The 24th STUOD Sandbox meeting to be held in a hybrid mode on Friday, 30th June. The Sandboxes are special events, set in specially equipped audio-visual rooms, among Ifremer, Imperial, and Inria with all available tools to manipulate and test ideas.
The 24th meeting of the Sandbox Series will be devoted to Data Assimilation theory and application.
Chair: Dan Crisan
09.00am-9.50am Andrew Stuart (Caltech) on “The Ensemble Kalman Filter in the Near-Gaussian Setting“
Summary: The ensemble Kalman filter is widely used in applications because, for high dimensional filtering problems, it has a robustness that is not shared by the particle filter; in particular it does not suffer from weight collapse. However there is no theory which quantifies its accuracy, as an approximation of the true filtering distribution, except in the Gaussian setting. To address this issue we provide an analysis of the accuracy of the ensemble Kalman filter for problems where the filtering distribution is non-Gaussian. Our analysis is developed for the mean field ensemble Kalman filter. We rewrite this filter in terms of maps on probability measures, and then we introduce a weighted total variation metric in which these maps are locally Lipschitz. Using these stability estimates we demonstrate that, if the true filtering distribution is close to Gaussian after appropriate lifting to the joint space of state and data, then it is well approximated by the mean-field ensemble Kalman filter.
Chair: Etienne Memin
10.30am-10.50am Alex Lobbe (ICL) “Noise Calibration for the Stochastic Rotating Shallow Water Model”
10.50am-11.10am Maneesh Singh (ICL) “Towards nudging particle filter for Stochastic PDEs“
11.10am-11.30am Benjamin Dufee (Inria) on “Data assimilation for ensemble forecast in RKHS“
11.30am-11.50am Hamza Ruzayqat (King Abdullah University of Science and Technology) on “Sequential Markov Chain Monte Carlo for Lagrangian Data Assimilation with Applications to Unknown Data Locations”