Learning Variational Data Assimilation Models and Solvers

• URL: http://arxiv.org/abs/2007.12941v1
• Date: Sat, 25 Jul 2020 14:28:48 GMT
• Title: Learning Variational Data Assimilation Models and Solvers
• Authors: Ronan Fablet, Bertrand Chapron, Lucas. Drumetz, Etienne Memin, Olivier Pannekoucke, Francois Rousseau
• Abstract summary: We introduce end-to-end neural network architectures for data assimilation. 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.
• Score: 34.22350850350653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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