Summary of Ai For Extreme Event Modeling and Understanding: Methodologies and Challenges, by Gustau Camps-valls et al.

AI for Extreme Event Modeling and Understanding: Methodologies and Challenges

by Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs, Adrian Höhl, Andrea Castelletti, Aytac Pacal, Claire Robin, Francesco Martinuzzi, Ioannis Papoutsis, Ioannis Prapas, Jorge Pérez-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 Happé, Tristan Williams

First submitted to arxiv on: 28 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper reviews how artificial intelligence (AI) is being used in Earth system sciences to analyze extreme events such as floods, droughts, wildfires, and heatwaves. AI has improved weather forecasting, model emulation, parameter estimation, and the prediction of these events. However, developing accurate predictors from noisy, heterogeneous, and limited annotated data poses specific challenges. To overcome these hurdles, it is crucial to create accurate, transparent, and reliable AI models that can integrate information in real-time, be deployed effectively, and be understandable to stakeholders. The paper highlights the importance of collaboration across different fields to develop practical, understandable, and trustworthy AI solutions for analyzing and predicting extreme events, ultimately enhancing disaster readiness and risk reduction.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper is about using artificial intelligence (AI) to help us understand and prepare for big natural disasters like floods, droughts, wildfires, and heatwaves. Right now, AI is being used in weather forecasting, but it’s also helping us predict these extreme events. The problem is that we don’t have enough good data to train the AI models accurately. To fix this, scientists are working together across different fields to create better AI solutions that can take in new information quickly and make sense to people. This will help us respond to disasters more effectively and reduce the risks of them happening.