Deep Learning Parameterization of the Tropical Cyclone Boundary Layer

Abstract

AbstractThe research on tropical cyclone (TC) relieson numerical models and simulations, with the currently widely used boundary layer parameterization posing a significant challenge on accurately predicting turbulent mixing. However, machine learning has opened up new possibilities for boundary layer sub‐grid process parameterization. In this study, a deep‐learning parameterization scheme for the TC boundary layer (DeepBL) on predicting turbulent flux is proposed. DeepBL comprises a one‐dimensional convolutional structure that relies on a small number of learnable parameters that accomplishes an error reduction compared to the standard fully connected neural networks. Furthermore, a nonlinear transformation scheme is introduced to alleviate the training data's skewness and improve the DeepBL performance by affording a smaller prediction error. Specifically, the output data of a large‐eddy simulation of an idealized TC are used to train, validate, and test DeepBL, affording significantly better performance than the YSU scheme in the Weather Research and Forecast model. Interpretability analysis on DeepBL demonstrates that the deep‐learning parameterization scheme is physically reasonable.