Data Assimilation
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Course Description
Dynamic systems and state-space representation. Kalman Filter (KF). KF for large systems, like the Ensemble KF, the Compressed State KF, and others. Other approaches to data assimilation. Estimation of filter hyperparameters and testing the optimality for optimality. Computational issues and practical challenges. Examples from Hydrology, Meteorology, and Hydrodynamics that involve many state variables.
Grading Basis
ROP - Letter or Credit/No Credit
Min
3
Max
3
Course Repeatable for Degree Credit?
No
Course Component
Lecture
Enrollment Optional?
No
Programs
CEE261D
is a
completion requirement
for: