Effective roughness calculated from satellite-derived land cover maps and hedge-information used in a weather forecasting model

In numerical weather prediction, climate and hydrological modelling, the grid cell size is typically larger than the horizontal length scales of variations in aerodynamic roughness, surface temperature and surface humidity. These local land cover variations give rise to sub-grid scale surface flux differences. Especially the roughness variations can give a significantly different value between the equilibrium roughness in each of the patches as compared to the aggregated roughness value, the so-called effective roughness, for the grid cell. The effective roughness is a quantity that secures the physics to be well-described in any large-scale model. A method of aggregating the roughness step changes in arbitrary real terrain has been applied in flat terrain (Denmark) where sub-grid scale vegetation-driven roughness variations are a dominant characteristic of the landscape. The aggregation model is a physical two-dimensional atmospheric flow model in the horizontal domain based on a linearized version of the Navier Stoke equation. The equations are solved by the Fast Fourier Transformation technique, hence the code is very fast. The new effective roughness maps have been used in the HIgh Resolution Limited Area Model (HIRLAM) weather forecasting model and the weather prediction results are compared for a number of cases to synoptic and other observations with improved agreement above the predictions based on current standard input. Typical seasonal springtime bias on forecasted winds over land of +0.5 m s(-1) and -0.2 m s(-1) in coastal areas is reduced by use of the effective roughness maps.