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

, but it also reflects the fact that they are more integrated measures. Relative to their numbers, the DFS of Satwind and GPSRO measurements are high, which can be explained by the fact that they are given a large confidence and also because they are present in data-sparse areas like over the oceans. In the Northern Hemisphere, the largest DFS are clearly for Radiosounding data, where they are quite numerous, followed by Aircraft and Brightness temperature data ( Fig. 7 , middle panel). Despite the

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Derek J. Posselt and Tomislava Vukicevic

experiments has been to account for it as a source of model error in the assimilation system and to produce a more accurate estimate of the state of the atmosphere. The principle that underlies sensitivity studies and data assimilation experiments is the fact that a functional relationship exists between parameters and model state. The extent to which this relationship can be quantified has been termed “parameter identifiability” in the ensemble data assimilation literature ( Anderson 2001 ; Aksoy et al

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Olivier Pannekoucke

1. Introduction Data assimilation aims to estimate the most likely numerical representation of a real system from known observations. This state is called the analysis and corresponds to the initial state of a new forecast. To estimate the analysis is quite a difficult problem for the atmosphere where observations are heterogeneous in time and space, and also because these observations are affected by noise. A prediction/correction method is often used so that the analysis is designed as a

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