mlsanityData to 7 Oct 2026

Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning

arXiv 2307.09060 ↗First posted 18 Jul 2023physics.ao-ph cs.LG physics.data-ancited by 3

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Abstract (arXiv)

We present a data-driven emulator, stochastic weather generator (SWG), suitable for estimating probabilities of prolonged heatwaves in France and Scandinavia. This emulator is based on the method of analogs of circulation to which we add temperature and soil moisture as predictor fields. We train the emulator on an intermediate complexity climate model run and show that it is capable of predicting conditional probabilities (forecasting) of heatwaves out of sample. Special attention is payed that this prediction is evaluated using proper score appropriate for rare events. To accelerate the computation of analogs dimensionality reduction techniques are applied and the performance is evaluated. The probabilistic prediction achieved with SWG is compared with the one achieved with Convolutional Neural Network (CNN). With the availability of hundreds of years of training data CNNs perform better at the task of probabilistic prediction. In addition, we show that the SWG emulator trained on 80 years of data is capable of estimating extreme return times of order of thousands of years for heatwaves longer than several days more precisely than the fit based on generalised extreme value distribution. Finally, the quality of its synthetic extreme teleconnection patterns obtained with stochastic weather generator is studied. We showcase two examples of such synthetic teleconnection patterns for heatwaves in France and Scandinavia that compare favorably to the very long climate model control run.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 104 references that are in the map. First posted on arXiv 18 Jul 2023.

Builds on

13 of 104 references.

References · 13

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1 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwavescited by 4
  2. 2025AI-boosted rare event sampling to characterize extreme weathercited by 2
  3. 2024Estimation of AMOC transition probabilities using a machine learning based rare-event algorithmcited by 0