Related papers: Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation

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

URL: http://arxiv.org/abs/2101.02121v1
Date: Wed, 6 Jan 2021 16:23:58 GMT
Title: Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation
Authors: Julian Mack, Rossella Arcucci, Miguel Molina-Solana and Yi-Ke Guo
Abstract 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.
Score: 11.143409762586638
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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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