This paper describes a machine learning (ML) framework for tropical cyclone
intensity and track forecasting, combining multiple distinct ML techniques and
utilizing diverse data sources. Our framework, which we refer to as Hurricast
(HURR), is built upon the combination of distinct data processing techniques
using gradient-boosted trees and novel encoder-decoder architectures, including
CNN, GRU and Transformers components. We propose a deep-feature extractor
methodology to mix spatial-temporal data with statistical data efficiently. Our
multimodal framework unleashes the potential of making forecasts based on a
wide range of data sources, including historical storm data, and visual data
such as reanalysis atmospheric images. We evaluate our models with current
operational forecasts in North Atlantic and Eastern Pacific basins on 2016-2019
for 24-hour lead time, and show our models consistently outperform
statistical-dynamical models and compete with the best dynamical models, while
computing forecasts in seconds. Furthermore, the inclusion of Hurricast into an
operational forecast consensus model leads to a significant improvement of 5% -
15% over NHC's official forecast, thus highlighting the complementary
properties with existing approaches. In summary, our work demonstrates that
combining different data sources and distinct machine learning methodologies
can lead to superior tropical cyclone forecasting. We hope that this work opens
the door for further use of machine learning in meteorological forecasting.Comment: Under revision by the AMS' Weather and Forecasting journa