Annual Seminar 2026

Advancing the assimilation of Earth system observations with new methodology and Machine Learning Data Assimilation continues to underpin all operational weather forecasting, initialising physics-based models as well as initialising and training data-driven models. The 2026 Annual Seminar will look at how the deployment of Machine Learning (ML) technology and innovative methodology is changing the way we assimilate observations for numerical weather prediction, atmospheric composition and...

Sea ice data assimilation in ORAS6

Abstract. Accurate weather and climate forecasting relies heavily on the precise modeling of sea ice, a critical component of the Earth's climate system. Sea ice influences global weather patterns, ocean circulation, and the exchange of heat and moisture between the atmosphere and oceans. Initialisation of the sea ice component of global coupled models relies on data assimilation techniques to incorporate information from observations to constrain the system. This study focuses on the development of sea ice data assimilation for ECMWF’s latest Ocean Reanalysis System 6 (ORAS6) that includes a multicategory sea ice model. The research addresses the challenge of appropriately distributing sea ice concentration increments across various thickness categories in the model. Here, we show that using a simple proportional increment splitting method improves the accuracy of sea ice concentration analyses compared to previous approaches. Our findings indicate that adding an additional sea ice-sea water temperature balance brings further performance benefits. These results suggest that the choice of increment distribution strategy significantly impacts the accuracy of sea ice representation in reanalysis systems. The system presented here will form the basis of ECMWF's data assimilation system for numerical weather prediction, as well as the next generation coupled reanalyses.

Internships | Computational and Information Systems Lab