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

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 witho...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering Vol. 372; p. 113291
Main Authors: Mack, Julian, Arcucci, Rossella, Molina-Solana, Miguel, Guo, Yi-Ke
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2020
Subjects:
Attention networks
Convolutional Autoencoders
Variational Data Assimilation
Convolutional Autoencoders
Attention networks
Variational Data Assimilation
ISSN:0045-7825, 1879-2138
Online Access:Get full text
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Summary: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 our proposal with data from a real-world application: a pollution model of a site in Elephant and Castle (London, UK) and found that we could (1) reduce the size of the background covariance matrix representation by O(103), and (2) increase our data assimilation accuracy with respect to existing reduced space methods. •We propose a new ‘Bi-Reduced Space’ approach to solving 3D Variational Data Assimilation.•We use Convolutional Autoencoders to create the reduced space for solving 3D Var.•We prove that our approach has the same solution as previous methods for reducing 3D Var space.•We prove that our approach has significantly lower computational complexity of previous methods.•We tested the new method with data from a real-world application: a pollution model in London.
ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2020.113291