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• W2043613231 abstract "Abstract In this study, sensitivities of the track and intensity forecasts of Typhoon Megi (2010) to the Cooperative Institute for Meteorological Satellite Studies (CIMSS) University of Wisconsin satellite atmospheric motion vector (AMV) dataset are examined. Assimilation of the CIMSS AMV dataset using the local ensemble transform Kalman filter implemented in the Weather Research and Forecasting model shows that the AMV data can significantly improve the track forecast of Typhoon Megi, especially the sharp turn from west-northwest to north after crossing the Philippines. By broadening the western Pacific subtropical high to the west, the AMV data can help reduce the eastward bias of the track, thus steering the storm away inimical shear environment and facilitating its subsequent development. Further sensitivity experiments with separated assimilation of the low- to midlevel (800–300 hPa) and upper-level (300–100 hPa) AMV winds reveal that, despite the sparse distribution of the low-level AMV winds with most of the data points located in the periphery of Megi’s main circulation, the track forecasts tend to be more sensitive to the low-level than to the upper-level wind observations. This indicates that the far-field low-level observations can improve the large-scale environmental flow that storms are to move in, giving rise to a better representation of the steering flow and subsequent intensity change. While much of the recent effort in tropical cyclone research focuses on inner-core observations to improve the intensity forecast, the results in this study show that the peripheral observations outside the storm center could contribute considerably to the intensity and track forecasts and deserve attention for better typhoon forecast skills." @default.
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• W2043613231 date "2012-12-01" @default.
• W2043613231 modified "2023-10-10" @default.
• W2043613231 title "Sensitivity of the Track and Intensity Forecasts of Typhoon Megi (2010) to Satellite-Derived Atmospheric Motion Vectors with the Ensemble Kalman Filter" @default.
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• W2043613231 doi "https://doi.org/10.1175/jtech-d-12-00020.1" @default.
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