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Batched NN 4D-Var: a performant hybrid model framework for the Integrated Forecasting System — arXiv:2610.32582

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.32582
License (as archived): CC0-1.0
Archived: 2026-10-26T20:14:38.891Z
SHA-256 of the archived text: c4e23f9e21804b07f4249cd2f374ec115ec32e6c3150b14f94254312029c1e84
Stable URL of this page: https://metraipolis.com/documento/dc_6313bbdcd9eb

How to cite

Batched NN 4D-Var: a performant hybrid model framework for the Integrated Forecasting System — arXiv:2610.32582 — arXiv (en), https://arxiv.org/abs/2610.32582 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_6313bbdcd9eb (Metraipolis archive dc_6313bbdcd9eb, sha256 c4e23f9e21804b07f4249cd2f374ec115ec32e6c3150b14f94254312029c1e84)

BibTeX

@misc{dc6313bbdcd9eb,
  title = {Batched NN 4D-Var: a performant hybrid model framework for the Integrated Forecasting System — arXiv:2610.32582},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.32582},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_6313bbdcd9eb; sha256 c4e23f9e21804b07f4249cd2f374ec115ec32e6c3150b14f94254312029c1e84},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.32582. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Hybrid forecasting systems combining machine learning and numerical weather prediction offer a promising route toward improved forecast skill while preserving physical consistency. In this study, we investigate online learning of state-dependent model-error corrections within the Integrated Forecasting System using 4D-Var. Building on previous work, we represent model error using a neural network embedded within the forecast model and trained from observations through the data assimilation cycle. First, we introduce a targeted variant of neural-network 4D-Var in which only the neural-network parameters are estimated, while the atmospheric state is provided by a precomputed analysis. We then propose a batched extension that processes assimilation windows in random order and averages parameter increments over multiple windows, drawing inspiration from mini-batch stochastic optimization. Experiments are conducted with the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts at an intermediate resolution. Sequential online learning substantially improves forecasts within the training period, with error reductions exceeding 10%in some cases. However, these improvements fail to generalize to unseen data. In contrast, the proposed batched formulation produces hybrid models that consistently improve forecast skill in out-of-sample experiments. Larger batch sizes accelerate convergence and improve robustness by reducing the noise of parameter updates. Diagnostics further show that the online-trained corrections preserve realistic forecast variability and do not improve skill through artificial smoothing. These results demonstrate that the formulation of the online learning problem is critical for generalisation and establish batched online training as a promising framework for hybrid machine-learning/data-assimilation systems.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.32582 — 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 2026-10-26T19:49:03.494Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).