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LoRA Adaptation Strength in Aurora-WRF: Trade-offs in Regional Heavy-Rainfall Forecasting — arXiv:2610.03747

Source: arXiv
Original: https://arxiv.org/abs/2610.03747
License (as archived): CC0-1.0
Archived: 2026-10-06T05:25:47.459Z
SHA-256 of the archived text: a726364556ece26929871c9ba018bff5e625e11ec170bad501a59cc42f7c2d51
Stable URL of this page: https://metraipolis.com/documento/dc_eeaaedd52272

How to cite

LoRA Adaptation Strength in Aurora-WRF: Trade-offs in Regional Heavy-Rainfall Forecasting — arXiv:2610.03747 — arXiv, https://arxiv.org/abs/2610.03747 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eeaaedd52272 (Metraipolis archive dc_eeaaedd52272, sha256 a726364556ece26929871c9ba018bff5e625e11ec170bad501a59cc42f7c2d51)

BibTeX

@misc{dceeaaedd52272,
  title = {LoRA Adaptation Strength in Aurora-WRF: Trade-offs in Regional Heavy-Rainfall Forecasting — arXiv:2610.03747},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.03747},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eeaaedd52272; sha256 a726364556ece26929871c9ba018bff5e625e11ec170bad501a59cc42f7c2d51},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.03747. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We investigate how parameter-efficient adaptation of an atmospheric foundation model affects downstream regional precipitation in a controlled Aurora-WRF coupling experiment over the Beijing-Tianjin-Hebei region. A single-step, precipitation-weighted LoRA adaptation of AuroraPretrained is trained on May-September 2020-2021 and selected using 2022 validation data. Five inference-time adaptation strengths are coupled to an unchanged 9-km WRF configuration and compared with a GFS-driven control under common ERA5 initialization and auxiliary-field rules. Evaluation uses 17 initializations in 2023, hourly ERA5 precipitation, and conservative remapping to a fixed 356-cell, 0.25-degree verification grid. Heavy rainfall is defined exclusively as at least 50 mm in a continuous 24-hour window, advanced hourly. For window starts from 0 to 48 hours, unadapted Aurora attains the highest mean critical success index (CSI), 0.216, compared with 0.144 for the GFS control. An intermediate LoRA strength of 0.75 reduces the Aurora-driven 24-hour precipitation mean squared error by 30.7% and brings pooled frequency Bias from 1.510 to 1.024, but lowers CSI to 0.182. GFS retains the lowest whole-domain error. These results characterize a trade-off between rainfall detection, frequency calibration, and intensity error, showing the importance of evaluating adaptation strength against multiple downstream criteria within a common coupling framework.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Atmospheric and Oceanic Physics (physics.ao-ph) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.03747 — 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 2026-10-06T05:24:05.729Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).