Three-dimensional variational data assimilation for a limited area model. Part II: Observation handling and assimilation experiments
Assimilation des données variationnelle et tri-dimensionnelle pour un modèle à domaine limité.
Lindskog, M. ; Gustafsson, N. ; Navascués, B. ; Mogensen, K.-S. ; Huang, X.-Y. ; Yang, X. ; Andrae, U. ; Berre, L. ; Thorsteinsson, S. ; Rantakokko, J.
Année de publication
2001
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, <em>J</em><sub>b</sub>, which
measures the distance between the resulting analysis and a background
field, in general a short-range forecast, and another term, <em>J</em><sub>o</sub>, which measures the distance between the analysis and the observations. This paper is concerned with <em>J</em><sub>o</sub>
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 <em>J</em><sub>b</sub> 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.</div>
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