AI for Extreme Event Modeling and Understanding: Methodologies and Challenges
<|reference_start|>AI for Extreme Event Modeling and Understanding: Methodologies and Challenges: In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous and limited annotated data. This paper reviews how AI is being used to analyze extreme events (like floods, droughts, wildfires and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating information in real-time, deploying models, and making them understandable, all crucial for gaining the trust of stakeholders and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy for analyzing and predicting extreme events. Such collaborative efforts aim to enhance disaster readiness and disaster risk reduction.<|reference_end|>
@article{camps-valls2024ai,
    title={AI for Extreme Event Modeling and Understanding: Methodologies and
  Challenges},
    author={Gustau Camps-Valls, Miguel-'Angel Fern'andez-Torres, Kai-Hendrik
  Cohrs, Adrian H"ohl, Andrea Castelletti, Aytac Pacal, Claire Robin,
  Francesco Martinuzzi, Ioannis Papoutsis, Ioannis Prapas, Jorge
  P'erez-Aracil, Katja Weigel, Maria Gonzalez-Calabuig, Markus Reichstein,
  Martin Rabel, Matteo Giuliani, Miguel Mahecha, Oana-Iuliana Popescu, Oscar J.
  Pellicer-Valero, Said Ouala, Sancho Salcedo-Sanz, Sebastian Sippel, Spyros
  Kondylatos, Tamara Happ'e, Tristan Williams},
    journal={arXiv preprint arXiv:2406.20080},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.20080},
    primaryClass={cs.AI physics.ao-ph physics.geo-ph}
}