Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

• Main Authors: Boussioux, Léonard, Zeng, Cynthia, Guénais, Théo, Bertsimas, Dimitris
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2020
• Subjects: Machine Learning cs.LG; Artificial Intelligence cs.AI; Atmospheric and Oceanic Physics physics.ao-ph; FOS Computer and information sciences; FOS Physical sciences; Pacific; North Atlantic
• Online Access: https://dx.doi.org/10.48550/arxiv.2011.06125; https://arxiv.org/abs/2011.06125

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 extracting features with deep-learning encoder-decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and Eastern Pacific basins on 2016-2019 for 24-hour lead time track and intensity forecasts and show they achieve comparable mean average error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve over the National Hurricane Center's official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting. : Spotlight talk at NeurIPS 2021, Tackling Climate Change with AI Under revision by the AMS' Weather and Forecasting journal