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This reports answers the three following questions: What is the uncertainty of PGN (Pandonia Global Network) trace gas columns for retrievals using a reference spectrum from the literature? How do time series of new products like O3 effective temperature compare to other datasets? How do retrieved O3 column uncertainties compare to the uncertainties given in measurements from other ground-based data sources?

The goal of this report was to test the impact of using different types of reference spectra for the retrieval of lunar trace gas columns (e.g. a reference spectrum from solar measurements or lunar measurements). fine-tune the spectral fitting setup for the retrieval of lunar trace gas columns. optimize thresholds for dedicated quality indicators for the retrieved trace gas columns.&nbsp

This is the final report of WP2126 “PGN Products Quantitative Uncertainty” of the ESA project “Quality Assurance for Earth Observation” (QA4EO). In this report, the algorithm error of a total column NO2 retrieval is discussed as well as the common error for a particular reference spectrum. Further, the uncertainty propagation within the Blick Software Suite is outlined.

International audience; For more than three years now, the first atmospheric satellite of the Copernicus EO programme, Sentinel-5p (S5P) TROPOMI, has acquired spectral measurements of the Earth radiance in the visible range, from which near-real-time (NRTI) and offline (OFFL) processors retrieve the total, tropospheric and stratospheric column abundance of NO2. The S5P Mission Performance Centre performs continuous QA/QC of these data products enabling users to verify the fitness-for- purpose of the S5P data. Quality Indicators are derived from comparisons to ground-based reference data, both station-by-station in the S5P Automated Validation Server (AVS), and globally in more in-depth analyses. Complementary quality information is obtained from product intercomparisons (NRTI vs. OFFL) and from satellite-to-satellite comparisons. After three years of successful operation we present here a consolidated overview of the quality of the S5P TROPOMI NO2 data products, with particular attention paid to the impact of the various processor improvements, especially in the latest version (v1.4), activated on 2 December 2020, which introduces an updated cloud retrieval resulting in higher NO2 columns in polluted regions. Also the upcoming v2, due in April 2021 but already used to produce a Diagnostic Data Set, is discussed.S5P NO2 data are compared to ground-based measurements collected through either the ESA Validation Data Centre (EVDC) or network data archives (NDACC, PGN). Measurements from the Pandonia Global Network (PGN) serve as a reference for total NO2 validation, Multi-Axis DOAS data for tropospheric NO2 validation, and NDACC zenith-scattered-light DOAS data for stratospheric NO2 validation. Comparison methods are optimized to limit spatial and temporal mismatch errors (co-location strategy, photochemical adjustment to account for local time difference). Comparison results are analyzed to derive Quality Indicators and to conclude on the compliance w.r.t. the Powered by TCPDF (www.tcpdf.org)mission requirements. This include estimates of: (1) the bias, as proxy for systematic errors, (2) the dispersion of the differences, which combines random errors with seasonal and mismatch errors, and (3) the dependence of these on key influence quantities (surface albedo, cloud cover...)Overall, the MPC quality assessment of S5P NO2 data concludes to an excellent performance for the stratospheric data (bias

