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 - please see the draft timetable for more details.
- 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
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.
Introductory Data Assimilation Course at the University of Reading
The National Centre for Earth Observation and the Data Assimilation Research Centre will run their data assimilation courses this year in conjunction with us. The course will run from 5-8 March 2019 at the University of Reading campus.
An Introduction to Advanced Data Assimilation Methods
This course will provide an introduction of all data assimilation methods used in numerical weather prediction, including the latest developments. Participants will get the opportunity to apply data-assimilation methods to small and large numerical models, to obtain hands-on experience with the different methods and deepen understanding of their working and limitations. For those unfamiliar with data assimilation, or those who are keen to obtain a deeper understanding, this course is a solid preparation for the main ECMWF course.
Course details and further information about the application procedure can be found on the Data Assimilation Research Centre's training pages.