The group's research field is the development of new forecasting methods for the sea, sea ice and climate, based on the best possible combination of models and observations. Data assimilation is a way of taking measured data from the real world into models, so that the model's calculations become more precise.

The group has specialist expertise in the development and use of data assimilation methods, as well as the use of machine learning and other forms of artificial intelligence. The “Ensemble Kalman filter” is a data assimilation method which was developed at the Nansen Center and which is used in forecasting systems worldwide.

Being able to reliably predict changes in climate, ocean, and sea-ice conditions is of great importance, both within research and in various business fields such as e.g., climate adaptation, seafood production or ship traffic. By developing new methods that incorporate machine learning and data assimilation methods, we can make such forecasts more precise.

Data assimilation is used both when improving models that produce forecasts for future conditions, and for reanalyzes of the past. For example, when mapping how climatic conditions have developed over the past 30 years, data assimilation must be used to be able to carry out and deliver reliable calculations.