Modelling of tropospheric NO₂ using WRF-Chem with optimized temporal NOₓ emission profiles derived from in-situ observations – Comparisons to in-situ, satellite, and MAX-DOAS observations over central Europe

We present a WRF-Chem simulation over central Europe with a high spatial resolution of 3 km × 3 km and a focus on nitrogen dioxide (NO₂). A regional emission inventory, issued by the German Environmental Agency, with a spatial resolution of 1 km × 1 km is used. We demonstrate, that by precise temporal modulation of the emission data (use of "temporal profiles"), significant improvement in model accuracy over existing simulations is achieved. Simulated NO₂ surface concentrations are compared to measurements from a total of 275 in-situ measurement stations in Germany, where the model was able to reproduce average noontime NO₂ concentrations with a bias of +0.9 % and R = 0.76. A comparison between modelled NO₂ vertical column densities (VCDs) and satellite observations from TROPOMI (TROPOspheric Monitoring Instrument) is conducted, where crucial aspects of the observation process, such as altitude-dependent NO₂ sensitivity as well as the influence of clouds and a priori assumptions of the retrieval, are taken into account. Simulations and satellite observations are shown to agree with a model bias of −6.6 % and R = 0.84 for monthly means. Lastly, simulated NO₂ concentration profiles are compared to profiles obtained from Multiaxis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements of five European ground stations using the profile retrieval algorithms from the Mexican MAX-DOAS fit (MMF) and the Mainz Profile Algorithm (MAPA). For stations within Germany, biases of −5.9 % to +50.3 % were obtained when comparing average noontime NO₂ concentrations at different altitudes. Outside of Germany, where lower resolution emission data was used, biases of up to +78.6 % were observed. Overall, the study demonstrates that temporal modulation of emission data is crucial for modelling tropospheric NO₂ realistically