3D-Var Data Assimilation using a Variational Autoencoder
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913329457397760 |
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| author | Melinc, Boštjan Zaplotnik, Žiga |
| author_facet | Melinc, Boštjan Zaplotnik, Žiga |
| contents | 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 auto-differentiable, simplifying the computation of the cost function gradient necessary for efficient minimisation. We demonstrate that the background-error covariance ($\mathbf{B}$) matrix measured and represented in the latent space is quasi-diagonal. 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 $\mathbf{B}$-matrix simultaneously describes both tropical and extratropical background-error covariances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_16073 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | 3D-Var Data Assimilation using a Variational Autoencoder Melinc, Boštjan Zaplotnik, Žiga Atmospheric and Oceanic Physics 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 auto-differentiable, simplifying the computation of the cost function gradient necessary for efficient minimisation. We demonstrate that the background-error covariance ($\mathbf{B}$) matrix measured and represented in the latent space is quasi-diagonal. 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 $\mathbf{B}$-matrix simultaneously describes both tropical and extratropical background-error covariances. |
| title | 3D-Var Data Assimilation using a Variational Autoencoder |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2308.16073 |