Hurricane Forecasting: A Novel Multimodal Machine Learning Framework
This work addresses tropical cyclone forecasting for meteorologists and disaster management, offering incremental improvements through complementary integration with existing approaches.
The paper tackles hurricane intensity and track forecasting by developing a multimodal machine learning framework called Hurricast, which combines spatial-temporal and statistical data to achieve comparable mean absolute error and skill to operational models while computing in seconds.
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 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 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.