Three-dimensional variational data assimilation for a limited area model. Part II: Observation handling and assimilation experiments

Année de publication

2001

A 3-dimensional variational data assimilation (3D-Var) scheme for the <br>High Resolution Limited Area Model (HIRLAM) forecasting system is <br>described. The HIRLAM 3D-Var is based on the minimisation of a cost <br>function that consists of one term, <em>J</em>_b, which <br>measures the distance between the resulting analysis and a background <br>field, in general a short-range forecast, and another term, <em>J</em>_o, which measures the distance between the analysis and the observations. This paper is concerned with <em>J</em>_o<br> and the handling of observations, while the companion paper by <br>Gustafsson et al. (2001) is concerned with the general 3D-Var <br>formulation and with the <em>J</em>_b term. Individual system <br>components, such as the screening of observations and the observation <br>operators, and other issues, such as the parallelisation strategy for <br>the computer code, are described. The functionality of the observation <br>quality control is investigated and the 3D-Var system is validated <br>through data assimilation and forecast experiments. Results from <br>assimilation and forecast experiments indicate that the 3D-Var <br>assimilation system performs significantly better than two currently <br>used HIRLAM systems, which are based on statistical interpolation. The <br>use of all significant level data from multilevel observation reports is<br> shown to be one factor contributing to the superiority of the 3D-Var <br>system. Other contributing factors are most probably the formulation of <br>the analysis as a single global problem, the use of non-separable <br>structure functions and the variational quality control, which accounts <br>for non-Gaussian observation errors.</div>

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