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arXiv · 2101.02121

Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation

Julian Mack·Rossella Arcucci·Miguel Molina-Solana·Yi-Ke Guo
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Abstract

We propose a new 'Bi-Reduced Space' approach to solving 3D Variational Data Assimilation using Convolutional Autoencoders. We prove that our approach has the same solution as previous methods but has significantly lower computational complexity; in other words, we reduce the computational cost without affecting the data assimilation accuracy. We tested the new method with data from a real-world application: a pollution model of a site in Elephant and Castle, London and found that we could reduce the size of the background covariance matrix representation by O(10^3) and, at the same time, increase our data assimilation accuracy with respect to existing reduced space methods.

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Julian Mack, Rossella Arcucci, Miguel Molina-Solana, Yi-Ke Guo. 2021-01-06. Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation. https://doi.org/10.1016/j.cma.2020.113291

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