# Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

arXiv: https://arxiv.org/abs/2011.06125 · HTML: https://mlsanity.com/paper/2011.06125/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 13 of 13)

- [AI Foundation Models for Weather and Climate: Applications, Design, and Implementation](https://mlsanity.com/paper/2309.10808/) (2023)
- [TIML: Task-Informed Meta-Learning for Agriculture](https://mlsanity.com/paper/2202.02124/) (2022)
- [Predicting Landfall's Location and Time of a Tropical Cyclone Using Reanalysis Data](https://mlsanity.com/paper/2103.16108/) (2021)
- [Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach](https://mlsanity.com/paper/2304.04308/) (2023)
- [Forecasting formation of a Tropical Cyclone Using Reanalysis Data](https://mlsanity.com/paper/2212.06149/) (2022)
- [Leveraging AI for Natural Disaster Management : Takeaways From The Moroccan Earthquake](https://mlsanity.com/paper/2311.08999/) (2023)
- [Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning](https://mlsanity.com/paper/2405.20486/) (2024)
- [Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset](https://mlsanity.com/paper/2504.11022/) (2025)
- [Data-Based Models for Hurricane Evolution Prediction: A Deep Learning Approach](https://mlsanity.com/paper/2111.12683/) (2021)
- [Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data](https://mlsanity.com/paper/2401.16936/) (2024)
- [VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting](https://mlsanity.com/paper/2404.16037/) (2024)
- [Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting](https://mlsanity.com/paper/2510.02050/) (2025)
- [TC-Next: Zero-Shot Multimodal Cyclone Forecasting](https://mlsanity.com/paper/2609.02085/) (2026)

## Abstract (arXiv)

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.