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

01/06/2021
by   Julian Mack, et al.
22

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset