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Abstract The hazards induced by tropical cyclones (TCs), for example, high winds, extreme precipitation, and storm tides, are closely related to the TC surface wind field. Parametric models for TC surface wind distribution have been widely used for hazards and risk analysis due to their simplicity and efficiency in application. Here we revisit the parametric modeling of TC wind fields, including the symmetrical and asymmetrical components, and its applications in storm tide modeling in the North Atlantic. The asymmetrical wind field has been related to TC motion and vertical wind shear; however, we find that a simple and empirical background‐wind model, based solely on a rotation and scaling of the TC motion vector, can largely capture the observed surface wind asymmetry. The implicit inclusion of the wind shear effect can be understood with the climatological relationship between the general TC motion and wind shear directions during hurricane seasons. For the symmetric wind field, the widely used Holland wind profile is chosen as a benchmark model, and we find that a physics‐based complete wind profile model connecting the inner core and outer region performs superiorly compared to a wind analysis data set. When used as wind forcing for storm tide simulations, the physics‐based complete wind profile integrated with the background‐wind asymmetry model can reproduce the observed storm tides with lower errors than the often‐used Holland model coupled with a translation‐speed‐based method.
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Machine Learning–Based Hurricane Wind Reconstruction
Abstract Here we present a machine learning–based wind reconstruction model. The model reconstructs hurricane surface winds with XGBoost, which is a decision-tree-based ensemble predictive algorithm. The model treats the symmetric and asymmetric wind fields separately. The symmetric wind field is approximated by a parametric wind profile model and two Bessel function series. The asymmetric field, accounting for asymmetries induced by the storm and its ambient environment, is represented using a small number of Laplacian eigenfunctions. The coefficients associated with Bessel functions and eigenfunctions are predicted by XGBoost based on storm and environmental features taken from NHC best-track and ERA-Interim data, respectively. We use HWIND for the observed wind fields. Three parametric wind profile models are tested in the symmetric wind model. The wind reconstruction model’s performance is insensitive to the choice of the profile model because the Bessel function series correct biases of the parametric profiles. The mean square error of the reconstructed surface winds is smaller than the climatological variance, indicating skillful reconstruction. Storm center location, eyewall size, and translation speed play important roles in controlling the magnitude of the leading asymmetries, while the phase of the asymmetries is mainly affected by storm translation direction. Vertical wind shear impacts the asymmetry phase to a lesser degree. Intended applications of this model include assessing hurricane risk using synthetic storm event sets generated by statistical–dynamical downscaling hurricane models.
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- Award ID(s):
- 1945113
- PAR ID:
- 10397585
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Weather and Forecasting
- Volume:
- 37
- Issue:
- 4
- ISSN:
- 0882-8156
- Page Range / eLocation ID:
- p. 477-493
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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