Official description

Data assimilation involves combining observations with model output to obtain a consistent, evolving 3-dimensional picture of the atmosphere. This process is used to generate an initial state for producing forecasts at operational weather forecast centers. Data assimilation can also provide added value to observations by filling in data gaps and inferring information about unobserved variables. In this course, common methods of data assimilation (optimal  interpolation, Kalman filtering, variational methods) are introduced and derived in the context of estimation theory. A hands-on approach will be taken so that methods introduced in the lectures will be implemented in computer assignments using toy models.

Additional information

References

1. Rodgers, C., 2000, "Inverse Methods for Atmospheric Sounding", World Scientific Publishing 2. Kalnay, E., 2003, "Atmospheric Modeling, Data Assimilation and Predictability", Cambridge University Press.
2. Lahoz, W. A., B. Khattatov, and R. Menard, 2010, "Data Assimilation, Making Sense of Observations”, Springer.
3. Ismail-Zadeh, A., F. Castelli, D. Jones, and S. Sanchez, 2023, "Applications of Data Assimilation and Inverse Problems in the Earth Sciences", Cambridge University Press.

Grading

Computer assignments 50%, project 50%.