This paper reports on consolidated ground-based validation results of the atmospheric NO2 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 NO2 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 NO2 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 NO2 operational data processor provide similar NO2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values. This research has been supported by the ESA/ESRIN (grant no. 4000117151/16/I-LG) and the BELSPO/ESA ProDEx (TROVA-E2 (PEA grant no. 4000116692)). Part of the reported work was carried out in the framework of the Copernicus Sentinel-5 Precursor Mission Performance Centre (S5P MPC), contracted by the European Space Agency and supported by the Belgian Federal Science Policy Office (BELSPO), the Royal Belgian Institute for Space Aeronomy (BIRA-IASB), the Netherlands Space Office (NSO), and the German Aerospace Centre (DLR). Part of this work was carried out also in the framework of the S5P Validation Team (S5PVT) AO projects NIDFORVAL (ID no. 28607, PI Gaia Pinardi, BIRA-IASB) and CESAR (ID no. 28596, PI Arnoud Apituley, KNMI). The authors express special thanks to Ann Mari Fjæraa, José Granville, Sander Niemeijer, and Olivier Rasson for post-processing of the network and satellite data and for their dedication to the S5P operational validation. The LATMOS real-time processing facility is acknowledged for fast delivery of ZSL-DOAS SAOZ data. Fast delivery of MAX-DOAS data tailored to the S5P validation was organized through the S5PVT AO project NIDFORVAL. The authors are grateful to ESA/ESRIN for supporting the ESA Validation Data Centre (EVDC) established at NILU and for running the Fiducial Reference Measurements (FRM) programme and in particular the FRM4DOAS and Pandonia projects. The PGN is a bilateral project between NASA and ESA, and the NASA funding for the PGN is provided through the NASA Tropospheric Composition Program and Goddard Space Flight Center Pandora project. The MAX-DOAS, ZSL-DOAS, and PGN instrument PIs and staff at the stations are thanked warmly for their sustained effort on maintaining high-quality measurements and for valuable scientific discussions. Aleksandr Elokhov and Aleksandr Gruzdev acknowledge national funding from RFBR through the project 20-95-00274. IUP Bremen acknowledges DLR Bonn for funding received through project 50EE1709A. The SAOZ network acknowledges funding from the French Institut National des Sciences de l'Univers (INSU) of the Centre National de la Recherche Scientifique (CNRS), Centre National d'Etudes Spatiales (CNES), and Institut polaire français Paul Emile Victor (IPEV). Work done by Hitoshi Irie was supported by the Environment Research and Technology Development Fund (2-1901) of the Environmental Restoration and Conservation Agency of Japan, JSPS KAKENHI (grant nos. JP19H04235 and JP17K00529), the JAXA 2nd Research Announcement on the Earth Observations (grant no. 19RT000351), and JST CREST (grant no. JPMJCR15K4). The University of Toronto ZSL-DOAS measurements at Eureka were made at the Polar Environment Atmospheric Research Laboratory (PEARL) by the Canadian Network for the Detection of Atmospheric Change (CANDAC), with support from the Canadian Space Agency (AVATARS project), the Natural Sciences and Engineering Research Council (PAHA project), and Environment and Climate Change Canada. 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Nitrogen oxides (NOx≡NO+NO2) in the NOx-limited upper troposphere (UT) are long-lived and so have a large influence on the oxidizing capacity of the troposphere and formation of the greenhouse gas ozone. Models misrepresent NOx in the UT, and observations to address deficiencies in models are sparse. Here we obtain a year of near-global seasonal mean mixing ratios of NO2 in the UT (450–180 hPa) at 1∘×1∘ by applying cloud-slicing to partial columns of NO2 from TROPOMI. This follows refinement of the cloud-slicing algorithm with synthetic partial columns from the GEOS-Chem chemical transport model. TROPOMI, prior to cloud-slicing, is corrected for a 13 % underestimate in stratospheric NO2 variance and a 50 % overestimate in free-tropospheric NO2 determined by comparison to Pandora total columns at high-altitude free-tropospheric sites at Mauna Loa, Izaña, and Altzomoni and MAX-DOAS and Pandora tropospheric columns at Izaña. Two cloud-sliced seasonal mean UT NO2 products for June 2019 to May 2020 are retrieved from corrected TROPOMI total columns using distinct TROPOMI cloud products that assume clouds are reflective boundaries (FRESCO-S) or water droplet layers (ROCINN-CAL). TROPOMI UT NO2 typically ranges from 20–30 pptv over remote oceans to >80 pptv over locations with intense seasonal lightning. Spatial coverage is mostly in the tropics and subtropics with FRESCO-S and extends to the midlatitudes and polar regions with ROCINN-CAL, due to its greater abundance of optically thick clouds and wider cloud-top altitude range. TROPOMI UT NO2 seasonal means are spatially consistent (R=0.6–0.8) with an existing coarser spatial resolution (5∘ latitude × 8∘ longitude) UT NO2 product from the Ozone Monitoring Instrument (OMI). UT NO2 from TROPOMI is 12–26 pptv more than that from OMI due to increase in NO2 with altitude from the OMI pressure ceiling (280 hPa) to that for TROPOMI (180 hPa), but possibly also due to altitude differences in TROPOMI and OMI cloud products and NO2 retrieval algorithms. The TROPOMI UT NO2 product offers potential to evaluate and improve representation of UT NOx in models and supplement aircraft observations that are sporadic and susceptible to large biases in the UT.

