Abstract

A series of experimental forecasts are performed to evaluate the impact of enhanced satellite-derived winds on numerical hurricane track predictions. The winds are derived from Geostationary Operational Environmental Satellite-8 (GOES-8) multispectral radiance observations by tracking cloud and water vapor patterns from successive satellite images. A three-dimensional optimum interpolation method is developed to assimilate the satellite winds directly into the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane prediction system. A series of parallel forecasts are then performed, both with and without the assimilation of GOES winds. Except for the assimilation of the satellite winds, the model integrations are identical in all other respects. A strength of this study is the large number of experiments performed. Over 100 cases are examined from 11 different storms covering three seasons (1996–98), enabling the authors to account for and examine the case-to-case variability in the forecast results when performing the assessment. On average, assimilation of the GOES winds leads to statistically significant improvements for all forecast periods, with the relative reductions in track error ranging from ∼5% at 12 h to ∼12% at 36 h. The percentage of improved forecasts increases following the assimilation of the satellite winds, with roughly three improved forecasts for every two degraded ones. Inclusion of the satellite winds also dramatically reduces the westward bias that has been a persistent feature of the GFDL model forecasts, implying that much of this bias may be related to errors in the initial conditions rather than a deficiency in the model itself. Finally, a composite analysis of the deep-layer flow fields suggests that the reduction in track error may be associated with the ability of the GOES winds to more accurately depict the strength of vorticity gyres in the environmental flow. These results offer compelling evidence that the assimilation of satellite winds can significantly improve the accuracy of hurricane track forecasts.

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