Abstract
We propose to add an extra source of information to the data-assimilation of the regional HIgh Resolution Limited Area Model (HIRLAM) model, constraining larger scales to the host model providing the lateral boundary conditions. An extra term, Jk, measuring the distance to the large-scale vorticity of the host model, is added to the cost-function of the variational data-assimilation. Vorticity is chosen because it is a good representative of the large-scale flow and because vorticity is a basic control variable of the HIRLAM variational data-assimilation. Furthermore, by choosing only vorticity, the remaining model variables, divergence, temperature, surface pressure and specific humidity will be allowed to adapt to the modified vorticity field in accordance with the internal balance constraints of the regional model. The error characteristics of the Jk term are described by the horizontal spectral densities and the vertical eigenmodes (eigenvectors and eigenvalues) of the host model vorticity forecast error fields, expressed in the regional model geometry. The vorticity field, provided by the European Centre for Medium-range Weather Forecasts (ECMWF) operational model, was assimilated into the HIRLAM model during an experiment period of 33 d in winter with positive impact on forecast verification statistics for upper air variables and mean sea level pressure.The review process was handled by Editor-in-Chief Harald Lejenäs
Highlights
In order to run a regional Numerical Weather Prediction (NWP) model, an initial condition and a host model providing lateral boundaries are required
We have described and tested a method to assimilate the host model information (ECMWF) into a regional NWP model (HIRLAM)
The vorticity field from the host model was chosen to act as a constraint in the regional model variational analysis
Summary
In order to run a regional Numerical Weather Prediction (NWP) model, an initial condition and a host model providing lateral boundaries are required. One reason for doing this is to make the initial model state more consistent with the host model that will be applied at the lateral boundaries during time-integration Another reason is that the global models often have more advanced assimilation techniques that use more satellite data, for example, and give a better description of the large-scale flow. We use a short forecast from ECMWF, instead of an analysis, to reduce the possibility of error correlations between the host model field and the observations used in the regional model data-assimilation. It is not even possible for us in practice to use the ECMWF analysis in an operational context since it is not available in real time.
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