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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
06 Apr 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).
Nonlinear effects in 4D-Var
Massimo Bonavita, Peter Lean, and Elias Holm European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, RG2 9AX, UK
Abstract. The ability of a data assimilation system to deal effectively with nonlinearities arising from the prognostic model or the relationship between the control variables and the available observations has received a lot of attention in theoretical studies based on very simplified test models. Less work has been done to quantify the importance of nonlinearities in operational, state-of-the-art global data assimilation systems. In this paper we analyse the nonlinear effects present in ECMWF 4D-Var and evaluate the ability of the incremental formulation to solve the nonlinear assimilation problem in a realistic NWP environment. We find that nonlinearities have increased over the years due to a combination of increased model resolution and the ever-growing importance of observations that are nonlinearly related to the state. Incremental 4D-Var is well suited for dealing with these nonlinear effects, but at the cost of increasing the number of outer loop relinearisations. We then discuss strategies for accommodating the increasing number of sequential outer loops in the tight schedules of operational global NWP.
Citation: Bonavita, M., Lean, P., and Holm, E.: Nonlinear effects in 4D-Var, Nonlin. Processes Geophys. Discuss.,, in review, 2018.
Massimo Bonavita et al.
Massimo Bonavita et al.
Massimo Bonavita et al.


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Short summary
This paper deals with the effects of nonlinearity in a state of the art atmospheric global data assimilation systems. It is shown that these effects have become increasingly important over the years due to increased model resolution and use of nonlinear observations. The ability to deal with nonlinearities has thus become a crucial asset of data assimilation algorithms. At ECMWF this is done in a perturbative fashion. Advantages and limitations of this technique are discussed.
This paper deals with the effects of nonlinearity in a state of the art atmospheric global data...