Data Assimilation Meetings at Reading
| Date | Meeting type | Speakers |
| 30 October 2013 | Internal Speaker |
Bertrand Bonan (University of Reading) An ETKF approach for initial state and parameter estimation in ice sheet modelling (pdf) A hot topic in ice sheet modelling is to run prognostic simulations over the next 100 years to investigate the impact of Antarctica and Greenland ice sheets on sea level change. Such simulations require an initial state of ice sheets which must be as close as possible to what is currently observed. Large scale ice sheet dynamical models are mostly governed by the following input parameters and variables: basal dragging coefficient, bedrock topography, surface elevation, temperature field. But we do not have satisfying initial states for simulations. Fortunately, some observations are available such as surface and (sparse) bedrock topography, surface velocities, surface elevation trend. The use of inverse methods appears to be the adequate tool to produce satisfying initial states. We use an Ensemble Transform Kalman Filter (ETKF) to infer optimal actual states for ice sheet model initialisation thanks to available observations. As we first want to assess the validity of the method we begin with twin experiments (simulated observations) with a simple flowline large scale model, Winnie, as a first step toward data assimilation for a full 3D ice sheet model. Winnie is an ice sheet model using shallow ice approximation and a basal sliding law. As a flowline model it is a good prototype to valide our methods. Here we try here to retrieve the prescribed following input parameters and variables: basal sliding coefficients, bedrock topography and ice thickness thanks to our simulated observations of surface elevation, surface velocities and sparse bedrock topography. We also run several diagnostics to assess the quality of the recovered parameters. |