Tropical Cyclone Forecast Using Multitask Deep Learning Framework

Abstract

A tropical cyclone is a robust weather system that affects human daily life. Accurate and rapid tropical cyclone forecast can guide human disaster prevention and mitigation work against tropical cyclones. The mainstream tropical cyclone forecasting method is numerical forecasting, which requires abundant prior knowledge and luxurious calculation. Nowadays, machine learning methods have received increasing attention for which they can overcome these disadvantages. However, existing machine learning methods usually ignored some potential factors since they mainly concentrated on one aspect of the tropical cyclone forecast. This letter proposes a multitask machine learning framework to forecast tropical cyclone path and intensity, which possesses two modules: one is the prediction module and the other is the estimate module. We use an improved generative adversarial network as the prediction module to predict the tropical cyclone spatial data at a certain moment in the future. Then, we use two different deep neural networks as the estimation module to extract the position and intensity from the generated prediction data. The method we propose is a general and relatively accurate tropical cyclone forecast method. We reach a 24-h path forecast error of 116 km and a 24-h intensity forecast error of 13.06 kt.