Improving ICON-DREAM Cloud Information through AI-driven Data Assimilation

Accurate cloud information is essential for short-range weather prediction, yet remains a major source of uncertainty in limited-area ensemble systems such as ICON-DREAM. In particular, cloud cover forecasts are affected by model resolution constraints and simplified cloud representations, limiting their skill at nowcasting time scales.

 

Geostationary satellite observations provide frequent and spatially detailed information on cloud evolution, offering valuable constraints for improving cloud-related model fields. However, the direct integration of such observations into numerical weather prediction systems is computationally demanding and often not feasible for high-frequency updates.

 

In this contribution, we present an AI-driven data assimilation approach that improves cloud cover information in ICON-DREAM through a variational post-processing framework. The method combines concepts from variational data assimilation with graph neural networks to explicitly account for spatial dependencies in cloud fields. Background forecasts from ICON-DREAM are represented on a spatial graph, while satellite-derived cloud information from SEVIRI is incorporated via a loss function that balances consistency with observations, consistency with the model background, and spatial regularisation.

The framework is trained on historical forecast–observation pairs and evaluated for very short-range lead times of up to several hours. The results demonstrate a systematic improvement in cloud cover forecasts compared to the raw ICON-DREAM output for short-range lead times.

 

These findings highlight the potential of AI-driven data assimilation concepts to enhance cloud information in ensemble prediction systems without modifying the underlying numerical model, and illustrate a flexible pathway for exploiting satellite observations in very short-range forecasting applications.