Key points are not available for this paper at this time.
Abstract In this paper, the operational 3D variational data assimilation system (3D‐var) of the Canadian Meteorological Centre (CMC) is described and its performance is compared to that of the previously operational statistical interpolation analysis. Deliberately configuring the 3D‐var to be as close as possible to the statistical interpolation system permits an evaluation of the impact of data selection on both the analysis and the resulting forecasts. The current implementation of the 3D‐var is incremental in the horizontal and the vertical since the analysis increments are constructed at a lower horizontal resolution on prescribed pressure levels. They are subsequently interpolated vertically to the σ levels of the model. The results show that although there could be significant differences in the single analysis increments, the impact on the resulting forecasts is neutral. The 3D‐var implements a multivariate covariance model implicitly through changes of variables. It is shown that the implicit covariances resulting from using the linear balance relationship leads to correlations that are not compactly supported. Using a “local” balance relationship has enabled us to avoid this problem when background‐error variances vary fully in physical space. There are also indications that the 3D‐var analysis increments are better balanced than those of the statistical interpolation. This implies that the 3D‐var analysis may not need to undergo a nonlinear normal mode initialization. A brief description is given of the work going on at the Atmospheric Environment Service to improve the 3D‐var making possible the direct assimilation of new types of data from a variety of instruments.
Gauthier et al. (Tue,) studied this question.