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Forecasting non-linear systems using the ensemble Kalman filter and related methods

Tildelt: kr 20,3 mill.

The project will develop new assimilation techniques related to the Ensemble Kalman Filter and apply them in various research fields in view of operational forecasting of non-linear dynamical systems. The disciplines targetted are the following: - Physic al oceanography (MSC: TOPAZ sea-ice): An parameter estimation extension of the EnKF will be developed in the context of assimilation of sea-ice drift in the Arctic Ocean. - Biological oceanography (MSC: TOPAZ-ECO system): An extension of the EnKF for ass imilation of non-Gaussian variables will be developed for a 3D coupled physcial-ecosystem model of the Atlantic and Arctic Oceans. - Weather forecasting (Met.no: HIRLAM model, Storm: MM5/WRF model). Met.no will conduct EnKF-related modifications of an ex isting variational data assimilation system, Storm will directly use the EnKF for forecasting targetted local weather phenomena in Northern Norway. - Oil reservoir forecasting (MSC/Hydro: Eclipse). Develop new EnKF-based methods general problems of oil r eservoir models. An internal project at Hydro will complement with practical applications and demonstrate the use of probabilisitc production forecasts with oil fields in operations. - Bioeconomic management (NHH) sustainable management of marine resourc es with a stochastic market and stochastic biology. - Health (Ecole des Mines de Paris): Analysis or early warning of epidemic outbreaks in France based on the Sentinelles network. These disciplines share some challenging aspects related to the non-linea rity of the system (biases, non-Gaussian distributions). Some of them are high-dimensional and some are low-dimensional, the latter will therefore be used as a laboratory for assessing new developments against advanced Monte Carlo methods.