{
  "abstract": "Accurate forecasts of near-surface winds are important for low-altitude aviation, unmanned aerial vehicle operations, and local weather services. Compared with single-station forecasting, multi-station forecasting can exploit complementary spatial information to improve wind-field representation. However, pronounced spatial heterogeneity and complex inter-station dependencies make this task inherently a spatially structured multivariate time-series forecasting problem. To address these issues, we propose AGST-WindFormer, an Adaptive-Graph Spatiotemporal Gated Wind Transformer for directly forecasting the horizontal wind components U and V. A shared Transformer captures station-wise temporal dependencies, while node embeddings generate an adaptive static graph shared across samples. A feature-wise gate then integrates the temporal and graph-aggregated representations, and a multi-output head predicts future U and V values. Experiments used hourly Meteostat observations from 854 stations during 2022–2025. The model achieved a mean wind-speed RMSE of 1.507 ± 0.009 m/s over the 10-h forecast horizon and a wind-direction MAE of 23.5 ± 0.1° for observations with wind speed ≥ 2 m/s. Relative to the strongest baseline for each metric, errors decreased by 1.1–3.2%. Ablations identified cross-station aggregation and nonuniform learned edges as the principal contributors. These results indicate that AGST-WindFormer can effectively exploit the spatiotemporal information contained in multi-station near-surface wind observations and improve forecasting accuracy across multiple consecutive future time steps.",
  "abstract_shown": true,
  "affiliations": [
    "College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chun Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Yuankang Ye"
    },
    {
      "affs": [
        0
      ],
      "name": "Shihang Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Feng Gao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6a1d4r3fwcwcs.bib",
  "cited_by": 0,
  "doi": "10.3390/atmos17100980",
  "fulltext_url": "https://www.mdpi.com/2073-4433/17/10/980",
  "id": "6a1d4r3fwcwcs",
  "issns": [
    "2073-4433"
  ],
  "landing_url": "https://doi.org/10.3390/atmos17100980",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QC851-999"
    ],
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7220863784",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/6a1d4r3fwcwcs.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:21:21Z",
      "native_id": "022b8734ee874f05a91aca6a8d905eee",
      "source": "doaj",
      "url": "https://doi.org/10.3390/atmos17100980"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1902|Meteorological Phenomena and Simulations",
      "subfield": "atmospheric"
    }
  ],
  "title": "AGST-WindFormer: A Forecasting Model for Multi-Station Near-Surface Winds",
  "title_text": "AGST-WindFormer: A Forecasting Model for Multi-Station Near-Surface Winds",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:21:21Z",
  "url": "https://research.draconitic.com/paper/6a1d4r3fwcwcs",
  "venue": "Atmosphere",
  "versions": []
}