Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone
This work addresses the critical need for accurate early warnings to reduce human and material losses from tropical cyclones in the North Indian Ocean, representing an incremental improvement over existing methods.
The researchers tackled the problem of predicting tropical cyclone landfall intensity, location, and time using a Long Short-Term Memory-based Recurrent Neural Network model, achieving state-of-the-art results with mean absolute errors of 4.24 knots for intensity, 4.5 hours for time, 0.24 degrees for latitude, and 0.37 degrees for longitude, leading to a distance error of 51.7 kilometers.
The prediction of the intensity, location and time of the landfall of a tropical cyclone well advance in time and with high accuracy can reduce human and material loss immensely. In this article, we develop a Long Short-Term memory based Recurrent Neural network model to predict intensity (in terms of maximum sustained surface wind speed), location (latitude and longitude), and time (in hours after the observation period) of the landfall of a tropical cyclone which originates in the North Indian ocean. The model takes as input the best track data of cyclone consisting of its location, pressure, sea surface temperature, and intensity for certain hours (from 12 to 36 hours) anytime during the course of the cyclone as a time series and then provide predictions with high accuracy. For example, using 24 hours data of a cyclone anytime during its course, the model provides state-of-the-art results by predicting landfall intensity, time, latitude, and longitude with a mean absolute error of 4.24 knots, 4.5 hours, 0.24 degree, and 0.37 degree respectively, which resulted in a distance error of 51.7 kilometers from the landfall location. We further check the efficacy of the model on three recent devastating cyclones Bulbul, Fani, and Gaja, and achieved better results than the test dataset.