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3D-Var data assimilation using a variational autoencoder
ID
Melinc, Boštjan
(
Author
),
ID
Zaplotnik, Žiga
(
Author
)
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MD5: 81202288C58BE54A9BED5B6C5F2A4B5A
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https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.4708
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Abstract
Data assimilation of atmospheric observations traditionally relies on variational and Kalman filter methods. Here, an alternative neural network data assimilation (NNDA) with variational autoencoder (VAE) is proposed. The three-dimensional variational (3D-Var) data assimilation cost function is utilised to determine the analysis that optimally fuses simulated observations and the encoded short-range persistence forecast (background), accounting for their errors. The minimisation is performed in the reduced-order latent space discovered by the VAE. The variational problem is autodifferentiable, simplifying the computation of the cost-function gradient necessary for efficient minimisation. We demonstrate that the background-error covariance (B) matrix measured and represented in the latent space is quasidiagonal. The background-error covariances in the grid-point space are flow-dependent, evolving seasonally and depending on the current state of the atmosphere. Data assimilation experiments with a single temperature observation in the lower troposphere indicate that the B matrix describes both tropical and extratropical background-error covariances simultaneously.
Language:
English
Keywords:
meteorology
,
data assimilation
,
machine learning
,
neural networks
,
variational autoencoder
,
3D-Var
,
analysis increments
,
background errors
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FMF - Faculty of Mathematics and Physics
Publication status:
Published
Publication version:
Version of Record
Year:
2024
Number of pages:
Str. 2273–2295
Numbering:
Vol. 150, iss. 761, pt. B
PID:
20.500.12556/RUL-158318
UDC:
551.5
ISSN on article:
0035-9009
DOI:
10.1002/qj.4708
COBISS.SI-ID:
194564099
Publication date in RUL:
05.06.2024
Views:
202
Downloads:
52
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Record is a part of a journal
Title:
Quarterly Journal of the Royal Meteorological Society
Shortened title:
Q. J. R. Meteorol. Soc.
Publisher:
Royal Meteorological Society
ISSN:
0035-9009
COBISS.SI-ID:
26227200
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Secondary language
Language:
Slovenian
Keywords:
meteorologija
,
asimilacija meritev
,
strojno učenje
,
nevronske mreže
,
variacijski avtokodirnik
,
3D-Var
Projects
Funder:
ARIS - Slovenian Research and Innovation Agency
Project number:
P1-0188
Name:
Astrofizika in fizika atmosfere
Funder:
ARIS - Slovenian Research and Innovation Agency
Funding programme:
Young researchers
Funder:
EC - European Commission
Funding programme:
Destination Earth
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