20.500.12556/RUL-158318 3D-Var data assimilation using a variational autoencoder 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. meteorology data assimilation machine learning neural networks variational autoencoder 3D-Var analysis increments background errors meteorologija asimilacija meritev strojno učenje nevronske mreže variacijski avtokodirnik 3D-Var true false true Angleški jezik Slovenski jezik Članek v reviji 2024-06-05 09:39:25 2024-06-05 09:39:26 2024-06-26 03:39:37 0000-00-00 00:00:00 2024 0 0 Str. 2273–2295 iss. 761, pt. B Vol. 150 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 551.5 0035-9009 10.1002/qj.4708 194564099 Quart_J_Royal_Meteoro_Soc_-_2024_-_Melinc_-_3D‐Var_data_assimilation_using_a_variational_autoencoder.pdf Quart_J_Royal_Meteoro_Soc_-_2024_-_Melinc_-_3D‐Var_data_assimilation_using_a_variational_autoencoder.pdf 1 81202288C58BE54A9BED5B6C5F2A4B5A 907df5a5ab8bb34224042aa029deb4f3c3892157d740042004a007a8b7c0a7b0 4301b205-2346-11ef-b111-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=185910 https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.4708 0 0b8d2f5f-230f-11ef-b111-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=185880 Fakulteta za matematiko in fiziko 0 0 0