Documentation of a Systematic Bias in the Aviation Model's Forecast of the Atlantic Tropical Upper-Tropospheric Trough: Implications for Tropical Cyclone Forecasting

  • Fitzpatrick P
  • Knaff J
  • Landsea C
  • et al.
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Abstract

This study uncovers what appears to be a systematic bias in the National Meteorological Center's aviation (AVN) model at 200 mb over the Caribbean Sea. In general, the 48-h forecast in the vicinity of the Tropical Upper Tropospheric Trough (TUTT) underpredicts the magnitude of the westerly 200-mb winds on the order of 5-10 m s−1. This unrealistic weakening of the TUTT and associated cold lows by the AVN results in erroneous values of the vertical (850-200 mb) wind shear. These systematic errors are in the same order of magnitude as the minimum shear threshold for tropical cyclone genesis and development. Thus, 48-h tropical cyclone formation and intensity forecasts based upon the AVN model are often incorrect in the vicinity of the TUTT. Knowing the correct future upper-wind regime is also crucial for track forecasting of more intense tropical cyclones, especially in cases of recurvature. It is shown that simple persistence or climatology of the 200-mb winds south of a TUTT axis is superior to the AVN model's 48-h forecast. Until this bias in the AVN is successfully removed, the tropical cyclone forecaster for the Atlantic basin should be aware of this systematic error and make subjective changes in his/her forecasts. For 200-mb west winds greater than or equal to 10 m s−1, forecasts based on persistence are best, while for west winds less than 10 m s−1, half climatology and half persistence is the preferable predictor. If the TUTT is weak such that 200-mb easterly winds occur, climatology tends to be the best predictor as it nudges the forecast back to a normal westerly wind regime.

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APA

Fitzpatrick, P. J., Knaff, J. A., Landsea, C. W., & Finley, S. V. (1995). Documentation of a Systematic Bias in the Aviation Model’s Forecast of the Atlantic Tropical Upper-Tropospheric Trough: Implications for Tropical Cyclone Forecasting. Weather and Forecasting, 10(2), 433–446. https://doi.org/10.1175/1520-0434(1995)010<0433:doasbi>2.0.co;2

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