Tasks
I am leading the Bjerknes CPU's RA2 - Data assimilation methods. My task is to make use of - or assimilate - existing observations so as to best reduce error in climate models and provide accurate probabilistic forecasts. There is a fine equilibrium to reach such an optimal without breaking down the consistency of the system... BOOOOOM!

What are you most excited about with this project?
Applying data assimilation to the climate system raises a great many new challenges compared to current standard application in weather forecasting. The climate system features many different scales, which interact in a complex and non-linear manner. Finding a way to optimally use sparse observations in modelling such a system can allow us to achieve a breakthrough in enhancing the skill of climate predictions, from seasons to decades ahead. We hope that these will become as essential and reliable as weather forecasting is for our society.

What do you see as your biggest challenge for now?
Dealing with the scale interaction is not the only bottleneck. The very large model bias (partly inherent to the misrepresentation of the scale interaction) makes application of our data assimilation method even more challenging. This very large topic is addressed with other side projects.

Related projects and committees

• NFR SFI Climate Futures
• JPI Ocean & JPI Climate ROADMAP
• NFR researcher project for young talents CoRea
• EU H2020 TRIATLAS
• EU H2020 Blue-Action
• ERC CoG STERCP
• EU H2020 INTAROS
• NFR Infrastructure INES
• NFR Seasonal Forecasting Engine

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