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 skill-ful 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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“…Therefore, a combination of big data and machine learning (ML) offers a promising alternative method for untangling those complex relationships between environment forcings and TC activities. Previous studies have demonstrated that ML has robust predictive capabilities in TC genesis, intensity, precipitation, and rapid intensification (14,(25)(26)(27). While current ML research on TCs primarily centers on enhancing forecasting and prediction capabilities, ML models also have the potential to unveil intricate and nonlinear relationships between features and response variables.…”