Improving the Near-Surface Wind and Turbulence at the Edge of the Orographic Drag Gray Zone by Tuning the Roughness Length

Abstract

Abstract In this paper, we present the implementation and evaluate the impact of the new roughness length configuration in the ALARO canonical model configuration of the ALADIN system at the edge of the orographic gravity wave drag gray zone. As an essential input for turbulence parameterization, the roughness length affects the near-surface turbulent fluxes and the screen-level interpolation of meteorological parameters. We utilize GMTED2010 and ECOCLIMAP-II databases to derive orographic and vegetation components of the effective roughness length and introduce tuning parameters enabling us to optimize predicted near-surface turbulent momentum fluxes and 10-m wind speed. Based on sensitivity tests, we (i) prove the necessity of tuning the roughness length fields, (ii) considerably reduce the RMSE of near-surface turbulent momentum fluxes (6%–7%) and 10-m wind speed for different groups of stations (3%–10%), and (iii) identify the tree height as the most influential input parameter in our computational domain. The RMSE decomposition indicates that the improvement of 10-m wind speed mostly comes from a decrease in the random error and bias of the mean. The variability is slightly underestimated, thus reducing the model accuracy for wind speeds above the 95th percentile but at an acceptable level. We explain that roughness length tuning also compensates for the missing roughness sublayer correction in our system. Finally, we show that, although the impact of the orographic gravity wave drag scheme at a horizontal mesh size of 1.8 km is generally small, it is still beneficial for capturing some finer features observed in atmospheric soundings. Significance Statement Aiming to improve the 10-m wind speed forecast without sacrificing the accuracy of turbulent momentum fluxes in the kilometric resolution numerical weather prediction model, we derived new roughness length fields from high-resolution physiography databases. Therein, we proved the importance of tuning the input orography and vegetation fields and, depending on the time of day and year, reduced the root-mean-square error of 10-m wind speed by 3%–10%. Further, we demonstrated that orographic gravity wave drag parameterization is still needed to predict finer details seen in wind profiles from atmospheric soundings. Finally, we discussed the related simplifications in our model and their implications and proposed steps toward a more consistent and complete treatment of the near-surface turbulence.