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Open AccessArticle

2018 Atmospheric Motion Vector (AMV) Intercomparison Study

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Space Science and Engineering Center (SSEC), University of Wisconsin-Madison, 1225 West Dayton Street, Madison, WI 53706, USA
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EUMETSAT, Eumetsat Allee 1, 64295 Darmstadt, Germany
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I.M. Systems Group at National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), 5830 University Research Court, College Park, MD 20740, USA
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Satellite Application Facility on support to Nowcasting and Very short range forecasting (NWCSAF), Agencia Estatal de Meteorología (AEMET), Leonardo Prieto Castro 8, 28040 Madrid, Spain
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Center for Weather Prediction and Climate Studies (CPTEC), National Institute for Space Research (INPE), Cachoeira Paulista, São Paulo 12630-000, Brazil
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GMV INSYEN at EUMETSAT, Eumetsat Allee 1, 64295 Darmstadt, Germany
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Japan Meteorological Agency, 1-3-4 Otemachi, Chiyoda-ku, Tokyo 100-8122, Japan
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Department of Physics and Astronomy, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
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National Meteorological Satellite Center (NMSC), Korea Meteorological Administration (KMA), 64-18, Guam-gil, Gwanghyewon-myeon, Jincheon-gun, Chungcheongbuk-do 27803, Korea
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Center for Satellite Applications and Research (STAR), National Environmental Satellite, Data and Information Service (NESDIS), National Oceanic and Atmospheric Administration (NOAA), 5830 University Research Court, College Park, MD 20740, USA
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I.M. Systems Group at Center for Satellite Applications and Research (STAR), National Environmental Satellite, Data and Information Service (NESDIS), National Oceanic and Atmospheric Administration (NOAA), 5830 University Research Court, College Park, MD 20740, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2240; https://doi.org/10.3390/rs11192240
Received: 31 July 2019 / Revised: 18 September 2019 / Accepted: 20 September 2019 / Published: 26 September 2019
(This article belongs to the Special Issue Satellite-Derived Wind Observations)
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’. View Full-Text
Keywords: Atmospheric Motion Vectors (AMVs); Intercomparison; Himawari; CPTEC/INPE; EUMETSAT; JMA; KMA; NOAA; NWCSAF Atmospheric Motion Vectors (AMVs); Intercomparison; Himawari; CPTEC/INPE; EUMETSAT; JMA; KMA; NOAA; NWCSAF
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Santek, D.; Dworak, R.; Nebuda, S.; Wanzong, S.; Borde, R.; Genkova, I.; García-Pereda, J.; Galante Negri, R.; Carranza, M.; Nonaka, K.; Shimoji, K.; Oh, S.M.; Lee, B.-I.; Chung, S.-R.; Daniels, J.; Bresky, W. 2018 Atmospheric Motion Vector (AMV) Intercomparison Study. Remote Sens. 2019, 11, 2240.

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