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FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting — arXiv:2609.25505

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.25505
License (as archived): CC0-1.0
Archived: 2026-10-08T15:08:15.150Z
SHA-256 of the archived text: 02e8cf08c026e644b30f6873deda3342f64030c7de2a487bc50d79db48b54e18
Stable URL of this page: https://metraipolis.com/documento/dc_eaef1c87be2a

How to cite

FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting — arXiv:2609.25505 — arXiv (en), https://arxiv.org/abs/2609.25505 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eaef1c87be2a (Metraipolis archive dc_eaef1c87be2a, sha256 02e8cf08c026e644b30f6873deda3342f64030c7de2a487bc50d79db48b54e18)

BibTeX

@misc{dceaef1c87be2a,
  title = {FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting — arXiv:2609.25505},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.25505},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eaef1c87be2a; sha256 02e8cf08c026e644b30f6873deda3342f64030c7de2a487bc50d79db48b54e18},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.25505. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-driven approaches often lack physical interpretability. We present FAST-ML, a hybrid framework that bridges data-driven efficiency with physical constraints. A physically informed dual-stream neural parameterization ingests 3D ERA5 fields to diagnose ventilation controls---environmental wind shear and mid-level entropy deficit. By optimizing these parameters end-to-end through a differentiable FAST intensity model, this architecture establishes a robust new paradigm for observation-driven parameter optimization, ensuring storm evolution remains strictly governed by thermodynamic principles. By better capturing the storm's continuous intensity evolution, FAST-ML improves upon its physical baseline, reducing ensemble CRPS across forecast lead times, with a reduction of approximately 31% at 60 h and nearly halving the RI false alarm ratio without sacrificing detection skill. In a 100-member ensemble configuration, FAST-ML produces intensity forecasts comparable to FNV3 for selected storms under the evaluated input configurations. Furthermore, zero-shot tests on selected Eastern Pacific storms provide encouraging evidence of cross-basin transferability. FAST-ML provides a modular intensity forecasting framework that can be coupled with externally supplied storm tracks and environmental fields. It demonstrates that observation-driven parameter learning within physically constrained dynamics simultaneously enhances accuracy, interpretability, and computational efficiency.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-23 in cs.LG).
Categorie arXiv: Atmospheric and Oceanic Physics (physics.ao-ph) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.25505 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T14:37:29.535Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-23?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).