Airborne and ground-based Pandora spectrometer NO2 column measurements were collected during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region, which coincided with early observations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOMI NO2 Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO2. First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated (r2= 0.92 and slope of 1.03), with the largest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representativity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representativity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250 m × 250 m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (r2=0.96) than Pandora measurements are with TROPOMI (r2=0.84). The largest outliers between TROPOMI and the reference measurements appear to stem from too spatially coarse a priori surface reflectivity (0.5∘) over bright urban scenes. In this work, this results during cloud-free scenes that, at times, are affected by errors in the TROPOMI cloud pressure retrieval impacting the calculation of tropospheric air mass factors. This factor causes a high bias in TROPOMI TrVCs of 4 %–11 %. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19 %–33 % during the LISTOS timeframe of June–September 2018. Part of this low bias is caused by coarse a priori profile input from the TM5-MP model; replacing these profiles with those from a 12 km North American Model–Community Multiscale Air Quality (NAMCMAQ) analysis results in a 12 %–14 % increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7 %–19 % low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets.

Nitrogen oxides (NOx ≡ NO + NO2) in the NOx-limited upper troposphere (UT) are long-lived and so have a large influence on the oxidizing capacity of the troposphere and formation of the greenhouse gas ozone. Models misrepresent NOx in the UT and observations to address deficiencies in models are sparse. Here we obtain a year of near-global seasonal mean mixing ratios of NO2 in the UT (450–180 hPa) at 1 ° x 1° by applying cloud-slicing to partial columns of NO2 from TROPOMI. This follows refinement of the cloud-slicing algorithm with synthetic partial columns from the GEOS-Chem chemical transport model. We find that synthetic cloud-sliced UT NO2 are spatially consistent (R = 0.64) with UT NO2 calculated across the same cloud pressure range and scenes as are cloud-sliced (“true” UT NO2), but the cloud-sliced UT NO2 is 11–22 % more than the "true" all-sky seasonal mean. The largest contributors to differences between synthetic cloud-sliced and “true” UT NO2 are target resolution of the cloud-sliced product and uniformity of overlying stratospheric NO2. TROPOMI, prior to cloud-slicing, is corrected for a 13 % underestimate in stratospheric NO2 variance and a 50 % overestimate in free tropospheric NO2 determined by comparison to Pandora total columns at high-altitude sites in Mauna Loa, Izaña and Altzomoni, and MAX-DOAS and Pandora tropospheric columns at Izaña. Two cloud-sliced seasonal mean UT NO2 products for June 2019 to May 2020 are retrieved from corrected TROPOMI total columns using distinct TROPOMI cloud products that assume clouds are reflective boundaries (FRESCO-S) or water droplet layers (ROCINN-CAL). TROPOMI UT NO2 typically ranges from 20-30 pptv over remote oceans to > 80 pptv over locations with intense seasonal lightning. Spatial coverage is mostly in the tropics and subtropics with FRESCO-S and extends to the midlatitudes and polar regions with ROCINN-CAL, due to its greater abundance of optically thick clouds and wider cloud top altitude range. TROPOMI UT NO2 seasonal means are spatially consistent (R = 0.6–0.8) with an existing coarser spatial resolution (5° latitude x 8° longitude) UT NO2 product from the Ozone Monitoring Instrument (OMI). UT NO2 from TROPOMI is 12–26 pptv more than that from OMI due to increase in NO2 with altitude from the OMI pressure ceiling (280 hPa) to that for TROPOMI (180 hPa), but possibly also systematic altitude differences between the TROPOMI and OMI cloud products. The TROPOMI UT NO2 product offers potential to evaluate and improve representation of UT NOx in models and supplement aircraft observations that are sporadic and susceptible to large biases in the UT.

This paper reports on consolidated ground-based validation results of the atmospheric NO2 data produced operationally since April 2018 by the TROPOMI instrument on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5p) satellite. Tropospheric, stratospheric, and total NO2 column data from S5p are compared to correlative measurements collected from, respectively, 19 Multi-Axis DOAS (MAX-DOAS), 26 NDACC Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 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 an 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 bias for the stratospheric column data, of about −0.2 Pmolec/cm2, 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 NO2 column, with small to slightly positive bias values for columns below 6 Pmolec/cm2 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/cm2), but exceed those for the tropospheric column data (0.7 Pmolec/cm2). While a part of the biases and dispersion may be due to representativeness differences, 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 chemistry transport model used in the S5p retrieval, and to a lesser extent, to the treatment of cloud effects. Although considerable differences (up to 2 Pmolec/cm2 and more) are observed at single ground-pixel level, the near-real-time (NRTI) and off-line (OFFL) versions of the S5p NO2 operational data processor provide similar NO2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.

