Overview

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 a couple of 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
  • Oceanographic applications and atmospheric chemical assimilation
  • Analysis of soil moisture, soil temperature, snow and sea surface temperature
  • The global observing system, with emphasis on how to use satellite observations
  • Bias correction, quality control and diagnostics

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.

Pre and Post Data Assimilation Courses 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.  Participants can attend one or both of the courses. Note that the post-course session runs at the same time as the EUMETSAT/ECMWF Satellite Data Assimilation course.

Pre-course: AN Introduction to Data Assimilation Methods (23-24 March 2017)

This is an intensive course on the basics of data assimilation and how all data assimilation methods are connected. Key topics covered:

  • Bayes Theorem
  • Introduction to Variational Methods
  • Introduction to Ensemble Kalman Filters
  • Nonlinear Data Assimilation
  • Hybrid Methods

Post-Course: computing "hands on" course (3-4 April 2017)

This is an intensive two-day course applying data assimilation methods to small and large models. Key topics covered in the practical sessions are:

  • Variational Methods
  • Ensemble Kalman Filters
  • Particle Filters
  • Nonlinear Data Assimilation