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arXiv Machine Learning By Jun Liu, Xiaohui Zhong, Kai Zheng, Jiarui Li, Yifei Li, Tao Zhou, Wenwen Zhang, Hao Li

3DTCR: Vortex-following generative reconstruction improves tropical cyclone intensity prediction from global AI weather forecasts

Read the original on arXiv Machine Learning →

The paper introduces 3DTCR, a vortex‑following generative framework that reconstructs high‑resolution, three‑dimensional tropical cyclone (TC) structures from coarse global AI forecasts. By training on a 3‑km, TC‑centered dataset generated with the WRF model, 3DTCR learns a coarse‑to‑fine mapping and adapts it to forecast inputs, producing detailed TC fields in minutes on a single GPU. Evaluations on 2024 western North Pacific forecasts show significant reductions in RMSE and mean bias error for maximum 10‑m wind speed compared to both FuXi and ECMWF‑HRES, while capturing key wind gradients and vortex asymmetries.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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