This paper reports on consolidated ground-based validation results of the atmospheric NO 2 data produced operationally since April 2018 by the TROPOspheric Monitoring Instrument (TROPOMI) on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite. Tropospheric, stratospheric, and total NO 2 column data from S5P are compared to correlative measurements collected from, respectively, 19 Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), 26 Network for the Detection of Atmospheric Composition Change (NDACC) Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 Pandonia Global Network (PGN)/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatio-temporal co-location of the satellite and correlative data, e.g. by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5P data show, on average, (i) a negative bias for the tropospheric column data, of typically −23  % to −37  % in clean to slightly polluted conditions but reaching values as high as −51  % over highly polluted areas; (ii) a slight negative median difference for the stratospheric column data, of about −0.2  Pmolec cm −2 , i.e. approx. −2  % in summer to −15  % in winter; and (iii) a bias ranging from zero to −50  % for the total column data, found to depend on the amplitude of the total NO 2 column, with small to slightly positive bias values for columns below 6 Pmolec cm −2 and negative values above. The dispersion between S5P and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec cm −2 ) but exceed those for the tropospheric column data (0.7 Pmolec cm −2 ). While a part of the biases and dispersion may be due to representativeness differences such as different area averaging and measurement times, it is known that errors in the S5P tropospheric columns exist due to shortcomings in the (horizontally coarse) a priori profile representation in the TM5-MP chemical transport model used in the S5P retrieval and, to a lesser extent, to the treatment of cloud effects and aerosols. Although considerable differences (up to 2 Pmolec cm −2 and more) are observed at single ground-pixel level, the near-real-time (NRTI) and offline (OFFL) versions of the S5P NO 2 operational data processor provide similar NO 2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.

We present high spatial resolution (up to 2.2×2.2   km 2 ) 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 ( NO 2 ) 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 NO 2 . 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 NO 2 . We propose a consistent and robust approach to evaluate the vertical distribution of NO 2 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 NO 2 VMR and dSCDs and is particularly critical when vertical mixing is suppressed, and the atmospheric lifetime of NO 2 is relatively long.

The second Cabauw Intercomparison of Nitrogen Dioxide measuring Instruments (CINDI-2) took place in Cabauw (the Netherlands) in September 2016 with the aim of assessing the consistency of multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of tropospheric species (NO 2 , HCHO, O 3 , HONO, CHOCHO and O 4 ). This was achieved through the coordinated operation of 36 spectrometers operated by 24 groups from all over the world, together with a wide range of supporting reference observations (in situ analysers, balloon sondes, lidars, long-path DOAS, direct-sun DOAS, Sun photometer and meteorological instruments). In the presented study, the retrieved CINDI-2 MAX-DOAS trace gas (NO 2 , HCHO) and aerosol vertical profiles of 15 participating groups using different inversion algorithms are compared and validated against the colocated supporting observations, with the focus on aerosol optical thicknesses (AOTs), trace gas vertical column densities (VCDs) and trace gas surface concentrations. The algorithms are based on three different techniques: six use the optimal estimation method, two use a parameterized approach and one algorithm relies on simplified radiative transport assumptions and analytical calculations. To assess the agreement among the inversion algorithms independent of inconsistencies in the trace gas slant column density acquisition, participants applied their inversion to a common set of slant columns. Further, important settings like the retrieval grid, profiles of O 3 , temperature and pressure as well as aerosol optical properties and a priori assumptions (for optimal estimation algorithms) have been prescribed to reduce possible sources of discrepancies. The profiling results were found to be in good qualitative agreement: most participants obtained the same features in the retrieved vertical trace gas and aerosol distributions; however, these are sometimes at different altitudes and of different magnitudes. Under clear-sky conditions, the root-mean-square differences (RMSDs) among the results of individual participants are in the range of 0.01–0.1 for AOTs, (1.5–15)  × 10 14 molec . cm - 2 for trace gas (NO 2 , HCHO) VCDs and (0.3– 8 ) × 10 10 molec . cm - 3 for trace gas surface concentrations. These values compare to approximate average optical thicknesses of 0.3 , trace gas vertical columns of 90 × 10 14 molec . cm - 2 and trace gas surface concentrations of 11 × 10 10 molec . cm - 3 observed over the campaign period. The discrepancies originate from differences in the applied techniques, the exact implementation of the algorithms and the user-defined settings that were not prescribed. For the comparison against supporting observations, the RMSDs increase to a range of 0.02–0.2 against AOTs from the Sun photometer, (11– 55 ) × 10 14 molec . cm - 2 against trace gas VCDs from direct-sun DOAS observations and (0.8– 9 ) × 10 10 molec . cm - 3 against surface concentrations from the long-path DOAS instrument. This increase in RMSDs is most likely caused by uncertainties in the supporting data, spatiotemporal mismatch among the observations and simplified assumptions particularly on aerosol optical properties made for the MAX-DOAS retrieval. As a side investigation, the comparison was repeated with the participants retrieving profiles from their own differential slant column densities (dSCDs) acquired during the campaign. In this case, the consistency among the participants degrades by about 30  % for AOTs, by 180  % ( 40  % ) for HCHO (NO 2 ) VCDs and by 90  % ( 20  % ) for HCHO (NO 2 ) surface concentrations. In former publications and also during this comparison study, it was found that MAX-DOAS vertically integrated aerosol extinction coefficient profiles systematically underestimate the AOT observed by the Sun photometer. For the first time, it is quantitatively shown that for optimal estimation algorithms this can be largely explained and compensated by considering biases arising from the reduced sensitivity of MAX-DOAS observations to higher altitudes and associated a priori assumptions.

