Optimizing High-Resolution Simulations with the Weather Research and Forecasting (WRF) Model for the German Rhine-Neckar Metropolitan Region

Urban areas are responsible for more than 70% of global CO2 emissions, thus representing an enormous mitigation potential. However, reliable bottom-up information on (intra-)urban CO2 emissions is not readily available at high temporal and spatial resolution and even if it is available, it is usually subject to large uncertainties. Urban monitoring networks can independently quantify anthropogenic CO2 emissions in cities and thus provide stakeholders with valuable information on (intra-)urban mitigation efforts. Optimizing urban monitoring networks with respect to accuracy and cost efficiency requires an evaluation of different network configurations. In the scope of the joint project "Integrated Greenhouse Gas Monitoring System for Germany" (ITMS) we will analyze optimal network designs in German urban and metropolitan areas using the WRF model. The first step in this task is to assure an accurate representation of atmospheric transport in our modeling framework, which we analyze in this study.

We here show results of high resolution (1km x1km) meteorological simulations for a typical German metropolitan area, the Rhine-Neckar region, using the WRF model. We examine the effects of model resolution, input data resolution, planetary boundary layer schemes and urban parametrization strategies on model performance. We analyze model performance by comparing air temperature, wind direction, wind speed and boundary layer height for four different periods of a year to re-analyzed data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and selected measurement stations. Our analysis benefits from a large number of meteorological stations by the German Weather Service in this domain, which will be used for the comparison. The aim of this work is to ensure that the simulation of greenhouse gas dispersion is based on robust and realistic atmospheric transport and can ultimately provide representative recommendations for the optimal design of measurement networks.