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

observations (black dots in Fig. 1 ) that are commonly used as verification data, the operational analyses have uniform coverage throughout the globe, which is essential to assess the impact of assimilating the AIRS humidity retrievals that have the highest concentration over the oceans. Two statistical quantities are used to show the difference in accuracy between the humidity runs and the control run. One is root-mean-square (rms) error ( Figs. 2 and 6 ), which shows the absolute magnitude of the

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Hong Li, Eugenia Kalnay, Takemasa Miyoshi, and Christopher M. Danforth

-right panel in Fig. 10 ), as if they were based only on satellite observations, resulting in uniform error fields (not shown). However, in reality there are more rawinsonde observations over land and fewer over the ocean. To investigate the sensitivity of each method to the choice of observational system, we assimilate a rawinsonde-like network (the upper-left panel in Fig. 10 ) for u , υ , T , q ( p s is still available everywhere) using pure additive inflation, the DdSM+, and the LDM+. We retune

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Gérald Desroziers, Loïk Berre, Vincent Chabot, and Bernard Chapnik

analysis scheme is based on a multivariate formulation ( Derber and Bouttier 1999 ), that includes a normalization in physical space of the vorticity covariances by a field of standard deviations of vorticity background errors. This allows us to introduce some heterogeneity of the covariances, which especially induces a larger weight of observations in the areas where the vorticity error standard deviations are large, like in extratropical areas over oceans. To make the fields of vorticity error

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