Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO₂ measurements over Antwerp, Belgium

Abstract

Abstract. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is employed as an intercomparison tool for validating TROPOspheric Monitoring Instrument (TROPOMI) satellite NO2 retrievals against high-resolution Airborne Prism EXperiment (APEX) remote sensing observations performed in June 2019 in the region of Antwerp, a major hotspot of NO2 pollution in Europe. The model is first evaluated using meteorological and chemical observations in this area. Sensitivity simulations varying the model planetary layer boundary (PBL) parameterization were conducted for a 3 d period in June 2019, indicating a generally good performance of most parameterizations against meteorological data (namely ceilometer, surface meteorology, and balloon measurements), except for a moderate overestimation (∼ 1 m s−1) of near-surface wind speed. On average, all but one of the PBL schemes reproduce the surface NO2 measurements at stations of the Belgian Interregional Environmental Agency fairly well, although surface NO2 is generally underestimated during the day (between −4.3 % and −25.1 % on average) and overestimated at night (8.2 %–77.3 %). This discrepancy in the diurnal evolution arises despite (1) implementing a detailed representation of the diurnal cycle of emissions (Crippa et al., 2020) and (2) correcting the modeled concentrations to account for measurement interferences due to NOy reservoir species, which increases NO2 concentrations by about 20 % during the day. The model is further evaluated by comparing a 15 d simulation with surface NO2, NO, CO, and O3 data in the Antwerp region. The modeled daytime NO2 concentrations are more negatively biased during weekdays than during weekends, indicating a misrepresentation of the weekly temporal profile applied to the emissions obtained from Crippa et al. (2020). Using a mass balance approach, we determined a new weekly profile of NOx emissions, leading to a homogenization of the relative bias among the different weekdays. The ratio of weekend to weekday emissions is significantly lower in this updated profile (0.6) than in the profile based on Crippa et al. (2020; 0.84). Comparisons with remote sensing observations generally show a good reproduction of the spatial patterns of NO2 columns by the model. The model underestimated both APEX (by ca. −37 %) and TROPOMI columns (ca. −25 %) on 27 June, whereas no significant bias is found on 29 June. The two datasets are intercompared by using the model as an intermediate platform to account for differences in vertical sensitivity through the application of averaging kernels. The derived bias of TROPOMI v1.3.1 NO2 with respect to APEX is about −10 % for columns between (6–12) × 1015 molec. cm−2. The obtained bias for TROPOMI v1.3.1 increases with the NO2 column, following CAPEX=1.217Cv1.3-0.783 × 1015 molec. cm−2, in line with previous validation campaigns. The bias is slightly lower for the reprocessed TROPOMI v2.3.1, with CAPEX=1.055CPAL-0.437 × 1015 molec. cm−2 (PAL). Finally, a mass balance approach was used to perform a crude inversion of NOx emissions based on 15 d averaged TROPOMI columns. The emission correction is conducted only in regions with high columns and high sensitivity to emission changes in order to minimize the errors due to wind transport. The results suggest that emissions increase over Brussels–Antwerp (+20 %), the Ruhr Valley (13 %), and especially Paris (+39 %), and emissions decrease above a cluster of power plants in western Germany.

Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States

“…WRF‐Chem tropospheric NO 2 column concentrations, or VCDs, are quantified using the averaging kernels (AKs) and the ratio of total to tropospheric air mass factors (AMFs) associated with the TROPOMI NO 2 column retrieval for each overpass (Douros et al., 2023). This accounts for instrument sensitivity in modeled columns, creating a more direct comparison with the TROPOMI product (e.g., Poraicu et al., 2023). The AKs and AMFs are derived from the a priori NO 2 profile from the TM5‐MP chemical transport model (Williams et al., 2017): $$${\text{NO}}_{2}\text{VCD}=\frac{\text{AMF}}{{\text{AMF}}_{\text{trop}}}\times \frac{{A}_{v}}{gM}\sum\limits _{n=1}^{{n}_{\text{tot}}}{\left[{\text{NO}}_{2}\right]}_{n}\times {\text{AK}}_{n}\times {\increment}{P}_{n}$$$ where AMF and AMF trop are the TROPOMI‐provided total and tropospheric air mass factors (AMFs), respectively, A v is Avogadro's number, g is the force due to gravity, M is the molecular weight of air, n is the WRF model layer, and n tot is the total number of model layers within the troposphere, with the tropopause determined by the last model layer with a temperature lapse rate >2 C km −1 .…”

“…WRF-Chem tropospheric NO 2 column concentrations, or VCDs, are quantified using the averaging kernels (AKs) and the ratio of total to tropospheric air mass factors (AMFs) associated with the TROPOMI NO 2 column retrieval for each overpass (Douros et al, 2023). This accounts for instrument sensitivity in modeled columns, creating a more direct comparison with the TROPOMI product (e.g., Poraicu et al, 2023). The AKs and AMFs are derived from the a priori NO 2 profile from the TM5-MP chemical transport model (Williams et al, 2017):…”

Ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of NO2 and H2CO at Kinshasa and comparisons with TROPOMI observations

“…For example, TROPOMI H 2 CO columns tend to systematically underestimate ground-based infrared remotesensing data in polluted regions (Vigouroux et al, 2020). Regarding tropospheric NO 2 , columns validation studies indicate moderate underestimations at polluted midlatitude sites (e.g., Dimitropoulou et al, 2020;Zhao et al, 2020;Tack et al, 2021;Verhoelst et al, 2021;Poraicu et al, 2023). However, satellite measurements are poorly characterized in tropical regions.…”

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

“…Unfortunately, the continuous deployment of RCT simulations is no easy endeavour, due to their computational expense, dependence on input data which may not always be available in an up-to-date form at high resolution (in particular emission data), and the uncertainty in choice of simulation parametrizations. Another point of concern is the general accuracy of these models: RCT simulations reported in recent literature have shown significant deviations from observational reference data (see Visser et al (2019); Kuik et al (2016); Kuik et al (2018); Poraicu et al (2023)), e.g. an underestimation of the summertime surface-level NO 2 concentration of up to −50 %.…”