Deep neural network provides an unprecedented forecasting tool up to two weeks before the onset of an extreme heat event

This press release was provided by CNRS—Institut de physique, in collaboration with the American Physical Society and the Laboratoire de physique de l’ENS de Lyon.

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Freddy Bouchet presents our work on learning how to predict heatwaves from data at APS DFD 2021:

Over the past decade, several extreme heat waves and heat domes have had a catastrophic impact on society and the biosphere. In 2021, all regions of the northern hemisphere have been affected. In late June and July, we saw simultaneous extreme heat waves in the Pacific Northwest, persistent heat waves in Siberia that fueled massive wildfires, and temperatures and humidity in Pakistan, northern India, and the Middle East that were at the limit of what the human body can withstand.

Understanding these extreme events is critical to quantifying the impact of climate change and better assessing risk.

However, one of the main scientific challenges raised by these events is that they cannot be studied using historical data, because they are unprecedented. Studying them with the best climate models is also severely limited, because simulating many rare events requires extremely long numerical simulations, which is often infeasible with the current computer capacities.

Freddy Bouchet, a physicist and CNRS research director at the Physics Laboratory of the ENS Lyon (France), and his team are using new mathematical, computational, and artificial intelligence tools to address these two key problems.