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Stochastic 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7211 - 7223 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] Suisse Résumé : (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 [article]

Assessment of renewable energy resources with remote sensing / Fernando Ramos Martins (2021)  

Public Titre : Assessment of renewable energy resources with remote sensing Type de document : Monographie Auteurs : Fernando Ramos Martins, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 244 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-3-0365-0481-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] climat [Termes IGN] détection des nuages [Termes IGN] données lidar [Termes IGN] énergie éolienne [Termes IGN] énergie géothermique [Termes IGN] énergie renouvelable [Termes IGN] énergie solaire [Termes IGN] Extreme Gradient Machine [Termes IGN] hydroélectricité [Termes IGN] image GOES [Termes IGN] Matlab [Termes IGN] prévision météorologique [Termes IGN] rayonnement solaire [Termes IGN] réseau neuronal artificiel [Termes IGN] semis de points Résumé : (éditeur) The book “Assessment of Renewable Energy Resources with Remote Sensing" focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques. The eleven papers inside the book provide an overview of remote sensing applications on hydro, solar, wind and geothermal energy resources and their major goal is to provide state of art knowledge to contribute with the renewable energy resource deployment, especially in regions where energy demand is rapidly expanding. Renewable energy resources have an intrinsic relationship with local environmental features and the regional climate. Even small and fast environment and/or climate changes can cause significant variability in power generation at different time and space scales. Methodologies based on remote sensing are the primary source of information for the development of numerical models that aim to support the planning and operation of an electric system with a substantial contribution of intermittent energy sources. In addition, reliable data and knowledge on renewable energy resource assessment are fundamental to ensure sustainable expansion considering environmental, financial and energetic security. Note de contenu : 1- Enhancement of cloudless skies frequency over a large tropical reservoir in Brazil 2- On the land-sea contrast in the surface solar radiation (SSR) in the Baltic region 3- Real-time automatic cloud detection using a low-cost sky camera 4- Attenuation factor estimation of direct normal irradiance combining sky camera images and mathematical models in an inter-tropical area 5- Multistep-ahead solar radiation forecasting scheme based on the light gradient boosting machine: A case study of Jeju Island 6- Modified search strategies assisted crossover whale optimization algorithm with selection operator for parameter extraction of solar photovoltaic models 7- Industry experience of developing day-ahead photovoltaic plant forecasting system based on machine learning 8- The global wind resource observed by scatterometer 9- Coastal wind measurements using a single scanning LiDAR 10- Characterizing geological heterogeneities for geothermal purposes through combined geophysical prospecting methods 11- A computational workflow for generating a voxel-based design approach based on subtractive shading envelopes and attribute information of point cloud data Numéro de notice : 28653 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0481-0 En ligne : https://doi.org/10.3390/books978-3-0365-0481-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99795

Statistical data fusion of multi-sensor AOD over the Continental United States / Sweta Jinnagara Puttaswamy in Geocarto international, vol 29 n° 1 - 2 (February - April 2014)  

Public [article]  inGeocarto international > vol 29 n° 1 - 2 (February - April 2014) . - pp 48 - 64 Titre : Statistical data fusion of multi-sensor AOD over the Continental United States Type de document : Article/Communication Auteurs : Sweta Jinnagara Puttaswamy, Auteur ; Hai M. Nguyen, Auteur ; Amy Braverman, Auteur ; Xuefei Hu, Auteur ; Yang Liu, Auteur Année de publication : 2014 Article en page(s) : pp 48 - 64 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] aérosol [Termes IGN] données de terrain [Termes IGN] Etats-Unis [Termes IGN] fusion de données [Termes IGN] image GOES [Termes IGN] image Terra-MODIS [Termes IGN] interpolation linéaire [Termes IGN] krigeage [Termes IGN] profondeur Résumé : (Auteur) This article illustrates two techniques for merging daily aerosol optical depth (AOD) measurements from satellite and ground-based data sources to achieve optimal data quality and spatial coverage. The first technique is a traditional Universal Kriging (UK) approach employed to predict AOD from multi-sensor aerosol products that are aggregated on a reference grid with AERONET as ground truth. The second technique is spatial statistical data fusion (SSDF); a method designed for massive satellite data interpolation. Traditional kriging has computational complexity O(N3), making it impractical for large datasets. Our version of UK accommodates massive data inputs by performing kriging locally, while SSDF accommodates massive data inputs by modelling their covariance structure with a low-rank linear model. In this study, we use aerosol data products from two satellite instruments: the moderate resolution imaging spectrometer and the geostationary operational environmental satellite, covering the Continental United States. Numéro de notice : A2014-234 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2013.827750 Date de publication en ligne : 10/09/2013 En ligne : https://doi.org/10.1080/10106049.2013.827750 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33137 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2014011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

