Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial–temporal data with statistical data by extr...

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Bibliographic Details
Published in:	Weather and forecasting Vol. 37; no. 6; pp. 817 - 831
Main Authors:	Boussioux, Léonard, Zeng, Cynthia, Guénais, Théo, Bertsimas, Dimitris
Format:	Journal Article
Language:	English
Published:	Boston American Meteorological Society 01.06.2022
Subjects:	Coders Cyclone forecasting Cyclones Data sources Deep learning Feature extraction Forecasting Frameworks Hurricane forecasting Hurricanes Lead time Learning algorithms Machine learning Mathematical models Spatiotemporal data Tropical climate Tropical cyclone forecasting Tropical cyclone intensities Tropical cyclone movement Tropical cyclones
ISSN:	0882-8156, 1520-0434
Online Access:	Get full text
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