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New AI Model TC-Next Enhances Tropical Cyclone Forecasting Accuracy

Researchers have developed TC-Next, a multimodal deep learning model for forecasting tropical cyclone track and intensity. The model leverages foundation model forecasts of atmospheric fields and satellite imagery. When trained on GraphCast forecasts, TC-Next significantly reduces track and intensity errors compared to traditional methods like TempestExtremes. Notably, TC-Next demonstrates strong performance even when applied zero-shot to other forecasting models such as Pangu-Weather, IFS HRES, and WeatherNext Cyclones, outperforming their specialized trackers. AI

IMPACT This model could significantly improve the accuracy and lead time of tropical cyclone forecasts, aiding disaster preparedness and response.

RANK_REASON The cluster describes a new research paper detailing a novel deep learning model for weather forecasting.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New AI Model TC-Next Enhances Tropical Cyclone Forecasting Accuracy

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Zhe Wang, Sijie Chen, Yiming Luo, Daehyun Kim, Chien-Yi Chang ·

    TC-Next: Zero-Shot Multimodal Cyclone Forecasting

    arXiv:2609.02085v1 Announce Type: new 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 therm…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    TC-Next: Zero-Shot Multimodal Cyclone Forecasting

    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 i…