Reformulates ML weather model rollout training as 4DVar in autoencoder latent space to approximate cross-variable error covariances and improve physical realism in long-term forecasts.
Generative emulation of chaotic dynamics with coherent prior.Computer Methods in Applied Mechanics and Engineering, 448:118410, January 2026
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Learning more physically realistic dynamics in machine-learning based weather forecasting with latent-space constraints
Reformulates ML weather model rollout training as 4DVar in autoencoder latent space to approximate cross-variable error covariances and improve physical realism in long-term forecasts.