University at Buffalo 3 credits Mechanical & Aerospace Engr
The Data assimilation (DA) course is an advanced level graduate course with focus on forecasting for large scale dynamical systems such as weather, tsunamis, epidemics, etc. i.e., any spatio-temporally varying dynamical system. The objectives are to develop a fundamental understanding of integrating measurement data with model data accounting for uncertainties in both the sources of data. Topics included in this course include: Review of linear and non-linear least squares; Tikhnov regularization and systematic approaches for the selection of the regularization parameter such as the L-curve and Mazarov’s approach; Total least squares, variable projection least squares, adjoint approach for determining gradients for optimization; 3Dvar, Kalman Filter, Extended Kalman Filter, Reduced Rank Filters such as Ensemble Kalman Filter, and Local Ensemble Kalman Filter.
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