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 util...

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Vydané v:	Quarterly journal of the Royal Meteorological Society Ročník 150; číslo 761; s. 2273 - 2295
Hlavní autori:	Melinc, Boštjan, Zaplotnik, Žiga
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Chichester, UK John Wiley & Sons, Ltd 01.04.2024 Wiley Subscription Services, Inc
Predmet:	3D‐Var analysis increments background errors Computation Data assimilation Data collection Kalman filters Lower troposphere machine learning neural network Neural networks Troposphere variational autoencoder
ISSN:	0035-9009, 1477-870X
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