Advanced Search
Article Contents
ABOUT THIS ARTICLE
The Innovation Geoscience > 2026 Vol. 4 > No. 3 > 100232
Next Article Previous Article
LETTER   Open Access     Cite

NUIST-WE: A foundation architecture for weather and energy forecasting integrating meteorological physics and generative pre-training

  • 1.

    Institute of Energy Meteorology, State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • 2.

    School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • 3.

    Institute of Energy Meteorology, Guodian Nanjing Automation CO., LTD., Nanjing 210032, China

  • 4.

    Weimeide company, Guodian Nanjing Automation CO., LTD., Nanjing 210032, China

  • 5.

    School of Medicine and Health, Harbin Institute of Technology, Harbin 150000, China

  • 6.

    Faculty of Computing, Harbin Institute of Technology, Harbin 150000, China

  • 7.

    School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • 8.

    School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • 9.

    Inner Mongolia Service Center of Meteorology, Hohhot 010051, China

  • 10.

    School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • 11.

    School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • 12.

    School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

    • Show all affliationsShow less
More Information
The Innovation Geoscience  4(3),  https://doi.org/10.59717/j.xinn-geo.2026.100232  |  Cite this article
    • FullText(HTML)
  • 加载中
    • REFERENCES
  • [1] Zhang Z., Lin L., Gao S., et al. (2025). A machine learning model for hub-height short-term wind speed prediction. Nat. Commun. 16:3195. DOI:10.1038/s41467-025-58456-4

    View in Article CrossRef Google Scholar

    [2] Dean N. (2022). Collaborating on clean energy action. Nat. Energy 7:785−787. DOI:10.1038/s41560-022-01118-2

    View in Article CrossRef Google Scholar

    [3] Supran G., Rahmstorf S. and Oreskes N. (2023). Assessing ExxonMobil’s global warming projections. Science 379:eabk0063. DOI:10.1126/science.abk0063

    View in Article CrossRef Google Scholar

    [4] Bodnar C., Bruinsma W. P., Lucic A., et al. (2025). A foundation model for the Earth system. Nature 641:1180−1187. DOI:10.1038/s41586-025-09005-y

    View in Article CrossRef Google Scholar

    [5] Jimenez P. A., Hacker J. P., Dudhia J., et al. (2016). WRF-Solar: Description and clear-sky assessment of an augmented NWP model for solar power prediction. Bull. Amer. Meteor. Soc. 97:1249−1264. DOI:10.1175/BAMS-D-14-00279.1

    View in Article CrossRef Google Scholar

    [6] Hersbach H., Bell B., Berrisford P., et al. (2020). The ERA5 global reanalysis. Q. J. R. Meteorol Soc. 146:1999−2049. DOI:10.1002/qj.3803

    View in Article CrossRef Google Scholar

    [7] Reichstein M., Camps-Valls G., Stevens B., et al. (2019). Deep learning and process understanding for data-driven Earth system science. Nature 566:195−204. DOI:10.1038/s41586-019-0912-1

    View in Article CrossRef Google Scholar

    [8] Moon J., Kim J., Kang P., et al. (2020). Solving the cold-start problem in short-term load forecasting using tree-based methods. Energies 13:886. DOI:10.3390/en13040886

    View in Article CrossRef Google Scholar

    • Cited by

  • Cite this article:

    Chen H., Liu Y., Zhu H., et al. (2026). NUIST-WE: A foundation architecture for weather and energy forecasting integrating meteorological physics and generative pre-training. The Innovation Geoscience 4:100232. https://doi.org/10.59717/j.xinn-geo.2026.100232
    Chen H., Liu Y., Zhu H., et al. (2026). NUIST-WE: A foundation architecture for weather and energy forecasting integrating meteorological physics and generative pre-training. The Innovation Geoscience 4:100232. https://doi.org/10.59717/j.xinn-geo.2026.100232

Welcome!

To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.

PDF
Figures(1)    

Share

  • Share the QR code with wechat scanning code to friends and circle of friends.

Article Metrics

Article views(1435) PDF downloads(647)

Relative Articles

Cited by

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return

    {{newsColumn.name}}

    {{newsColumn.name}}

    Stay connected

    {{newsColumn.name}}

    Website copyright © Innovation Press. Published articles are licensed under CC BY with copyright retained by the authors.