IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v621y2023i7980d10.1038_s41586-023-06545-z.html
   My bibliography  Save this article

Author Correction: Accurate medium-range global weather forecasting with 3D neural networks

Author

Listed:
  • Kaifeng Bi

    (Huawei Cloud)

  • Lingxi Xie

    (Huawei Cloud)

  • Hengheng Zhang

    (Huawei Cloud)

  • Xin Chen

    (Huawei Cloud)

  • Xiaotao Gu

    (Huawei Cloud)

  • Qi Tian

    (Huawei Cloud)

Abstract

No abstract is available for this item.

Suggested Citation

  • Kaifeng Bi & Lingxi Xie & Hengheng Zhang & Xin Chen & Xiaotao Gu & Qi Tian, 2023. "Author Correction: Accurate medium-range global weather forecasting with 3D neural networks," Nature, Nature, vol. 621(7980), pages 45-45, September.
    • Handle: RePEc:nat:nature:v:621:y:2023:i:7980:d:10.1038_s41586-023-06545-z
    DOI: 10.1038/s41586-023-06545-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-023-06545-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/s41586-023-06545-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhenjia Chen & Zhenyuan Lin & Ji Yang & Cong Chen & Di Liu & Liuting Shan & Yuanyuan Hu & Tailiang Guo & Huipeng Chen, 2024. "Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Fabian Dvorak & Regina Stumpf & Sebastian Fehrler & Urs Fischbacher, 2024. "Generative AI Triggers Welfare-Reducing Decisions in Humans," Papers 2401.12773, arXiv.org.
    3. Huijun Zhang & Mingjie Zhang & Ran Yi & Yaxin Liu & Qiuzi Han Wen & Xin Meng, 2024. "Growing Importance of Micro-Meteorology in the New Power System: Review, Analysis and Case Study," Energies, MDPI, vol. 17(6), pages 1-33, March.
    4. Mattia Cavaiola & Federico Cassola & Davide Sacchetti & Francesco Ferrari & Andrea Mazzino, 2024. "Hybrid AI-enhanced lightning flash prediction in the medium-range forecast horizon," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:621:y:2023:i:7980:d:10.1038_s41586-023-06545-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.