Learning Variational Data Assimilation Models and Solvers
Abstract
This paper addresses variational data assimilation from a learning point of view. Data assimilation aims to reconstruct the time evolution of some state given a series of observations, possibly noisy and irregularly-sampled. Using automatic differentiation tools embedded in deep learning frameworks, we introduce end-to-end neural network architectures for data assimilation. It comprises two key components: a variational model and a gradient-based solver both implemented as neural networks. A key feature of the proposed end-to-end learning architecture is that we may train the NN models using both supervised and unsupervised strategies. Our numerical experiments on Lorenz-63 and Lorenz-96 systems report significant gain w.r.t. a classic gradient-based minimization of the variational cost both in terms of reconstruction performance and optimization complexity. Intriguingly, we also show that the variational models issued from the true Lorenz-63 and Lorenz-96 ODE representations may not lead to the best reconstruction performance. We believe these results may open new research avenues for the specification of assimilation models in geoscience.
Cite
@article{arxiv.2007.12941,
title = {Learning Variational Data Assimilation Models and Solvers},
author = {Ronan Fablet and Bertrand Chapron and Lucas. Drumetz and Etienne Memin and Olivier Pannekoucke and Francois Rousseau},
journal= {arXiv preprint arXiv:2007.12941},
year = {2021}
}