Grand événement - AI and math for meteorology and climatology - Marc Bocquet: Artificial intelligence for geophysical data assimilation
Grand événement - À la recherche d'un Avenir Commun DurableL'IA et les mathématiques pour la météorologie et la climatologieAI and math for meteorology and climatologyCollège de FranceAnnée 2024-20255 mai 2025Grand événement - AI and math for meteorology and climatology - Marc Bocquet: Artificial intelligence for geophysical data assimilationMarc BocquetCEREA, ENPC, EdF R&D, Institut Polytechnique de ParisRésuméData assimilation is the set of key mathematical methods used to optimally combine observations and numerical model outputs. Data assimilation (DA) is critical to adjust the initial condition of meteorological forecasts, to estimate model parameters, and produce accurate re-analysis datasets. It has been at the heart of all operational weather forecasts for the past 50 years. Very recently, artificial intelligence (AI) and in particular deep learning, has begun being used as a tool to improve classical DA, to be combined with DA algorithmic schemes, or even to offer a substitute for DA.I will give an overview of the recent achievements and promising routes offered by AI into DA.For instance, ML can be leveraged in the regularisation of ensemble-based DA, in the solvers of variational DA methods, for generating or augmenting ensembles in DA, for building surrogates of the tangent linear and adjoint meteorological models to be used within DA, to learn a model error correction within a weak-constraint 4D-Var framework, or, ultimately, as a replacement for the DA analysis. I will also present an example where AI unveils new DA methods that were overlooked so far by the research community.Marc BocquetMarc Bocquet holds a Ph.D. from École Polytechnique and has an Habilitation delivered by Sorbonne University. He was a postdoctoral fellow at the University of Warwick and then at the University of Oxford. He is currently deputy director of CEREA, a laboratory of École nationale des ponts et chaussées and EDF R&D, and a professor at École nationale des ponts et chaussées, Institut Polytechnique de Paris. He works on data assimilation, inverse problems and statistical learning applied to the geosciences. He develops mathematical methods to better estimate the state of the atmosphere, of the ocean and the climate, as well as their constituents, using massive observations and complex models. He has published 115 papers and two books. He is associate editor of several peer-reviewed journals in the geosciences, and a Distinguished Research Fellow of the world's most renowned weather forecasting centre, the European Centre for Medium-Range Weather Forecasts (ECMWF).
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