The Impact of Satellite-derived Winds on Numerical Hurricane Track Forecasting

Christopher S. Velden University of Wisconsin, Cooperative Institute for Meteorological Satellite Studies, Madison, Wisconsin

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Christopher M. Hayden NOAA, National Environmental Satellite Data and Information Service, Madison, Wisconsin

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W. Paul Menzel NOAA, National Environmental Satellite Data and Information Service, Madison, Wisconsin

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James L. Franklin NOAA/AOML, Hurricane Research Division, Miami, Florida

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James S. Lynch NOAA, National Environmental Satellite Data and Information Service, Washington, D.C.

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Abstract

While qualitative information from meteorological satellites has long been recognized as critical for monitoring tropical cyclone activity, quantitative data are required to improve the objective analysis and numerical weather prediction of these events. In this paper, results are presented that show that the inclusion of high-density, multispectral, satellite-derived information into the analysis of tropical cyclone environmental wind fields can effectively reduce the error of objective track forecasts. Two independent analysis and barotropic track-forecast systems are utilized in order to examine the consistency of the results. Both systems yield a 10%–23% reduction in middle- to long-range track-forecast errors with the inclusion of the satellite wind observations.

Abstract

While qualitative information from meteorological satellites has long been recognized as critical for monitoring tropical cyclone activity, quantitative data are required to improve the objective analysis and numerical weather prediction of these events. In this paper, results are presented that show that the inclusion of high-density, multispectral, satellite-derived information into the analysis of tropical cyclone environmental wind fields can effectively reduce the error of objective track forecasts. Two independent analysis and barotropic track-forecast systems are utilized in order to examine the consistency of the results. Both systems yield a 10%–23% reduction in middle- to long-range track-forecast errors with the inclusion of the satellite wind observations.

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