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dc.rights.licenseopenen_US
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorLALANDE, Jean-Marie
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorBOURMAUD, Guillaume
IDREF: 191217530
dc.contributor.authorMINVIELLE, Pierre
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorGIOVANNELLI, Jean-François
dc.date.accessioned2022-08-29T12:20:02Z
dc.date.available2022-08-29T12:20:02Z
dc.date.issued2022-08-02
dc.identifier.issn1867-1381en_US
dc.identifier.otherhttp://www.cloudsat.cira.colostate.edu/en_US
dc.identifier.urioai:crossref.org:10.5194/amt-15-4411-2022
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/140605
dc.description.abstractEnSpatiotemporal statistical learning has received increased attention in the past decade, due to spatially and temporally indexed data proliferation, especially data collected from satellite remote sensing. In the meantime, observational studies of clouds are recognized as an important step toward improving cloud representation in weather and climate models. Since 2006, the satellite CloudSat of NASA is carrying a 94 GHz cloud-profiling radar and is able to retrieve, from radar reflectivity, microphysical parameter distribution such as water or ice content. The collected data are piled up with the successive satellite orbits of nearly 2 h, leading to a large compressed database of 2 Tb (http://cloudsat.atmos.colostate.edu/, last access: 8 June 2022). These observations offer the opportunity to extend the cloud microphysical properties beyond the actual measurement locations using an interpolation and prediction algorithm. To do so, we introduce a statistical estimator based on the spatiotemporal covariance and mean of the observations known as kriging. An adequate parametric model for the covariance and the mean is chosen from an exploratory data analysis. Beforehand, it is necessary to estimate the parameters of this spatiotemporal model; this is performed in a Bayesian setting. The approach is then applied to a subset of the CloudSat dataset.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.sourcecrossref
dc.title.enA kriging-based analysis of cloud liquid water content using CloudSat data
dc.typeArticle de revueen_US
dc.identifier.doi10.5194/amt-15-4411-2022en_US
dc.subject.halSciences de l'ingénieur [physics]/Milieux fluides et réactifsen_US
bordeaux.journalAtmospheric Measurement Techniquesen_US
bordeaux.page4411-4429en_US
bordeaux.volume15en_US
bordeaux.hal.laboratoriesLaboratoire d’Intégration du Matériau au Système (IMS) - UMR 5218en_US
bordeaux.issue15en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionCNRSen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcedissemin
hal.identifierhal-03763346
hal.version1
hal.date.transferred2022-08-29T12:20:08Z
hal.exporttrue
workflow.import.sourcedissemin
dc.rights.ccCC BYen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Atmospheric%20Measurement%20Techniques&rft.date=2022-08-02&rft.volume=15&rft.issue=15&rft.spage=4411-4429&rft.epage=4411-4429&rft.eissn=1867-1381&rft.issn=1867-1381&rft.au=LALANDE,%20Jean-Marie&BOURMAUD,%20Guillaume&MINVIELLE,%20Pierre&GIOVANNELLI,%20Jean-Fran%C3%A7ois&rft.genre=article


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