Abstract
Abstract. We present high spatial resolution (up to 2.2×2.2 km2) simulations focussed over south-west Germany using the online coupled regional atmospheric chemistry model system MECO(n) (MESSy-fied ECHAM and COSMO models nested n times). Numerical simulation of nitrogen dioxide (NO2) surface volume mixing ratios (VMRs) are compared to in situ measurements from a network with 193 locations including background, traffic-adjacent and industrial stations to investigate the model's performance in simulating the spatial and temporal variability of short-lived chemical species. We show that the use of a high-resolution and up-to-date emission inventory is crucial for reproducing the spatial variability and resulted in good agreement with the measured VMRs at the background and industrial locations with an overall bias of less than 10 %. We introduce a computationally efficient approach that simulates diurnal and daily variability in monthly-resolved anthropogenic emissions to resolve the temporal variability of NO2. MAX-DOAS (Multiple AXis Differential Optical Absorption Spectroscopy) measurements performed at Mainz (49.99∘ N, 8.23∘ E) were used to evaluate the simulated tropospheric vertical column densities (VCDs) of NO2. We propose a consistent and robust approach to evaluate the vertical distribution of NO2 in the boundary layer by comparing the individual differential slant column densities (dSCDs) at various elevation angles. This approach considers details of the spatial heterogeneity and sensitivity volume of the MAX-DOAS measurements while comparing the measured and simulated dSCDs. The effects of clouds on the agreement between MAX-DOAS measurements and simulations have also been investigated. For low elevation angles (≤8∘), small biases in the range of −14 % to +7 % and Pearson correlation coefficients in the range of 0.5 to 0.8 were achieved for different azimuth directions in the cloud-free cases, indicating good model performance in the layers close to the surface. Accounting for diurnal and daily variability in the monthly-resolved anthropogenic emissions was found to be crucial for the accurate representation of time series of measured NO2 VMR and dSCDs and is particularly critical when vertical mixing is suppressed, and the atmospheric lifetime of NO2 is relatively long.
Highlights
Regional atmospheric chemistry and transport models are important for the study and forecasting of atmospheric processes at fine spatial resolutions
The MECO(n) (MESSyfied ECHAM and COSMO models nested n times) regional model system developed by Kerkweg and Jöckel (2012b) allows for online coupling between different nests and in this way facilitates frequent updates of meteorological and chemical boundary conditions
We performed high spatial resolution regional model simulations focussed on south-west Germany to evaluate the short-lived pollutant NO2 using MAXDOAS and a network of in situ measurements
Summary
Regional atmospheric chemistry and transport models are important for the study and forecasting of atmospheric processes at fine spatial resolutions. The high spatial resolution of these models allows us to resolve localized emissions (e.g. industrial and urban clusters) and quantify their impacts on non-linear photochemical processes, e.g. ozone production (Vinken et al, 2014; Visser et al, 2019; Mertens et al, 2020a) as well as on heterogeneous processes, e.g. par-. Similar high-resolution model simulations including chemistry have been shown to better represent local maxima (e.g. isolated point sources, road networks and ship tracks) and facilitate understanding of sector-specific impacts on secondary pollution (e.g. ozone production) (Colette et al, 2014; Mertens et al, 2020a). Input emission inventories are available at temporal resolutions of months to years, but in reality, emissions from several sectors (e.g. road transport, residential combustion) vary markedly depending on the hour of the day and day of the week. From a modelling perspective incorporating high temporal resolution input emissions can be computationally inefficient due to the high readout time and subsequent requirement for interpolation on the model grid
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
