Simulation of Atlantic Hurricane Tracks and Features: A Deep Learning Approach
This provides a tool for meteorologists and disaster planners to simulate hurricane risks, but it is incremental as it applies existing deep learning methods to hurricane data.
The paper tackled simulating Atlantic hurricane tracks and features using deep learning to generate synthetic storms consistent with historical records, demonstrating efficacy for locations like New Orleans with models predicting landfall and wind speed.
The objective of this paper is to employ machine learning (ML) and deep learning (DL) techniques to obtain from input data (storm features) available in or derived from the HURDAT2 database models capable of simulating important hurricane properties such as landfall location and wind speed that are consistent with historical records. In pursuit of this objective, a trajectory model providing the storm center in terms of longitude and latitude, and intensity models providing the central pressure and maximum 1-$min$ wind speed at 10 $m$ elevation were created. The trajectory and intensity models are coupled and must be advanced together, six hours at a time, as the features that serve as inputs to the models at any given step depend on predictions at the previous time steps. Once a synthetic storm database is generated, properties of interest, such as the frequencies of large wind speeds may be extracted from any part of the simulation domain. The coupling of the trajectory and intensity models obviates the need for an intensity decay inland of the coastline. Prediction results are compared to historical data, and the efficacy of the storm simulation models is demonstrated for three examples: New Orleans, Miami and Cape Hatteras.