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.
@article{arxiv.2101.02121,
title = {Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation},
author = {Julian Mack and Rossella Arcucci and Miguel Molina-Solana and Yi-Ke Guo},
journal= {arXiv preprint arXiv:2101.02121},
year = {2021}
}
Comments
Published in Computer Methods in Applied Mechanics and Engineering in Dec 2020