In urban areas, road traffic is a dominant source of nitrogen oxides ( NO x = NO + NO 2 ). Although the emissions from individual vehicles are regulated by the European emission standards, real driving emissions often exceed these limits. In this study, two multi-axis differential optical absorption spectroscopy (MAX-DOAS) instruments on opposite sides of the motorway were used to measure the NO 2 absorption caused by road traffic at the A60 motorway close to Mainz, Germany. In combination with wind data, the total NO x emissions for the occurring traffic volume can be estimated. Hereto, the ozone-dependent photochemical equilibrium between NO and NO 2 is considered. We show that for 10 May 2019 the measured emissions exceed the maximum expected emissions calculated from the European emission standards for standardised test cycles by a factor of 11±7 . One major advantage of the method used here is that MAX-DOAS measurements are very sensitive to the integrated NO 2 concentration close to the surface. Thus, all emitted NO 2 molecules are detected independently from their altitude, and therefore the whole emission plume originating from the nearby motorway is captured, which is a key advantage compared to other approaches such as in situ measurements.

In this study, we compare measured and simulated O 4 absorptions for conditions of extremely low aerosol optical depth (between 0.034 to 0.056 at 360 nm) on one day during a ship cruise in the tropical Atlantic. For such conditions, the uncertainties related to imperfect knowledge of aerosol properties do not significantly affect the comparison results. We find that the simulations underestimate the measurements by 15 % to 20 %. Even for simulations without any aerosols, the measured O 4 absorptions are still systematically higher than the simulation results. The observed discrepancies cannot be explained by uncertainties of the measurements and simulations and thus indicate a fundamental inconsistency between simulations and measurements.

Clouds impact the radiative transfer of the Earth's atmosphere and strongly influence satellite measurements in the ultraviolet–visible (UV–vis) and infrared (IR) spectral ranges. For satellite measurements of trace gases absorbing in the UV–vis spectral range, particularly clouds ultimately determine the vertical sensitivity profile, mainly by reducing the sensitivity for trace-gas columns below the cloud. The Mainz iterative cloud retrieval utilities (MICRU) algorithm is specifically designed to reduce the error budget of trace-gas retrievals, such as those for nitrogen dioxide ( NO 2 ), which strongly depends on the accuracy of small cloud fractions (CFs) in particular. The accuracy of MICRU is governed by an empirical parameterisation of the viewing-geometry-dependent background surface reflectivity taking instrumental and physical effects into account. Instrumental effects are mainly degradation and polarisation effects; physical effects are due to the anisotropy of the surface reflectivity, e.g. shadowing of plants and sun glitter. MICRU is applied to main science channel (MSC) and polarisation measurement device (PMD) data collected between April 2007 and June 2013 by the Global Ozone Monitoring Experiment 2A (GOME-2A) instrument aboard the MetOp-A satellite. CFs are retrieved at different spectral bands between 374 and 758 nm. The MICRU results for MSC and PMD at different wavelengths are intercompared to study CF precision and accuracy, which depend on wavelength and spatial correlation. Furthermore, MICRU results are compared to FRESCO (fast retrieval scheme for clouds from the oxygen A band) and OCRA (optical cloud recognition algorithm) operational cloud products. We show that MICRU retrieves small CFs with an accuracy of 0.04 or better for the entire 1920 km wide swath with a potential bias between − 0.01 and − 0.03. CFs retrieved at shorter wavelengths are less affected by adverse surface heterogeneities. The comparison to the operational CF algorithms shows that MICRU significantly reduces the dependence on viewing angle, time, and sun glitter. Systematic effects along coasts are particularly small for MICRU due to its dedicated treatment of land and ocean surfaces. The MICRU algorithm is designed for spectroscopic instruments ranging from the GOME to Sentinel-5P/Tropospheric Monitoring Instrument (TROPOMI) but is also applicable to UV–vis imagers like the Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and Sentinel-2.