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@Article{SantekDNWBGGNCNSOLCDB:2019:20AtMo,
               author = "Santek, David and Dworak, Richard and Nebuda, Sharon and Wanzong, 
                         Steve and Borde, R{\'e}gis and Genkova, Iliana and 
                         Garc{\'{\i}}a-Pereda, Javier and Negri, Renato Galante and 
                         Carranza, Manuel and Nonaka, Kenichi and Shimoji, Kazuki and Oh, 
                         Soo Min and Lee, Byung-Il and Chung, Sung-Rae and Daniels, Jaime 
                         and Bresky, Wayne",
          affiliation = "{University of Wisconsin-Madison} and {University of 
                         Wisconsin-Madison} and {University of Wisconsin-Madison} and 
                         {University of Wisconsin-Madison} and EUMETSAT and {National 
                         Oceanic and Atmospheric Administration (NOAA)} and {Agencia 
                         Estatal de Meteorolog{\'{\i}}a (AEMET)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and EUMETSAT and {Japan 
                         Meteorological Agency} and {Japan Meteorological Agency} and 
                         {Seoul National University} and {Korea Meteorological 
                         Administration (KMA)} and {Korea Meteorological Administration 
                         (KMA)} and {National Oceanic and Atmospheric Administration 
                         (NOAA)} and {National Oceanic and Atmospheric Administration 
                         (NOAA)}",
                title = "2018 Atmospheric Motion Vector (AMV) intercomparison study",
              journal = "Remote Sensing",
                 year = "2019",
               volume = "11",
               number = "19",
                month = "Oct.",
             keywords = "Atmospheric Motion Vectors (AMVs), Intercomparison, Himawari, 
                         CPTEC, INPE, EUMETSAT, JMA, KMA, NOAA, NWCSAF.",
             abstract = "Atmospheric 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'.",
                  doi = "10.3390/rs11192240",
                  url = "http://dx.doi.org/10.3390/rs11192240",
                 issn = "2072-4292",
             language = "en",
           targetfile = "santek_2018.pdf",
        urlaccessdate = "19 abr. 2021"
}


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