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Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data

Abstract

The forecast of tropical cyclone trajectories is crucial for the protection of people and property. Although forecast dynamical models can provide high-precision short-term forecasts, they are computationally demanding, and current statistical forecasting models have much room for improvement given that the database of past hurricanes is constantly growing. Machine learning methods, that can capture non-linearities and complex relations, have only been scarcely tested for this application. We propose a neural network model fusing past trajectory data and reanalysis atmospheric images (wind and pressure 3D fields). We use a moving frame of reference that follows the storm center for the 24 h tracking forecast. The network is trained to estimate the longitude and latitude displacement of tropical cyclones and depressions from a large database from both hemispheres (more than 3,000 storms since 1979, sampled at a 6 h frequency). The advantage of the fused network is demonstrated and a comparison with current forecast models shows that deep learning methods could provide a valuable and complementary prediction. Moreover, our method can give a forecast for a new storm in a few seconds, which is an important asset for real-time forecasts compared to traditional forecasts.

Article / Publication Data
Active/Online
YES
Available Metadata
DOI ↗
Early Online Release
January 10, 2020
Fiscal Year
NOAA IR URL ↗
Peer Reviewed
YES
Publication Name
Frontiers In Big Data
Published On
January 28, 2020
Publisher Name
Frontiers
Submitted On
October 23, 2019
URL ↗

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Authors

Authors who have authored or contributed to this publication.