EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Uncertainty quantification in variational data assimilation with deep learning

Nicolas Lafon1, Philippe Naveau1, and Ronan Fablet2
Nicolas Lafon et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France (
  • 2IMT Atlantique, Lab-STICC , Brest, France

The spatio-temporal reconstruction of a dynamical process from some observationaldata is at the core of a wide range of applications in geosciences. This is particularly true for weather forecasting, operational oceanography and climate studies. However, the re35 construction of a given dynamic and the prediction of future states must take into ac36 count the uncertainties that affect the system. Thus, the available observational measurements are only provided with a limited accuracy. Besides, the encoded physical equa38 tions that model the evolution of the system do not capture the full complexity of the real system. Finally, the numerical approximation generates a non-negligible error. For these reasons, it seems relevant to calculate a probability distribution of the state system rather than the most probable state. Using recent advances in machine learning techniques for inverse problems, we propose an algorithm that jointly learns a parametric distribution of the state, the dynamics governing the evolution of the parameters, and a solver. Experiments conducted on synthetic reference datasets, as well as on datasets describing environmental systems, validate our approach.

How to cite: Lafon, N., Naveau, P., and Fablet, R.: Uncertainty quantification in variational data assimilation with deep learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15994,, 2023.