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Seung-Jong Baek, Istvan Szunyogh, Brian R. Hunt, and Edward Ott

temperature error in that region, because it allows the analysis, and the ensuing model forecast to shift in the direction of the model attractor. 5. Conclusions In this study, we evaluated the performance of bias model II introduced in Baek et al. (2006) to account for the model bias in an ensemble based data assimilation scheme. We carried out experiments in an idealized setting and focused on accounting for the bias in one particular model state variable, the surface pressure. This variable was

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Malaquias Peña, Zoltan Toth, and Mozheng Wei

“inflation” factor to improve the analysis. Corazza et al. (2002) add random values to a background error covariance matrix spanned by bred vectors before integrating the model forward. This additive noise is interpreted by Corazza et al. (2002) as a procedure to refresh the bred vectors and prevent collapsing into one dominant direction. Corazza et al. (2007) , Whitaker et al. (2008) , and Yang et al. (2009) combine additive noise with a localization treatment for better analysis performance

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Derek J. Posselt and Tomislava Vukicevic

evaluation of the relationship between changes to parameters and the effect on model output. We note the pros and cons of our choice of observations later in section 5 . Thorough examination of parameter–state relationships necessitates perturbation of all parameters simultaneously; it is the relationships between parameters and their joint effect on model output that is of interest. Because this is a computationally demanding exercise, it is useful to briefly explore the model sensitivity in a more

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Andrew Tangborn, Robert Cooper, Steven Pawson, and Zhibin Sun

sources using MOPITT measurements. Geophys. Res. Lett. , 31 , L01104 . doi:10.1029/2003GL018609 . Arellano , A. F. , and Coauthors , 2007 : Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission. Atmos. Chem. Phys. , 7 , 5695 – 5710 . Auger , L. , and A. V. Tangborn , 2004 : A wavelet-based reduced rank Kalman filter for assimilation of stratospheric chemical tracer observations. Mon. Wea. Rev. , 132 , 1220 – 1237 . Cohn

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Junjie Liu, Hong Li, Eugenia Kalnay, Eric J. Kostelich, and Istvan Szunyogh

) and in the appendix . 4. Results We evaluate the performance of the humidity runs by comparing the accuracy of the analyses and forecasts from these runs with those from the control run. These comparisons show the impact of the different ways of humidity data assimilation (i.e., passive q , univariate q , and multivariate q ) on the analyses and forecasts of the humidity and the other dynamical variables. a. Global mean analysis accuracy Figure 2 shows the time evolution of the global average

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Marc Bocquet

greenhouse gases and their relative balance ( Pachauri and Reisinger 2007 ). The first one, in terms of impact, is carbon dioxide. From climate theory, it is therefore crucial to evaluate precisely the fluxes and exchange of carbon on earth. Therefore, biogeochemists have built up inventories of fluxes that are still uncertain, especially biogenic emissions and uptakes. A complementary approach is to use partial flux data and carbon dioxide concentration measurements and, through inverse modeling and

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Chris Snyder, Thomas Bengtsson, Peter Bickel, and Jeff Anderson

-based assimilation methods of interest in geophysical applications. [See Gordon et al. (1993) or Doucet et al. (2001) for an introduction.] In their simplest form, particle filters calculate posterior weights for each ensemble member based on the likelihood of the observations given that member. Like the EnKF, particle filters are simple to implement and largely independent of the forecast model, but they have the added attraction that they are, in principle, fully general implementations of Bayes’s rule and

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