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.
The quiet revolution of numerical weather prediction.Nature, 525(7567):47–55, September 2015
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.