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Impact of Four-Dimensional Variational Data Assimilation of Atmospheric Motion Vectors on Tropical Cyclone Track Forecasts

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  • 1 Shanghai Typhoon Institute, and Laboratory of Typhoon Forecast Technique/CMA, Shanghai, China
  • | 2 Shanghai Typhoon Institute, Shanghai, and Department of Physics and Materials Science, City University of Hong Kong, Hong Kong, China
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Abstract

The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

Corresponding author address: Dongliang Wang, Shanghai Typhoon Institute, 166 Puxi Rd., Shanghai 200030, China. Email: wangdl@mail.typhoon.gov.cn

Abstract

The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

Corresponding author address: Dongliang Wang, Shanghai Typhoon Institute, 166 Puxi Rd., Shanghai 200030, China. Email: wangdl@mail.typhoon.gov.cn

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