Machine Learning Parameterization of Mature Tropical Cyclone Boundary Layer

Abstract:

Tropical cyclone (TC) is among the most destructive weather phenomena on the earth, whose structure and intensity are strongly modulated by TC boundary layer. Mesoscale model used for TC research and prediction must rely on boundary layer parameterization due to low spacial resolution. These boundary layer schemes are mostly developed on field experiments under moderate wind speed. They often underestimate the influence of shear-driven rolls and turbulences in the meantime. When applied under extreme condition like TC boundary layer, significant bias will be unavoidable. In this study, a novel machine learning model—one dimension convolutional neural network (1DCNN)—is proposed to tackle the TC boundary layer parameterization dilemma. The 1DCNN saves about half of the free parameters and accomplishes a steady improvement compared to fully-connected neural network. TC large eddy simulation outputs are used as training data of 1DCNN, which shows strong skewness in calculated turbulent fluxes. The data skewness problem is alleviated in order to reduce 1DCNN model bias. It is shown in an offline TC boundary layer test that our proposed scheme performs significantly better than popular schemes now utilized in TC simulations.