Machine Learning–Based Hurricane Wind Reconstruction

Author:

Yang Qidong12,Lee Chia-Ying3,Tippett Michael K.1,Chavas Daniel R.4,Knutson Thomas R.5

Affiliation:

1. a Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York

2. b Courant Institute of Mathematical Sciences, New York University, New York, New York

3. c Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

4. d Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette, Indiana

5. e NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Abstract

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.

Funder

National Science Foundation

Publisher

American Meteorological Society

Subject

Atmospheric Science

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