Abstract
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 in 2016–19 for 24-h lead-time track and intensity forecasts and show they achieve comparable mean absolute 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 upon 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.
Significance Statement
Machine learning techniques have not been fully explored for improving tropical cyclone movement and intensity changes. This work shows how advanced machine learning techniques combined with routinely available information can be used to improve 24-h tropical cyclone forecasts efficiently. The successes demonstrated for 24-h forecasts provide a recipe for improving predictions for longer lead times, further reducing forecast uncertainties and benefiting society.
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Boussioux and Zeng: Equal contribution.
