The very best way to make a forecast out of a numerical model and a set of observations.
International leadership in data assimilation, from theory to state-of-the-art applications within ocean and climate. Presently at the forefront of applications of the Ensemble Kalman Filter (EnKF)
Contribute to operational oceanography (TOPAZ: forecast and reanalysis) and climate sciences (NorCPM: Seasonal to decadal predictions) with forecasts and reanalysis of the highest standard for the Arctic Ocean, sea ice, ecosystem and the coupled climate system.
Contribute to the improvement of modelling and observation techniques, by testing hypotheses on their respective uncertainties.
Target & priorities
Provide validated reanalyses and forecasts for the operational oceanography and climate community with a focus on the Arctic and the Nordic Seas. E.g., a 30-years Arctic ice-ocean-ecosystem reanalysis and a global climate reanalysis (1850-present), both including residual uncertainty estimates.
Provide a data assimilation framework able to take up new models and observations at the frontiers of technology. E.g., assimilation in the Lagrangian neXtSIM model, coupled data assimilation in NorCPM, observations with unconventional error characteristics such as satellite-borne sea ice thickness.
Favour the emergence of a new ocean modeling group at NERSC able to take up the latest ocean, sea ice and ecosystem model research into our flagship integrated systems. E.g., integration of neXtSIM and ECOSMO into TOPAZ.
Extend the theory of data assimilation for non-linear chaotic systems (assimilation in the unstable subspace, improved stochastic model). E.g., proposing new algorithms improving the performance of the Ensemble Kalman Filter.
Pursue innovative applications of data assimilation with high societal impacts.
Associated group members:
- François Counillon
- Madlen Kimmritz
- Yiguo Wang
- Jiping Xie
- Bjørn Backeberg
|Name||Area of Expertise|
|Abhishek S. Shah||mathematics|
|Patrick N. Raanes||mathematics|