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: