Combining LSTM statistical analysis with dynamical models to investigate Typhoon Mangkhut (2018)

Clifford Evan Watkins, Sue Chen

Preprint · 07 March 2022

In one sentence. Trains an LSTM on COAMPS ensemble output to predict Typhoon Mangkhut's intensity and identify drivers of rapid intensification from asymmetry and heat flux features.

Abstract

This study develops a long short-term memory mode (LSTM) neural network algorithm for the prediction of tropical cyclone intensity (maximum wind speed). This is achieved by combing the statistical tools of machine learning and the dynamical earth system model of Typhoon Mangkhut (2018). The Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System is used to produce an ensemble of runs of Mangkhut to train the LSTM. Being able to predict the behavior of tropical cyclones allows for a way to analyze the complex dynamical inputs to investigate the driving forces behind periods of rapid intensification, stagnation, and weakening. The controlling parameters that produced the best prediction are the mean inner surface heat flux, amplitude of the first order asymmetry in the inner heat flux, the angle difference between the first order asymmetries of vorticity output at 850mb and 500mb, and the amplitude of first order asymmetry in the geopotential height at 850mb.

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