In September 2016, 36 spectrometers from 24 institutes measured a number of key atmospheric pollutants for a period of 17 d during the Second Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) that took place at Cabauw, the Netherlands (51.97 degrees N, 4.93 degrees E). We report on the outcome of the formal semi-blind intercomparison exercise, which was held under the umbrella of the Network for the Detection of Atmospheric Composition Change (NDACC) and the European Space Agency (ESA). The three major goals of CINDI-2 were (1) to characterise and better understand the differences between a large number of multi-axis differential optical absorption spectroscopy (MAX-DOAS) and zenith-sky DOAS instruments and analysis methods, (2) to define a robust methodology for performance assessment of all participating instruments, and (3) to contribute to a harmonisation of the measurement settings and retrieval methods. This, in turn, creates the capability to produce consistent high-quality ground-based data sets, which are an essential requirement to generate reliable long-term measurement time series suitable for trend analysis and satellite data validation. The data products investigated during the semi-blind intercomparison are slant columns of nitrogen dioxide (NO2), the oxygen collision complex (O-4) and ozone (O-3) measured in the UV and visible wavelength region, formaldehyde (HCHO) in the UV spectral region, and NO2 in an additional (smaller) wavelength range in the visible region. The campaign design and implementation processes are discussed in detail including the measurement protocol, calibration procedures and slant column retrieval settings. Strong emphasis was put on the careful alignment and synchronisation of the measurement systems, resulting in a unique set of measurements made under highly comparable air mass conditions. The CINDI-2 data sets were investigated using a regression analysis of the slant columns measured by each instrument and for each of the target data products. The slope and intercept of the regression analysis respectively quantify the mean systematic bias and offset of the individual data sets against the selected reference (which is obtained from the median of either all data sets or a subset), and the rms error provides an estimate of the measurement noise or dispersion. These three criteria are examined and for each of the parameters and each of the data products, performance thresholds are set and applied to all the measurements. The approach presented here has been developed based on heritage from previous intercomparison exercises. It introduces a quantitative assessment of the consistency between all the participating instruments for the MAX-DOAS and zenith-sky DOAS techniques. Netherlands Space Office (NSO); ESA through the CINDI-2 (ESA) project [4000118533/16/I-Sbo]; ESA through the FRM4DOAS (ESA) project [4000118181/16/I-EF]; EU 7th Framework Programme QA4ECV projectEuropean Union (EU) [607405]; Austrian Science Fund (FWF)Austrian Science Fund (FWF) [I 2296-N29]; Canadian Space Agency (AVATARS project); Natural Sciences and Engineering Research Council (PAHA project); Canada Foundation for InnovationCanada Foundation for Innovation; UVAS ("Ultraviolet and Visible Atmospheric Sounder") projects SEOSAT/INGENIO [ESP2015-71299-R]; DFG project RAPSODI [PL 193/17-1]; Centre National de la Recherche Scientifique (CNRS)Centre National de la Recherche Scientifique (CNRS); Centre National d'Etudes Spatiales (CNES)Centre National D'etudes Spatiales; National funding project HELADO [CTM2013-41311-P]; National funding project AVATAR [CGL2014-55230-R]; Russian Science FoundationRussian Science Foundation (RSF) [16-17-10275]; Russian Foundation for Basic ResearchRussian Foundation for Basic Research (RFBR) [16-05-01062, 18-35-00682]; ACTRIS-2 (H2020 grant) [654109]; NASA's Atmospheric Composition ProgramNational Aeronautics & Space Administration (NASA) [NASA-16-NUP2016-0001]; US National Science FoundationNational Science Foundation (NSF) [AGS-1620530]; NASANational Aeronautics & Space Administration (NASA); University of Bremen; DFG Research Center/Cluster of Excellence "The Ocean in the Earth System-MARUM"German Research Foundation (DFG); University of Bremen Institutional Strategy of the DFG; Luftblick through the ESA Pandonia Project; NASA Pandora Project at the Goddard Space Flight Center under NASA Headquarters' Tropospheric Composition Program CINDI-2 received funding from the Netherlands Space Office (NSO). Funding for this study was provided by ESA through the CINDI-2 (ESA contract no. 4000118533/16/I-Sbo) and FRM4DOAS (ESA contract no. 4000118181/16/I-EF) projects and partly within the EU 7th Framework Programme QA4ECV project (grant agreement no. 607405). The BOKU MAX-DOAS instrument was funded and the participation of Stefan F. Schreier was supported by the Austrian Science Fund (FWF): I 2296-N29. The participation of the University of Toronto team was supported by the Canadian Space Agency (through the AVATARS project) and the Natural Sciences and Engineering Research Council (through the PAHA project). The instrument was primarily funded by the Canada Foundation for Innovation and is usually operated at the Polar Environment Atmospheric Research Laboratory (PEARL) by the Canadian Network for the Detection of Atmospheric Change (CANDAC). Funding for CISC was provided by the UVAS ("Ultraviolet and Visible Atmospheric Sounder") projects SEOSAT/INGENIO, ESP2015-71299-R, MINECO-FEDER and UE. The activities of the IUP-Heidelberg were supported by the DFG project RAPSODI (grant no. PL 193/17-1). SAOZ and Mini-SAOZ instruments are supported by the Centre National de la Recherche Scientifique (CNRS) and the Centre National d'Etudes Spatiales (CNES). INTA recognises support from the National funding projects HELADO (CTM2013-41311-P) and AVATAR (CGL2014-55230-R). AMOIAP recognises support from the Russian Science Foundation (grant no. 16-17-10275) and the Russian Foundation for Basic Research (grant nos. 16-05-01062 and 18-35-00682). Ka L. Chan received transnational access funding from ACTRIS-2 (H2020 grant agreement no. 654109). Rainer Volkamer recognises funding from NASA's Atmospheric Composition Program (NASA-16-NUP2016-0001) and the US National Science Foundation (award AGS-1620530). Henning Finkenzeller is the recipient of a NASA graduate fellowship. Mihalis Vrekoussis recognises support from the University of Bremen and the DFG Research Center/Cluster of Excellence "The Ocean in the Earth System-MARUM". Financial support through the University of Bremen Institutional Strategy in the framework of the DFG Excellence Initiative is gratefully appreciated for Anja Schonhardt. Pandora instrument deployment was supported by Luftblick through the ESA Pandonia Project and NASA Pandora Project at the Goddard Space Flight Center under NASA Headquarters' Tropospheric Composition Program. The article processing charges for this open-access publication were covered by BK Scientific.

