Data assimilation

Thursday, February 22, 2018 - 8:30am - 9:10am
Matthias Katzfuss (Texas A & M University)
The ensemble Kalman filter (EnKF) is a computational technique for approximate inference on the state vector in spatio-temporal state-space models. It has been successfully used in many real-world nonlinear data-assimilation problems with very high dimensions, such as weather forecasting. However, the EnKF is most appropriate for additive Gaussian state-space models with linear observation equation and without unknown parameters.
Friday, September 8, 2017 - 9:35am - 10:10am
Tan Bui-Thanh (The University of Texas at Austin)
We cast data assimilation problem into a model inadequacy problem which is then solved by a Bayesian approach. The Bayesian posterior is then used for Bayesian Optimal Experimental Design (OED). Our focus is on the A- and D-optimal OED problems for which we construct scalable approximations that involve: 1) randomized trace estimators; 2) Gaussian quadratures; and 3) trace upper bounds. Unlike most of contemporary approaches, our methods work directly with the inverse of the posterior covariance, i.e.
Tuesday, March 15, 2016 - 10:30am - 11:00am
Chunming Wang (University of Southern California)
Assimilation of observation data in meteorology and space weather consists of using these data to estimate the current state and the spatially and temporally distributed parameters of Numerical Weather Prediction (NWP) models, which are often fluid dynamical equations. The aim of the data assimilation is to provide wider monitoring of the weather condition beyond the locations where data are collected, also referred to as now-casting and to provide forecasting of weather conditions using NWP.
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