Review and Improvement of Tropical Cyclone Surface Wind Parametric Models Using SAR Imagery

Abstract Parametric wind models can be used to formulate the wind distribution under a tropical cyclone (TC) as a simple function with a reduced set of parameters. Here, a range of 10 symmetric parametric models are fitted to a dataset of 920 surface wind radial profiles derived from synthetic aperture radar (SAR) high-resolution observations. Multiple linear regressions are performed to express optimal parameters fitted on every SAR-observed profile as functions of TC azimuthal-mean maximum wind speed, radius of maximum winds, and latitude. These new parameterizations are thoroughly evaluated, in particular their ability to represent the variability of observed profiles over the different intensity categories, and throughout the radial extent of the TC. Among all models, the Loridan model is the most versatile and has the lowest average root-mean-square error (3.18 m s −1 ). It shows a balanced performance among all TC categories and a very good representation of the eyewall and inner core areas. The Rankine pioneer model holds its place among the best models, preferable for the far outer core. The application of the SAR-derived regressions to best track TC parameters is also discussed. A regression of the first-mode wind asymmetry model of Olfateh is provided, allowing the reconstruction of a complete asymmetric wind field. These new SAR-based regressions parameterizations provide users with a state-of-the-art set of parameters based on high-resolution surface observations. Significance Statement This work provides new estimates of regressed parameters for a range of 10 different parametric models of tropical cyclone (TC) symmetric wind fields and one asymmetric surface wind parametric model. Regressions are performed after fitting each model to a state-of-the-art dataset of 920 synthetic aperture radar (SAR) observations, as functions of SAR-estimated azimuthally averaged maximum wind, radius of maximum winds, latitude, and translation speed (for the asymmetric model). The model with the best performance across all categories and radii is the Loridan model. These results provide users with a uniform set of regressions to accurately formulate synthetic surface wind fields. They also expose in detail the strengths and weaknesses of these models.