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StationPDE: Station-Oriented Surface PDE Learning for Multi-Station Multivariate Weather Forecasting — arXiv:2609.22123

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.22123
License (as archived): CC0-1.0
Archived: 2026-10-09T07:05:59.160Z
SHA-256 of the archived text: 74d71003b4d299cb6c71ef914e35964582b7948053d60ead1f7eb7eac7ff74d4
Stable URL of this page: https://metraipolis.com/documento/dc_85fe7ea3733d

How to cite

StationPDE: Station-Oriented Surface PDE Learning for Multi-Station Multivariate Weather Forecasting — arXiv:2609.22123 — arXiv (en), https://arxiv.org/abs/2609.22123 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_85fe7ea3733d (Metraipolis archive dc_85fe7ea3733d, sha256 74d71003b4d299cb6c71ef914e35964582b7948053d60ead1f7eb7eac7ff74d4)

BibTeX

@misc{dc85fe7ea3733d,
  title = {StationPDE: Station-Oriented Surface PDE Learning for Multi-Station Multivariate Weather Forecasting — arXiv:2609.22123},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.22123},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_85fe7ea3733d; sha256 74d71003b4d299cb6c71ef914e35964582b7948053d60ead1f7eb7eac7ff74d4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.22123. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multi-station multivariate weather forecasting aims to forecast future weather variables at multiple weather stations from historical surface observations. Existing station forecasting models learn statistical dependencies among discrete stations, but lack explicit physical evolution. Meanwhile, PDE-based weather models provide interpretable physical dynamics, yet require continuous fields and upper-air variables unavailable in surface station data. To bridge this gap, we propose StationPDE, a station-oriented surface PDE learning model. StationPDE constructs a terrain-aware continuous surface field from discrete station observations and decomposes its physical evolution into surface wind transport and upper-air inference. Surface wind transport explicitly evolves observable weather variables, while upper-air inference uses learnable horizontal diffusion to approximate the missing influence of unavailable upper-air variables. A parallel data-driven diffusion branch captures complementary motion patterns, and an adaptive router integrates the two forecasts for station-level multivariate forecasting. Experiments on Weather2K and MeteoNet show that StationPDE consistently outperforms state-of-the-art baselines, reducing MSE by about $9.6\%$ on average compared with the strongest baseline.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-22 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.22123 — 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 2026-10-09T06:58:09.120Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).