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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
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    References listed on IDEAS

    as
    1. 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.
    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. Imme Ebert-Uphoff & Kyle Hilburn, 2023. "The outlook for AI weather prediction," Nature, Nature, vol. 619(7970), pages 473-474, July.
    5. 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.
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