Predicting Landfall's Location and Time of a Tropical Cyclone Using Reanalysis Data

TL;DR

This paper presents a deep learning model combining CNN and LSTM to accurately predict the landfall location and time of tropical cyclones using high-resolution reanalysis data, enabling timely preventive measures.

Contribution

The study introduces a novel deep learning approach that predicts tropical cyclone landfall characteristics with high accuracy and rapid training, suitable for real-time applications.

Findings

Location prediction error: 66.18-158.92 km

Time prediction error: 4.71-8.20 hours

Model trains in 30-45 minutes and predicts in seconds

Abstract

Landfall of a tropical cyclone is the event when it moves over the land after crossing the coast of the ocean. It is important to know the characteristics of the landfall in terms of location and time, well advance in time to take preventive measures timely. In this article, we develop a deep learning model based on the combination of a Convolutional Neural network and a Long Short-Term memory network to predict the landfall's location and time of a tropical cyclone in six ocean basins of the world with high accuracy. We have used high-resolution spacial reanalysis data, ERA5, maintained by European Center for Medium-Range Weather Forecasting (ECMWF). The model takes any 9 hours, 15 hours, or 21 hours of data, during the progress of a tropical cyclone and predicts its landfall's location in terms of latitude and longitude and time in hours. For 21 hours of data, we achieve mean absolute error for landfall's location prediction in the range of 66.18 - 158.92 kilometers and for landfall's time prediction in the range of 4.71 - 8.20 hours across all six ocean basins. The model can be trained in just 30 to 45 minutes (based on ocean basin) and can predict the landfall's location and time in a few seconds, which makes it suitable for real time prediction.