[article]
Titre : HyMeX-SOP1, the field campaign dedicated to heavy precipitation and flash flooding in the northwestern Mediterranean Type de document : Article/Communication Auteurs : Véronique Ducrocq, Auteur ; Isabelle Braud, Auteur ; Silvio Davolio, Auteur ; Rossella Ferretti, Auteur ; Cyrille Flamant, Auteur ; Agustin Jansa, Auteur ; Norbert Kalthoff, Auteur ; Evelyne Richard, Auteur ; Isabelle Taupier-Letage, Auteur ; Pierre-Alain Ayral, Auteur ; Sophie Belamari, Auteur ; Alexis Berne, Auteur ; Marco Borga, Auteur ; Brice Boudevillain, Auteur ; Olivier Bock Année de publication : 2014 Projets : HyMeX / Richard, Evelyne Article en page(s) : pp 1083 - 1100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] campagne d'observations
[Termes IGN] inondation
[Termes IGN] littoral méditerranéen
[Termes IGN] précipitationRésumé : (auteur) The Mediterranean region is frequently affected by heavy precipitation events associated with flash floods, landslides, and mudslides that cause hundreds of millions of euros in damages per year and, often, casualties. A major field campaign was devoted to heavy precipitation and f lash f loods from 5 September to 6 November 2012 within the framework of the 10-yr international Hydrological Cycle in the Mediterranean Experiment (HyMeX) dedicated to the hydrological cycle and related high-impact events. The 2-month field campaign took place over the northwestern Mediterranean Sea and its surrounding coastal regions in France, Italy, and Spain. The observation strategy of the field experiment was devised to improve knowledge of the following key components leading to heavy precipitation and flash flooding in the region: 1) the marine atmospheric f lows that transport moist and conditionally unstable air toward the coasts, 2) the Mediterranean Sea acting as a moisture and energy source, 3) the dynamics and microphysics of the convective systems producing heavy precipitation, and 4) the hydrological processes during flash floods. This article provides the rationale for developing this first HyMeX field experiment and an overview of its design and execution. Highlights of some intensive observation periods illustrate the potential of the unique datasets collected for process understanding, model improvement, and data assimilation. Numéro de notice : A2014-656 Affiliation des auteurs : LASTIG LAREG+Ext (2012-mi2018) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1175/BAMS-D-12-00244.1 Date de publication en ligne : 22/08/2014 En ligne : http://dx.doi.org/10.1175/BAMS-D-12-00244.1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78522
in Bulletin of the American Meteorological Society > vol 95 n° 7 (July 2014) . - pp 1083 - 1100[article]Documents numériques
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HyMeX-SOP1, the field campaignAdobe Acrobat PDF
