Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model

Abstract

Abstract. The Ensemble Kalman filter (EnKF) assimilation method is applied to the tracer transport using the same stratospheric transport model as in the 4D-Var assimilation system BASCOE. This EnKF version of BASCOE was built primarily to avoid the large costs associated with the maintenance of an adjoint model. The EnKF developed in BASCOE accounts for two adjustable parameters: a parameter α controlling the model error term and a parameter r controlling the observational error. The EnKF system is shown to be markedly sensitive to these two parameters, which are adjusted based on the monitoring of a χ2-test measuring the misfit between the control variable and the observations. The performance of the EnKF and 4D-Var versions was estimated through the assimilation of Aura-MLS ozone observations during an 8 month period which includes the formation of the 2008 Antarctic ozone hole. To ensure a proper comparison, despite the fundamental differences between the two assimilation methods, both systems use identical and carefully calibrated input error statistics. We provide the detailed procedure for these calibrations, and compare the two sets of analyses with a focus on the lower and middle stratosphere where the ozone lifetime is much larger than the observational update frequency. Based on the Observation-minus-Forecast statistics, we show that the analyses provided by the two systems are markedly similar, with biases smaller than 5% and standard deviation errors smaller than 10% in most of the stratosphere. Since the biases are markedly similar, they have most probably the same causes: these can be deficiencies in the model and in the observation dataset, but not in the assimilation algorithm nor in the error calibration. The remarkably similar performance also shows that in the context of stratospheric transport, the choice of the assimilation method can be based on application-dependent factors, such as CPU cost or the ability to generate an ensemble of forecasts.

Evaluation of Analysis by Cross-Validation, Part II: Diagnostic and Optimization of Analysis Error Covariance

“…Ensemble Kalman filters seem, however, much better in that regard although they have their own issues with localization and inflation. Experiments with chemical data assimilation using an ensemble Kalman filter does gives χ 2 /N s values very close to unity after simple adjustments for observation and model error variances [27]. We thus argue that ensemble methods, such as the ensemble Kalman filter, would produce analysis error variance estimates that are much more consistent between the different diagnostics.…”

Data assimilation: making sense of Earth Observation

“…Following on from these efforts, chemical data assimilation has been used to test chemical theories (Lary et al, 2003;Marchand et al, 2003Marchand et al, , 2004; study transport processes (Cathala et al, 2003;Semane et al, 2007;Barret et al, 2008;El Amraoui et al, 2008;Barré et al, 2012Barré et al, , 2013; extract wind information from constituent information (Riishøjgaard, 1996;Hólm et al, 1999;Peuch et al, 2000;Semane et al, 2009); produce analyses of chemical species, including ozone, NO 2 , NO x (NO+NO 2 ), CH 4 , N 2 O, CO, CO 2 , water vapor and aerosols (Fonteyn et al, 2000;Errera and Fonteyn, 2001;Chipperfield et al, 2002;El Amraoui et al, 2004;Arellano et al, 2007;Errera et al, 2008;Chai et al, 2009;Engelen et al, 2009;Tangborn et al, 2009;Thornton et al, 2009;Miyazaki et al, 2012Miyazaki et al, , 2014Miyazaki and Eskes, 2013-for a representative list of references for ozone see section Ozone Data Assimilation); and design constituent measurement strategies (Khattatov et al, 2001). There have been efforts to improve the chemical data assimilation methodology, including representation of the background errors (Constantinescu et al, 2007a;Singh et al, 2011;Errera and Ménard, 2012); assessment of technical aspects of the chemical model, e.g., adjoint sensitivity (Sandu et al, 2003(Sandu et al, , 2005; and comparison of assimilation methods, e.g., 4D-Var vs. EnKF (Skachko et al, 2014)...…”

Precipitation Data Assimilation System Based on a Neural Network and Case-Based Reasoning System

“…In these studies, most scientists optimize the initial conditions by constructing a cost function and find the extreme of this cost function [23], finally obtaining optimization by obtaining a, or a set of maximum possible state/s, and we call this the Data Assimilation method. Although there are a lot of data assimilation methods mentioned above, essentially all these methods to solve this problem use statistical and mathematical analysis to find the final solution [24,25]. However, for an effective DAS algorithm, it would be hard to carry out operationally because of the large amount of calculation and some technical problems.…”