Predicting Hurricane Trajectories using a Recurrent Neural Network

TL;DR

This paper presents a recurrent neural network approach to predict hurricane trajectories using weather data, achieving competitive accuracy and extending prediction up to 120 hours.

Contribution

The study introduces a fully connected RNN model for hurricane path prediction, leveraging detailed weather data to improve forecast accuracy over existing methods.

Findings

Predicts hurricane paths up to 120 hours ahead

Achieves accuracy comparable to current NHC methods

Utilizes a fine grid to reduce truncation errors

Abstract

Hurricanes are cyclones circulating about a defined center whose closed wind speeds exceed 75 mph originating over tropical and subtropical waters. At landfall, hurricanes can result in severe disasters. The accuracy of predicting their trajectory paths is critical to reduce economic loss and save human lives. Given the complexity and nonlinearity of weather data, a recurrent neural network (RNN) could be beneficial in modeling hurricane behavior. We propose the application of a fully connected RNN to predict the trajectory of hurricanes. We employed the RNN over a fine grid to reduce typical truncation errors. We utilized their latitude, longitude, wind speed, and pressure publicly provided by the National Hurricane Center (NHC) to predict the trajectory of a hurricane at 6-hour intervals. Results show that this proposed technique is competitive to methods currently employed by the NHC and can predict up to approximately 120 hours of hurricane path.