Data assimilation involves combining observations with model output to obtain a consistent, evolving 3-dimensional state of the model. In the last 20 years data assimilation has gained center stage in many computational disciplines at both universities and research centers starting with geoscience applications.
In this course, common methods of data assimilation (Kalman filtering, ensemble Kalman filter and variational methods) are introduced and derived in the context of both variational and estimation theory with emphasis on computational aspects.
Novel aspects of data assimilation using reduced order modeling will be introduced and examples presented.
A hands-on approach will be taken so that methods introduced in the lectures will be implemented in computer assignments using toy models and material of the research of the lecturer and other experts in the field.