On the influence of vertical mixing, boundary layer schemes, and temporal emission profiles on tropospheric NO₂ in WRF-Chem – comparisons to in situ, satellite, and MAX-DOAS observations

Abstract

Abstract. We present WRF-Chem simulations over central Europe with a spatial resolution of 3 km × 3 km and 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 as input. We demonstrate by comparison of five different model setups that significant improvements in model accuracy can be achieved by choosing the appropriate boundary layer scheme, increasing vertical mixing strength, and/or tuning the temporal modulation of the emission data (“temporal profiles”) driving the model. The model setup with improved vertical mixing is shown to produce the best results. 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 ca. −3 % and R=0.74. The best agreement is achieved when correcting for the presumed NOy cross sensitivity of the molybdenum-based in situ measurements by computing an NOy correction factor from modelled peroxyacetyl nitrate (PAN) and nitric acid (HNO3) mixing ratios. A comparison between modelled NO2 vertical column densities (VCDs) and satellite observations from TROPOMI (TROPOspheric Monitoring Instrument) is conducted with averaging kernels taken into account. Simulations and satellite observations are shown to agree with a bias of +5.5 % and R=0.87 for monthly means. Lastly, simulated NO2 concentration profiles are compared to noontime NO2 profiles obtained from multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements at five locations in Europe. For stations within Germany, average biases of −25.3 % to +12.0 % were obtained. Outside of Germany, where lower-resolution emission data were used, biases of up to +50.7 % were observed. Overall, the study demonstrates the high sensitivity of modelled NO2 to the mixing processes in the boundary layer and the diurnal distribution of emissions.

Review of "NitroNet – A deep-learning NO2 profile retrieval prototype for the TROPOMI satellite instrument"

“…A study by Douros et al (2023) reveals overestimations of the winter-time NO 2 VCD by +50 %, and demonstrates that such biases even occur in ensemble models, such as CAMS (consisting of 11 different RCT models with 0.1°× 0.1°horizontal resolution). In previous work, we showed that a recalibration of the vertical mixing parametrization can mostly resolve such biases in the WRF-Chem model in summer over Europe (see Kuhn et al (2024)). However, the process of model recalibration is tedious, computationally expensive, and domain-dependent.…”

“…The simulation was run for the month of May 2019 on a domain over Europe with a spatial resolution of 3 km × 3 km, 43 terrain-following pressure levels, and hourly output. A detailed description, discussion, and validation study of this dataset was published in Kuhn et al (2024). The simulation setup additionally deploys the vertical emission profiles from Bieser et al (2011).…”

“…where [PAN], [HNO 3 ], and [NO 2 ] denote the true surface mixing ratios of PAN, HNO 3 , and NO 2 , while [NO * 2 ] denotes the biased measurement result. The same formula was used in Kuhn et al (2024), and was found to be crucial for the agreement between simulation data and in-situ measurements. NitroNet was trained to predict F (as learned from WRF-2019) as an additional output, so that when comparing NitroNet predictions to in-situ measurements, the measurement bias can be compensated.…”

“…Here, and throughout the rest of the article, "correlation coefficient" refers to the Pearson correlation coefficient. A more detailed discussion of the WRF-Chem simulation results can be found in Kuhn et al (2024).…”

“…NitroNet is trained on numerically simulated data from the WRF-Chem model, operated on a European domain for the month of May 2019 as described in Kuhn et al (2024). A data filtering scheme is used to ensure that only well-validated results from Cao (2023); all presented models were trained on in-situ surface observations) by inclusion of synthetic model data.…”