Three-dimensional variational data assimilation for a limited area model

Abstract

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