EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Dynamically informed covariance modelling in data assimilation

Ross Bannister1 and Ruth Petrie2
Ross Bannister and Ruth Petrie
  • 1University of Reading, Meteorology, Reading, United Kingdom of Great Britain – England, Scotland, Wales (
  • 2Centre for Environmental Data Analysis, Rutherford Appleton Laboratory, Harwell, United Kingdom of Great Britain – England, Scotland, Wales (
Data assimilation systems are progressively getting better, resulting in improved analyses and forecasts. One important reason for this is thought to be the improved representation of the multivariate PDF of a-priori errors seen by the assimilation. This means that observations can influence the trajectory/ies of the numerical model in more physically meaningful ways. While some improvement is gained by modelling deviations of the PDF from Gaussianity, and by statistical modelling of Gaussian covariances with ensembles, there is still scope to improve the structure of the `B-matrix' used in pure and hybrid versions of 3D/4D-Var.
Our hypothesis is that a good B-matrix for geophysical data assimilation applications should have multivariate structure functions that reflect the dynamics of the underlying physical system. So, if the underlying system is close to some balanced manifold, then the assimilation should not disturb that property. Existing practice is to impose any balances explicitly, but this is difficult when the balances are weak or difficult to determine, such as in convective-scale or tropical applications, etc. In this talk we look at how such covariances can be modelled, including an approach that uses the normal modes of the underlying dynamics.

How to cite: Bannister, R. and Petrie, R.: Dynamically informed covariance modelling in data assimilation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1778,, 2021.