Four-dimensional variational data assimilation for Doppler radar wind data

Fathalla A. Rihan, Chris G. Collier, Ian Roulstone

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


All forecast models, whether they represent the state of the weather, the spread of a disease, or levels of economic activity, contain unknown parameters. These parameters may be the model's initial conditions, its boundary conditions, or other tunable parameters which have to be determined. Four dimensional variational data assimilation (4D-Var) is a method of estimating this set of parameters by optimizing the fit between the solution of the model and a set of observations which the model is meant to predict. Although the method of 4D-Var described in this paper is not restricted to any particular system, the application described here has a numerical weather prediction (NWP) model at its core, and the parameters to be determined are the initial conditions of the model. The purpose of this paper is to give a review covering assimilation of Doppler radar wind data into a NWP model. Some associated problems, such as sensitivity to small variations in the initial conditions or due to small changes in the background variables, and biases due to nonlinearity are also studied.

Original languageEnglish
Pages (from-to)15-34
Number of pages20
JournalJournal of Computational and Applied Mathematics
Issue number1
Publication statusPublished - Apr 1 2005
Externally publishedYes


  • 3D-Var
  • 4D-Var
  • Adjoint
  • Data assimilation
  • Doppler radar
  • NWP
  • Nonlinear bias
  • Sensitivity

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics


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