GSICS inter-calibration of infrared channels of geostationary imagers using Metop-IASI / Tim J. Hewison in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 1 (March 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 3 Tome 1 (March 2013) . - pp 1160 - 1170 Titre : GSICS inter-calibration of infrared channels of geostationary imagers using Metop-IASI Type de document : Article/Communication Auteurs : Tim J. Hewison, Auteur ; Xiangqian Wu, Auteur ; Fangfang Yu, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 1160 - 1170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] erreur systématique [Termes IGN] étalonnage relatif [Termes IGN] image Feng-Yun [Termes IGN] image GOES [Termes IGN] image hyperspectrale [Termes IGN] image Météosat [Termes IGN] image MetOp-IASI [Termes IGN] image thermique [Termes IGN] rayonnement infrarouge thermique [Termes IGN] régression linéaire Résumé : (Auteur) The first products of the Global Space-based Inter-Calibration System (GSICS) include bias monitoring and calibration corrections for the thermal infrared (IR) channels of current meteorological sensors on geostationary satellites. These use the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) on the low Earth orbit (LEO) Metop satellite as a common cross-calibration reference. This paper describes the algorithm, which uses a weighted linear regression, to compare collocated radiances observed from each pair of geostationary-LEO instruments. The regression coefficients define the GSICS Correction, and their uncertainties provide quality indicators, ensuring traceability to the selected community reference, IASI. Examples are given for the Meteosat, GOES, MTSAT, Fengyun-2, and COMS imagers. Some channels of these instruments show biases that vary with time due to variations in the thermal environment, stray light, and optical contamination. These results demonstrate how inter-calibration can be a powerful tool to monitor and correct biases, and help diagnose their root causes. Numéro de notice : A2013-123 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2238544 En ligne : https://doi.org/10.1109/TGRS.2013.2238544 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32261 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013031A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Imagery on call: Europe leads the way / Anonyme in GEO: Geoconnexion international, vol 11 n° 4 (april 2012)

Public [article]  inGEO: Geoconnexion international > vol 11 n° 4 (april 2012) . - pp 24 - 25 Titre : Imagery on call: Europe leads the way Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2012 Article en page(s) : pp 24 - 25 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] Europe (géographie politique) [Termes IGN] image GOES [Termes IGN] image satellite [Termes IGN] protection civile [Termes IGN] risque environnemental [Termes IGN] risque naturel [Termes IGN] Union Européenne Résumé : (Auteur) Imagine a fully-staffed, available 24/7 and ready to deliver satellite imagery within hours of an emergency. A consortium has recently been appointed as the European Union's exclusive provider of just such a service. Numéro de notice : A2012-115 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31563 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
062-2012041	SL	Revue	Centre de documentation	Revues en salle	Disponible

Satellite-derived cloud top pressure product validation using aircraft-based cloud physics Lidar from the ATReC field campaign / S.T. Bedka in International Journal of Remote Sensing IJRS, vol 28 n° 10 (May 2007)  

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Study of rain events over the South China Sea by synergistic use of multi-sensor satellite and ground-based meteorological data / W. Alpers in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 3 (March 2007)  

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Bayesian-based subpixel brightness temperature estimation from multichannel infrared GOES radiometer data / S. Cain in IEEE Transactions on geoscience and remote sensing, vol 42 n° 1 (January 2004)  

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Destriping goes images by matching empirical distribution functions / M.P. Weinreb in Remote sensing of environment, vol 29 n° 2 (01/08/1989)  

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Calibration of NOAA-7 AVHRR, GOES-5 and GOES-6 VISSR-VAS solar channels / R. Frouin in Remote sensing of environment, vol 22 n° 1 (01/06/1987)  

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Comparison of HCMM and goes satellite temperatures and evaluation of surface statistics / E. Chen in Remote sensing of environment, vol 21 n° 3 (01/04/1987)  

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Determining rainfall intensity and type from goes imagery in the midlatitudes / A.A. Tsonis in Remote sensing of environment, vol 21 n° 1 (01/02/1987)  

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