# Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data

arXiv: https://arxiv.org/abs/1910.10566 · HTML: https://mlsanity.com/paper/1910.10566/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 12 of 12)

- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data](https://mlsanity.com/paper/2208.00971/) (2022)
- [Deep Learning Techniques in Extreme Weather Events: A Review](https://mlsanity.com/paper/2308.10995/) (2023)
- [Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data](https://mlsanity.com/paper/2401.15726/) (2024)
- [Predicting Landfall's Location and Time of a Tropical Cyclone Using Reanalysis Data](https://mlsanity.com/paper/2103.16108/) (2021)
- [Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast](https://mlsanity.com/paper/2202.13336/) (2022)
- [Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone](https://mlsanity.com/paper/2103.16180/) (2021)
- [Recognition of polar lows in Sentinel-1 SAR images with deep learning](https://mlsanity.com/paper/2203.16401/) (2022)
- [Forecasting formation of a Tropical Cyclone Using Reanalysis Data](https://mlsanity.com/paper/2212.06149/) (2022)
- [Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asia](https://mlsanity.com/paper/2112.12966/) (2021)
- [GraphTransformers for Geospatial Forecasting of Hurricane Trajectories](https://mlsanity.com/paper/2310.20174/) (2023)
- [Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data](https://mlsanity.com/paper/2603.29200/) (2026)

## Abstract (arXiv)

The forecast of tropical cyclone trajectories is crucial for the protection of people and property. Although forecast dynamical models can provide high-precision short-term forecasts, they are computationally demanding, and current statistical forecasting models have much room for improvement given that the database of past hurricanes is constantly growing. Machine learning methods, that can capture non-linearities and complex relations, have only been scarcely tested for this application. We propose a neural network model fusing past trajectory data and reanalysis atmospheric images (wind and pressure 3D fields). We use a moving frame of reference that follows the storm center for the 24h tracking forecast. The network is trained to estimate the longitude and latitude displacement of tropical cyclones and depressions from a large database from both hemispheres (more than 3000 storms since 1979, sampled at a 6 hour frequency). The advantage of the fused network is demonstrated and a comparison with current forecast models shows that deep learning methods could provide a valuable and complementary prediction. Moreover, our method can give a forecast for a new storm in a few seconds, which is an important asset for real-time forecasts compared to traditional forecasts.