Title:
Deep learning for hurricane forecasting
Abstract:
The forecast of hurricane trajectories is crucial for the protection of people and property, but machine learning techniques have been scarce for this so far. I will present a method that we developed recently, a fusion of neural networks, that is able to combine past
trajectory data and reanalysis atmospheric images (wind and pressure 3D fields). Our network is trained to estimate the longitude and latitude
displacement of hurricanes and depressions from a large database from
both hemispheres (more than 3000 storms since 1979, sampled at a 6 hour
frequency). Finally, I will give an overview of the hackathon that I organized on a very close topic at the Climate Informatics Workshop in September.