Deep Learning for Hurricane Track Forecasting from Aligned Spatio-temporal Climate Datasets

Sophie Giffard-Roisin 1 Mo Yang 2 Guillaume Charpiat 3 Balázs Kégl 2 Claire Monteleoni 1
3 TAU - TAckling the Underspecified
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
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. We propose a neural network fusing past trajectory data and reanalysis atmospheric images (wind and pressure 3D fields). We used a moving frame of reference that follows the storm center for the 24h tracking forecast. The 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). The advantage of the fusion network is demonstrated and a comparison with current forecast models shows that deep methods could provide a valuable and complementary prediction.
Type de document :
Communication dans un congrès
Modeling and decision-making in the spatiotemporal domain NIPS workhop, Dec 2018, Montréal, Canada
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https://hal.archives-ouvertes.fr/hal-01905408
Contributeur : Sophie Giffard-Roisin <>
Soumis le : jeudi 25 octobre 2018 - 17:20:54
Dernière modification le : jeudi 7 février 2019 - 16:54:22
Document(s) archivé(s) le : samedi 26 janvier 2019 - 16:00:28

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  • HAL Id : hal-01905408, version 1

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Sophie Giffard-Roisin, Mo Yang, Guillaume Charpiat, Balázs Kégl, Claire Monteleoni. Deep Learning for Hurricane Track Forecasting from Aligned Spatio-temporal Climate Datasets. Modeling and decision-making in the spatiotemporal domain NIPS workhop, Dec 2018, Montréal, Canada. 〈hal-01905408〉

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