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Coupling rare event algorithms and deep neural network to predict extreme heat waves

By Freddy Bouchet

Appears in collection : On Future Synergies for Stochastic and Learning Algorithms / Sur les synergies futures autour des algorithmes d'apprentissage et stochastiques

Extreme events are of primarily importance for understanding the impact of climate change. However, because they are too rare and realistic models are too complex, traditional deep neural networks are inefficient for predictions. We cope with this lack of data using rare event simulations. From the best climate models, we oversample extremely rare events and obtain several hundreds more events than with usual climate runs, at a fixed numerical cost. Coupled with deep neural networks this approach improves drastically the prediction of extreme heat waves.

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Citation data

  • DOI 10.24350/CIRM.V.19817403
  • Cite this video Bouchet Freddy (9/27/21). Coupling rare event algorithms and deep neural network to predict extreme heat waves. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19817403
  • URL https://dx.doi.org/10.24350/CIRM.V.19817403

Bibliography

  • RAGONE, Francesco, WOUTERS, Jeroen, et BOUCHET, Freddy. Computation of extreme heat waves in climate models using a large deviation algorithm. Proceedings of the National Academy of Sciences, 2018, vol. 115, no 1, p. 24-29. - https://doi.org/10.1073/pnas.1712645115
  • LUCENTE, Dario, ROLLAND, Joran, HERBERT, Corentin, et al. Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain. arXiv preprint arXiv:2110.05050, 2021. - https://arxiv.org/abs/2110.05050
  • JACQUES-DUMAS, Valérian, RAGONE, Francesco, BORGNAT, Pierre, et al. Deep Learning-based Extreme Heatwave Forecast. arXiv preprint arXiv:2103.09743, 2021. - https://arxiv.org/abs/2103.09743

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