40 pags., 22 figs., 13 tabs., In September 2016, 36 spectrometers from 24 institutes measured a number of key atmospheric pollutants for a period of 17¿d during the Second Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) that took place at Cabauw, the Netherlands (51.97¿¿N, 4.93¿¿E). We report on the outcome of the formal semi-blind intercomparison exercise, which was held under the umbrella of the Network for the Detection of Atmospheric Composition Change (NDACC) and the European Space Agency (ESA). The three major goals of CINDI-2 were (1) to characterise and better understand the differences between a large number of multi-axis differential optical absorption spectroscopy (MAX-DOAS) and zenith-sky DOAS instruments and analysis methods, (2) to define a robust methodology for performance assessment of all participating instruments, and (3) to contribute to a harmonisation of the measurement settings and retrieval methods. This, in turn, creates the capability to produce consistent high-quality ground-based data sets, which are an essential requirement to generate reliable long-term measurement time series suitable for trend analysis and satellite data validation. The data products investigated during the semi-blind intercomparison are slant columns of nitrogen dioxide (NO2), the oxygen collision complex (O4) and ozone (O3) measured in the UV and visible wavelength region, formaldehyde (HCHO) in the UV spectral region, and NO2 in an additional (smaller) wavelength range in the visible region. The campaign design and implementation processes are discussed in detail including the measurement protocol, calibration procedures and slant column retrieval settings. Strong emphasis was put on the careful alignment and synchronisation of the measurement systems, resulting in a unique set of measurements made under highly comparable air mass conditions. The CINDI-2 data sets were investigated using a regression analysis of the slant columns measured by each instrument and for each of the target data products. The slope and intercept of the regression analysis respectively quantify the mean systematic bias and offset of the individual data sets against the selected reference (which is obtained from the median of either all data sets or a subset), and the rms error provides an estimate of the measurement noise or dispersion. These three criteria are examined and for each of the parameters and each of the data products, performance thresholds are set and applied to all the measurements. The approach presented here has been developed based on heritage from previous intercomparison exercises. It introduces a quantitative assessment of the consistency between all the participating instruments for the MAX-DOAS and zenith-sky DOAS techniques., CINDI-2 received funding from the Netherlands Space Office (NSO). Funding for this study was provided by ESA through the CINDI-2 (ESA contract no. 4000118533/16/ISbo) and FRM4DOAS (ESA contract no. 4000118181/16/I-EF) projects and partly within the EU 7th Framework Programme QA4ECV project (grant agreement no. 607405). The BOKU MAX-DOAS instrument was funded and the participation of Stefan F. Schreier was supported by the Austrian Science Fund (FWF): I 2296-N29. The participation of the University of Toronto team was supported by the Canadian Space Agency (through the AVATARS project) and the Natural Sciences and Engineering Research Council (through the PAHA project). The instrument was primarily funded by the Canada Foundation for Innovation and is usually operated at the Polar Environment Atmospheric Research Laboratory (PEARL) by the Canadian Network for the Detection of Atmospheric Change (CANDAC). Funding for CISC was provided by the UVAS (“Ultraviolet and Visible Atmospheric Sounder”) projects SEOSAT/INGENIO, ESP2015-71299- R, MINECO-FEDER and UE. The activities of the IUP-Heidelberg were supported by the DFG project RAPSODI (grant no. PL 193/17-1). SAOZ and Mini-SAOZ instruments are supported by the Centre National de la Recherche Scientifique (CNRS) and the Centre National d’Etudes Spatiales (CNES). INTA recognises support from the National funding projects HELADO (CTM2013-41311-P) and AVATAR (CGL2014-55230-R). AMOIAP recognises support from the Russian Science Foundation (grant no. 16-17-10275) and the Russian Foundation for Basic Research (grant nos. 16-05- 01062 and 18-35-00682). Ka L. Chan received transnational access funding from ACTRIS-2 (H2020 grant agreement no. 654109). Rainer Volkamer recognises funding from NASA’s Atmospheric Composition Program (NASA-16-NUP2016-0001) and the US National Science Foundation (award AGS-1620530). Henning Finkenzeller is the recipient of a NASA graduate fellowship. Mihalis Vrekoussis recognises support from the University of Bremen and the DFG Research Center/Cluster of Excellence “The Ocean in the Earth System-MARUM”. Financial support through the University of Bremen Institutional Strategy in the framework of the DFG Excellence Initiative is gratefully appreciated for Anja Schönhardt. Pandora instrument deployment was supported by Luftblick through the ESA Pandonia Project and NASA Pandora Project at the Goddard Space Flight Center under NASA Headquarters’ Tropospheric Composition Program. The article processing charges for this open-access publication were covered by BK Scientific.

