TC-Next: Zero-Shot Multimodal Cyclone Forecasting
cs.LG, cs.AI, cs.CV
Submitted: 2026-09-02
Updated: 2026-09-02
Comments: 11 pages
License: http://creativecommons.org/licenses/by/4.0/
The gist: We present TropicalCycloneNext (TC-Next), a multimodal deep learning model that forecasts tropical cyclone track and intensity at 6 - 24 h leads by leveraging a foundation model's forecast fields of
Terminology
Abstract
We present TropicalCycloneNext (TC-Next), a multimodal deep learning model that forecasts tropical cyclone track and intensity at 6 - 24 h leads by leveraging a foundation model's forecast fields of atmospheric kinematic and thermodynamic fields and GridSat infrared satellite imagery. Trained only on GraphCast forecasts over the Western Pacific (WP), yet reliant only on generic atmospheric variables, TC-Next on GraphCast lowers track error by 15 - 44% and intensity error by a factor of 3 - 6 relative to a conventional, rule-based tracker, TempestExtremes; applied without retraining to the forecast fields of Pangu-Weather and IFS HRES, it stays ahead of TempestExtremes on both. Applied zero-shot to the generic weather fields of WeatherNext Cyclones on the 2025 WP season, TC-Next attains lower intensity error at every lead time, and lower or comparable track error, compared to that model's specialized direct tracker in a deterministic comparison. Our ablation studies show that our multimodal model is able to utilize the additional modality to improve performance in tracking errors at every lead time and in intensity prediction at longer lead times.
Sources
- A Foundation Model for the Earth System
- GenCast: Diffusion-based ensemble forecasting for medium-range weather
- Skillful joint probabilistic weather forecasting from marginals
- TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale
- FuXi-TC: A generative framework integrating deep learning and physics-based models for improved tropical cyclone forecasts
- CycloneMAE: A Scalable Multi-Task Learning Model for Global Tropical Cyclone Probabilistic Forecasting
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