3D‐Var data assimilation using a variational autoencoder
Boštjan Melinc,
Žiga Zaplotnik
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‐ord… Show more
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