TC-Next is a multimodal deep learning model that forecasts tropical cyclone track and intensity for 6‑24 hour lead times by combining atmospheric forecast fields from a foundation model with GridSat infrared satellite imagery. Trained solely on GraphCast forecasts for the Western Pacific, it reduces track error by 15‑44% and intensity error by a factor of 3‑6 compared to the rule‑based tracker TempestExtremes, and maintains superior performance when applied zero‑shot to other forecast systems such as Pangu‑Weather, IFS HRES, and WeatherNext Cyclones. Ablation studies confirm that incorporating the additional satellite modality consistently improves tracking accuracy at all lead times and enhances intensity predictions, especially at longer horizons.
By Zhe Wang, Sijie Chen, Yiming Luo, Daehyun Kim, Chien-Yi Chang
arXiv:2608. 09959v1 Announce Type: cross Abstract: AI weather models are in the process of revolutionising weather forecasting.
By Anna Allen, Wessel P. Bruinsma, Michael Maier-Gerber, Harrison Cook, Matthew Chantry, Richard E. Turner
Tianmu-TC is a physics‑constraints generative AI framework designed for global tropical cyclone forecasting. Trained on Western North Pacific data, it produces controllable outputs with reduced uncertainty, outperforming both deterministic and ensemble meteorological AI models as well as the ECMWF NWP system across global ocean basins. The model also demonstrates strong performance in challenging scenarios such as data sparsity, anomaly tracks, rapid intensification, and weakening, while maintaining significantly lower computational cost.
By Shiqi Zhang, Pan Mu, Cheng Huang, Hanting Yan, Yuchao Zhu, Jinglin Zhang, Shengyong Chen, Shoujuan Shu, Cong Bai
arXiv:2608. 09683v1 Announce Type: new Abstract: Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses.
By Swastik Agrawal, Nishkal Hundia, Ziyue Liu, Michelle Bensi
The paper introduces Pangu‑Bayes, a probabilistic forecasting hierarchy that separates atmospheric‑state uncertainty from learned‑model uncertainty as distinct stochastic variables, allowing cross‑flow perturbations of the evolving state with Bayesian parameter samples. In tests on 90 held‑out 2023 tropical cyclones, Pangu‑Bayes reduces track, pressure, and wind errors by 54.2%, 17.2%, and 24.9% respectively, and improves rapid‑intensification detection. The study finds that atmospheric‑state variability more consistently improves track prediction, while learned‑model variability more often enhances intensity prediction, demonstrating how model‑defined uncertainty resolution can be linked to target‑dependent value and dynamical interpretation.
By Wenbo Hu, Xinlei Xiong, Shuxun Zhou, Kaifeng Bi, Lingxi Xie, Jun Zhu, Richang Hong, Qi Tian
arXiv:2608. 09286v1 Announce Type: cross Abstract: Global medium-range weather forecasting requires modeling structured yet state-dependent interactions among heterogeneous atmospheric fields.
By Zhisheng Chen, Jinhan Li, Yuxuan Li, Yuan Gao, Hao Wu, Zheng Lu, Jinlong Du, Kun Wang, Bo An