This course was a remote event.
This five-day module focuses on describing data assimilation methods and general aspects of assimilating observations. Aspects of the implementation of the assimilation techniques for real-size numerical weather prediction (NWP) systems will also be described.
As well as lectures there will be discussion and hands-on sessions.
Main topics
- The fundamental data assimilation concepts
- Optimal Interpolation, 3D-Var, 4D-Var and the Kalman filter
- Ensemble Kalman Filter methods; Ensemble of Data Assimilations and uncertainty estimation; Hybrid variational/ensemble based methods
- Modelling of error covariances; handling of non-Gaussian errors
- The global observing system, with emphasis on how to use satellite observations
- Bias correction, quality control and diagnostics
- Applications of data assimilation methods for the land surface, ocean, atmospheric composition and reanalysis
Requirements
Participants should have a good meteorological and mathematical background, and in particular a good understanding of linear algebra. They are expected to be familiar with the contents of standard meteorological and mathematical textbooks.
If you are less familiar with data assimilation concepts, such as Bayes Theorem, you may wish to consider attending the University of Reading Introductory course, which runs the week before our course; see details below.
Introductory material not covered by the course can be found in our lecture note series.
Some practical experience in numerical weather prediction is an advantage.
All lectures will be given in English.