IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-44697-2.html
   My bibliography  Save this article

Hybrid AI-enhanced lightning flash prediction in the medium-range forecast horizon

Author

Listed:
  • Mattia Cavaiola

    (Chemical and Environmental Engineering
    Sezione di Genova
    Institute of Marine Sciences)

  • Federico Cassola

    (Regional Agency for Environmental Protection Liguria)

  • Davide Sacchetti

    (Regional Agency for Environmental Protection Liguria)

  • Francesco Ferrari

    (Chemical and Environmental Engineering
    Sezione di Genova)

  • Andrea Mazzino

    (Chemical and Environmental Engineering
    Sezione di Genova)

Abstract

Traditional fully-deterministic algorithms, which rely on physical equations and mathematical models, are the backbone of many scientific disciplines for decades. These algorithms are based on well-established principles and laws of physics, enabling a systematic and predictable approach to problem-solving. On the other hand, AI-based strategies emerge as a powerful tool for handling vast amounts of data and extracting patterns and relationships that might be challenging to identify through traditional algorithms. Here, we bridge these two realms by using AI to find an optimal mapping of meteorological features predicted two days ahead by the state-of-the-art numerical weather prediction model by the European Centre for Medium-range Weather Forecasts (ECMWF) into lightning flash occurrence. The prediction capability of the resulting AI-enhanced algorithm turns out to be significantly higher than that of the fully-deterministic algorithm employed in the ECMWF model. A remarkable Recall peak of about 95% within the 0-24 h forecast interval is obtained. This performance surpasses the 85% achieved by the ECMWF model at the same Precision of the AI algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-44697-2
    DOI: 10.1038/s41467-024-44697-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-44697-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-44697-2?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
    ---><---

    References listed on IDEAS

    as
    1. Imme Ebert-Uphoff & Kyle Hilburn, 2023. "The outlook for AI weather prediction," Nature, Nature, vol. 619(7970), pages 473-474, July.
    2. Casciaro, Gabriele & Ferrari, Francesco & Lagomarsino-Oneto, Daniele & Lira-Loarca, Andrea & Mazzino, Andrea, 2022. "Increasing the skill of short-term wind speed ensemble forecasts combining forecasts and observations via a new dynamic calibration," Energy, Elsevier, vol. 251(C).
    3. Yuchen Zhang & Mingsheng Long & Kaiyuan Chen & Lanxiang Xing & Ronghua Jin & Michael I. Jordan & Jianmin Wang, 2023. "Skilful nowcasting of extreme precipitation with NowcastNet," Nature, Nature, vol. 619(7970), pages 526-532, July.
    4. 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.
    5. Kaifeng Bi & Lingxi Xie & Hengheng Zhang & Xin Chen & Xiaotao Gu & Qi Tian, 2023. "Accurate medium-range global weather forecasting with 3D neural networks," Nature, Nature, vol. 619(7970), pages 533-538, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chu, Yinghao & Wang, Yiling & Yang, Dazhi & Chen, Shanlin & Li, Mengying, 2024. "A review of distributed solar forecasting with remote sensing and deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    2. Wang, Tao & Zhou, Hanxu & Fang, Qing & Han, Yanan & Guo, Xingxing & Zhang, Yahui & Qian, Chao & Chen, Hongsheng & Barland, Stéphane & Xiang, Shuiying & Lippi, Gian Luca, 2024. "Reservoir computing-based advance warning of extreme events," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    3. Fabian Dvorak & Regina Stumpf & Sebastian Fehrler & Urs Fischbacher, 2024. "Generative AI Triggers Welfare-Reducing Decisions in Humans," Papers 2401.12773, arXiv.org.
    4. Lei Chen & Xiaohui Zhong & Hao Li & Jie Wu & Bo Lu & Deliang Chen & Shang-Ping Xie & Libo Wu & Qingchen Chao & Chensen Lin & Zixin Hu & Yuan Qi, 2024. "A machine learning model that outperforms conventional global subseasonal forecast models," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    5. Hang Gao & Chun Shen & Xuesong Wang & Pak-Wai Chan & Kai-Kwong Hon & Jianbing Li, 2024. "Interpretable semi-supervised clustering enables universal detection and intensity assessment of diverse aviation hazardous winds," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    6. 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.
    7. Florian Achermann & Thomas Stastny & Bogdan Danciu & Andrey Kolobov & Jen Jen Chung & Roland Siegwart & Nicholas Lawrance, 2024. "WindSeer: real-time volumetric wind prediction over complex terrain aboard a small uncrewed aerial vehicle," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    8. 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.
    9. Liu, Wenhui & Bai, Yulong & Yue, Xiaoxin & Wang, Rui & Song, Qi, 2024. "A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM," Energy, Elsevier, vol. 294(C).
    10. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.

    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:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-44697-2. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.