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Deep Learning Experiments for Tropical Cyclone Intensity Forecasts

Presentation Date
Thursday, December 16, 2021 at 4:00pm
Location
Convention Center - Poster Hall, D-F
Abstract

Scientists have been searching for decades for breakthroughs in tropical cyclone (TC) intensity modeling to provide more accurate and timely tropical cyclone warnings. To address this, we developed a deep learning (DL)-based multilayer perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic basin. In the first experiment, a 24-h forecast period was considered. Independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model outperformed the statistical–dynamical models by 5%–22% and achieved comparable results as the leading dynamical model HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-h intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic basin. These results highlight the potential for using DL–based models to improve operational TC intensity forecasts and synthetic TC generation.

Funding Program Area(s)