High-Latitude Atmospheric Motion Vectors from Composite Satellite Data

Atmospheric motion vectors (AMVs) are derived from satellite-observed motions of clouds and water vapor features. They provide crucial information in regions void of conventional observations and contribute to forecaster diagnostics of meteorological conditions, as well as numerical weather predicti...

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Bibliographic Details
Published inJournal of applied meteorology and climatology Vol. 53; no. 2; pp. 534 - 547
Main Authors Lazzara, Matthew A., Dworak, Richard, Santek, David A., Hoover, Brett T., Velden, Christopher S., Key, Jeffrey R.
Format Journal Article
LanguageEnglish
Published Boston American Meteorological Society 01.02.2014
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Summary:Atmospheric motion vectors (AMVs) are derived from satellite-observed motions of clouds and water vapor features. They provide crucial information in regions void of conventional observations and contribute to forecaster diagnostics of meteorological conditions, as well as numerical weather prediction. AMVs derived from geostationary (GEO) satellite observations over the middle latitudes and tropics have been utilized operationally since the 1980s; AMVs over the polar regions derived from low-earth (polar)-orbiting (LEO) satellites have been utilized since the early 2000s. There still exists a gap in AMV coverage between these two sources in the latitude band poleward of 60° and equatorward of 70° (both hemispheres). To address this AMV gap, the use of a novel approach to create image sequences that consist of composites derived from a combination of LEO and GEO observations that extend into the deep middle latitudes is explored. Experiments are performed to determine whether the satellite composite images can be employed to generate AMVs over the gap regions. The derived AMVs are validated over both the Southern Ocean/Antarctic and the Arctic gap regions over a multiyear period using rawinsonde wind observations. In addition, a two-season numerical model impact study using the Global Forecast System indicates that the assimilation of these AMVs can improve upon the control (operational) forecasts, particularly during lower-skill (dropout) events.
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ISSN:1558-8424
1558-8432
DOI:10.1175/jamc-d-13-0160.1