MAX-DOAS and direct sun NO2 vertical column network data are used to investigate the accuracy of tropospheric NO2 column measurements of the GOME-2 instrument on the MetOP-A satellite platform and the OMI instrument on Aura. The study is based on 23 MAX-DOAS and 16 direct sun instruments at stations distributed worldwide. A method to quantify and correct for horizontal dilution effects in heterogeneous NO2 field conditions is proposed. After systematic application of this correction to urban sites, satellite measurements are found to present smaller biases compared to ground-based reference data in almost all cases. We investigate the seasonal dependence of the validation results, as well as the impact of using different approaches to select satellite ground pixels in coincidence with ground-based data. In optimal comparison conditions (satellite pixels containing the station) the median bias between satellite tropospheric NO2 column measurements and the ensemble of MAX-DOAS and direct sun measurements is found to be significant and equal to −36 % for GOME-2A and −20 % for OMI. These biases are further reduced to −24 % and −8 % respectively, after application of the dilution correction. Comparisons with the QA4ECV satellite product for both GOME-2A and OMI is also performed, showing less scatter but also a slightly larger median tropospheric NO2 column bias with respect to the ensemble of MAX-DOAS and direct sun measurements.

The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite (launched on 13 October 2017) is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral ranges. The measured spectra are used to retrieve total columns of trace gases, including nitrogen dioxide (NO2). For ground validation of these satellite measurements, Pandora spectrometers, which retrieve high-quality NO2 total columns via direct-sun measurements, are widely used. In this study, Pandora NO2 measurements made at three sites located in or north of the Greater Toronto Area (GTA) are used to evaluate the TROPOMI NO2 data products, including a standard Royal Netherlands Meteorological Institute (KNMI) tropospheric and stratospheric NO2 data product and a TROPOMI research data product developed by Environment and Climate Change Canada (ECCC) using a high-resolution regional air quality forecast model (in the air mass factor calculation). It is found that these current TROPOMI tropospheric NO2 data products (standard and ECCC) met the TROPOMI design bias requirement (NO2 emissions, which can be used to evaluate regional air quality changes. The TROPOMI ECCC NO2 research data product shows improved agreement with Pandora measurements compared to the TROPOMI standard tropospheric NO2 data product (e.g., lower multiplicative bias at the suburban and urban sites by about 10 %), demonstrating benefits from the high-resolution regional air quality forecast model.