Training course: Data assimilation

ECMWF | 24-28 February 2020

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 discussion and hands-on sessions - please see the draft timetable for more details.

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; for details see the corresponding tab on this page.

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.

Please note that no funds are available from ECMWF to support participants' attendance at training courses.

A course fee is payable by applicants who do not reside in an ECMWF Member or Co-operating State.