Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data
| Autor: | George Miloshevich, Bastien Cozian, Patrice Abry, Pierre Borgnat, Freddy Bouchet |
|---|---|
| Přispěvatelé: | Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Unité de Mathématiques Pures et Appliquées (UMPA-ENSL), École normale supérieure de Lyon (ENS de Lyon)-Centre National de la Recherche Scientifique (CNRS), Département de Physique [ENS Lyon], École normale supérieure - Lyon (ENS Lyon)-Université de Lyon, Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), ANR-20-CE01-0008,SAMPRACE,Simuler des Evenements Climatiques Rares(2020), ANR-16-IDEX-0005,IDEXLYON,IDEXLYON(2016), École normale supérieure de Lyon (ENS de Lyon)-Université de Lyon, École normale supérieure de Lyon (ENS de Lyon)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Fluid Flow and Transfer Processes
FOS: Computer and information sciences [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph] Computer Science - Machine Learning heatwaves Committor Function Convolutional Neural Networks Computational Mechanics FOS: Physical sciences Deep learning Machine Learning (cs.LG) Physics - Atmospheric and Oceanic Physics Rare Event Algorithms [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [SDU]Sciences of the Universe [physics] Modeling and Simulation Physics - Data Analysis Statistics and Probability Atmospheric and Oceanic Physics (physics.ao-ph) Data Analysis Statistics and Probability (physics.data-an) [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis Statistics and Probability [physics.data-an] climate extremes |
| Zdroj: | Physical Review Fluids Physical Review Fluids, 2023, 8 (4), pp.040501. ⟨10.1103/PhysRevFluids.8.040501⟩ |
| ISSN: | 2469-990X |
| Popis: | Understanding extreme events and their probability is key for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. Forecasting the occurrence probability of extreme heatwaves is a primary challenge for risk assessment and attribution, but also for fundamental studies about processes, dataset and model validation, and climate change studies. In this work we develop a methodology to build forecasting models which are based on convolutional neural networks, trained on extremely long climate model outputs. We demonstrate that neural networks have positive predictive skills, with respect to random climatological forecasts, for the occurrence of long-lasting 14-day heatwaves over France, up to 15 days ahead of time for fast dynamical drivers (500 hPa geopotential height fields), and also at much longer lead times for slow physical drivers (soil moisture). This forecast is made seamlessly in time and space, for fast hemispheric and slow local drivers. We find that the neural network selects extreme heatwaves associated with a North-Hemisphere wavenumber-3 pattern. The main scientific message is that most of the time, training neural networks for predicting extreme heatwaves occurs in a regime of lack of data. We suggest that this is likely to be the case for most other applications to large scale atmosphere and climate phenomena. For instance, using one hundred years-long training sets, a regime of drastic lack of data, leads to severely lower predictive skills and general inability to extract useful information available in the 500 hPa geopotential height field at a hemispheric scale in contrast to the dataset of several thousand years long. We discuss perspectives for dealing with the lack of data regime, for instance rare event simulations and how transfer learning may play a role in this latter task. Comment: 34 pages, 12 figures |
| Databáze: | OpenAIRE |
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