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the background error is zero (e.g., Dee and Da Silva 1998 ). The contribution of model bias to the discrepancy between a background forecast and the true atmospheric state can be comparable to, or even larger than, the contribution of the growing part of the initial condition error. Model bias has many sources, such as the finite-resolution representation of the continuous atmospheric fields, limited knowledge, and imperfect representation of the subgrid physical processes, and imperfect
the background error is zero (e.g., Dee and Da Silva 1998 ). The contribution of model bias to the discrepancy between a background forecast and the true atmospheric state can be comparable to, or even larger than, the contribution of the growing part of the initial condition error. Model bias has many sources, such as the finite-resolution representation of the continuous atmospheric fields, limited knowledge, and imperfect representation of the subgrid physical processes, and imperfect
these observations in the analysis. Cardinali et al. (2004) have used the DFS to measure the analysis sensitivity to observations in a real size assimilation system. Rabier et al. (2002) have also shown how to use such a diagnostic to select Infrared Atmospheric Sounding Interferometer (IASI) channels in order to extract the useful information from the very large amount of data provided by this instrument. Fisher (2003b) has investigated the possibility to compute the total DFS brought by the
these observations in the analysis. Cardinali et al. (2004) have used the DFS to measure the analysis sensitivity to observations in a real size assimilation system. Rabier et al. (2002) have also shown how to use such a diagnostic to select Infrared Atmospheric Sounding Interferometer (IASI) channels in order to extract the useful information from the very large amount of data provided by this instrument. Fisher (2003b) has investigated the possibility to compute the total DFS brought by the
