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2018 Atmospheric Motion Vector (AMV): intercomparison study
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dc.contributor.authorSantek, Davides_ES
dc.contributor.authorDworak, Richardes_ES
dc.contributor.authorNebuda, Sharones_ES
dc.contributor.authorWanzong, Stevees_ES
dc.contributor.authorBorde, Régises_ES
dc.contributor.authorGenkova, Ilianaes_ES
dc.contributor.authorGarcía Pereda, Javieres_ES
dc.contributor.authorGalante Negri, Renatoes_ES
dc.contributor.authorCarranza, Manueles_ES
dc.contributor.authorNonaka, Kenichies_ES
dc.contributor.authorShimoji, Kazukies_ES
dc.contributor.authorOh, Soo Mines_ES
dc.contributor.authorLee, Byung-Iles_ES
dc.contributor.authorChung, Sung-Raees_ES
dc.contributor.authorDaniels, Jaimees_ES
dc.contributor.authorBresky, Waynees_ES
dc.date.accessioned2020-04-27T11:29:16Z-
dc.date.available2020-04-27T11:29:16Z-
dc.date.issued2019-
dc.identifier.citationRemote Sensing. 2019, 11(19), 2240es_ES
dc.identifier.issn2072-4292-
dc.identifier.urihttp://hdl.handle.net/20.500.11765/11686-
dc.description.abstractAtmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.es_ES
dc.description.sponsorshipThe “Space Science and Engineering Center” (SSEC) of the “University ofWisconsin-Madison” (UW) was funded to do this research by the “European Organization for the Exploitation of Meteorological Satellites (EUMETSAT)”, through the “Satellite Application Facility on Support to Nowcasting and Very short range forecasting (NWCSAF)” “Visiting Scientist Activity (VSA)” program.es_ES
dc.language.isoenges_ES
dc.publisherMultidisciplinary Digital Publishing Institutees_ES
dc.rightsLicencia CC: Reconocimiento CC BYes_ES
dc.subjectAtmospheric Motion Vectors (AMVs)es_ES
dc.subjectIntercomparisones_ES
dc.subjectHimawaries_ES
dc.subjectCPTEC/INPEes_ES
dc.subjectEUMETSATes_ES
dc.subjectJMAes_ES
dc.subjectKMAes_ES
dc.subjectNOAAes_ES
dc.subjectNWCSAFes_ES
dc.title2018 Atmospheric Motion Vector (AMV): intercomparison studyes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://dx.doi.org/10.3390/rs11192240es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
Colecciones: Artículos científicos 2019-2022


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