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Air Quality (AQ) monitoring in Belgium has hitherto been relying mostly on in-situ measurements of surface concentration, with geographical gaps between observations filled in with numerical modelling ingesting (proxies for) bottom-up emission estimates. However, a new generation of satellite sounders on sun-synchronous Low Earth Orbits (LEO) – like the Copernicus Sentinel-5(p) series – performs now daily global mapping of atmospheric composition down to the 3-km scale. Soon this daily global mapping will be complemented with geostationary instruments (GEO, e.g. Sentinel-4) observing the diurnal cycle in trace gas concentrations, although over the limited geographical area accessible from a geostationary viewpoint. This new constellation of satellite sounders is built to support detailed monitoring of AQ on the different relevant scales: from point-like emissions to intercontinental transport, and from city-level to international regulations established by public authorities to manage AQ in their area of responsibility. Nevertheless, uptake of these new satellite AQ data by the various Belgian stakeholders is not guaranteed. Indeed, to realize the full complementary impact of this constellation of LEO and GEO satellites, i.e. to make their observations fit-for-purpose for air quality applications at the different scales, several challenges need to be addressed. These include (1) the need to enhance to sub-city scales the resolution of satellite data to make them better suited for the monitoring of e.g. the impact of the Low Emission Zones enforced in several European cities, (2) to characterize the non-trivial relation between the column amount of the pollutant measured by a satellite and the near-surface concentrations measured by in-situ networks, and (3) to determine how the different LEO and GEO vantage points lead to a different perception of atmospheric and surface details and how we can benefit from - or correct for - these differences.Work on these challenges is taking place in the dedicated Belgian federal research project LEGO-BEL-AQ (2020-2023, https://lego-bel-aq.aeronomie.be/index.php) funded by BELSPO, with a particular focus on AQ in Belgium. In this contribution, we demonstrate that a combination of temporal aggregation, careful data selection, and horizontal oversampling can produce a meaningful increase in horizontal resolution in S5P tropospheric NO2 column maps, revealing policy-relevant features in the NO2 distribution over Belgium’s major cities. Comparisons between our high-resolution S5P NO2 maps and the near-surface in-situ observations as procured by the Belgian authorities, reveal high correlation when considering longer time scales (seasonal and annual), allowing a pragmatic conversion from tropospheric columns to near-surface concentrations over the complete Belgian domain, and consequently also a confrontation to WHO annual thresholds at the level of individual Belgian municipalities.Acknowledgements This work has been supported by the BELSPO BRAIN-be 2.0 project LEGO-BEL-AQ (https://lego-bel-aq.aeronomie.be)

The first TOAR assessment encountered several observational challenges that limited the confidence in estimates of the burden, short-term variability and long-term changes of ozone in the free troposphere. One of these challenges is the difficulty to interpret tropospheric observations from space, especially when combining data records from multiple satellites with differences in vertical sensitivity, prior information, resolution and spatial domain. Additional confounding factors are time-varying biases and the lack of harmonisation of geophysical quantities, units and definition of the tropospheric top level. All together, these increased the uncertainty on the distribution and trends of tropospheric ozone, impeding firm assessments relevant for policy and science. These challenges motivated the Committee on Earth Observation Satellites (CEOS) to initiate a coordinated activity on improving assessments of tropospheric ozone measured from space. Here, we report on work that contributes to this CEOS activity and to various Working Groups (SOWG, OPT, HEGIFTOM, TOP, ROSTEES, ...) of the ongoing second TOAR assessment. Our primary objective is to harmonise the vertical perspective of different satellite data records. A first class of tropospheric ozone products is obtained through an inversion of spectral measurements by nadir-viewing sounders into a vertical profile. We describe two complementary approaches (Prior Replacement and Complete Data Fusion) to harmonise the differing profile retrieval set-up for GOME-2, IASI and other UV-visible and infrared nadir sensors, using information conveyed in the prior and the averaging kernels. A second class of products is obtained through subtraction of the stratospheric component from total column retrievals. The stratospheric column is derived with various methods, resulting in differing spatial coverage, tropospheric top level, sampling frequency, etc… We present how all tropospheric ozone products, from both classes, are harmonised to a common tropospheric top level. We then intercompare all harmonised satellite records, and report on the differences and how these reduce upon harmonisation. Finally, we reflect on the importance of the vertical harmonisation process to improve constraints of the spatial distribution and trends in tropospheric ozone. Acknowledgements : We are grateful to the sustained effort and committment of the teams, institutes and agencies that collect and provide satellite and ozonesonde data records of high quality.

The retrieval of atmospheric composition from space-based measurements, by e.g., Sentinel-5p TROPOMI, is strongly affected by radiative interferences with clouds. Dedicated cloud data products, typically retrieved from measurements by the same sounder, are therefore essential. Cloud information is used to filter data and as input to the modelling of atmospheric radiative transfer and the conversion of slant column densities into vertical column densities. The three main TROPOMI cloud retrieval algorithms are: (i) L2_CLOUD OCRA/ROCINN CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks; Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (Clouds-as Reflecting Boundaries), and (iii) the S5P support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands for Sentinel). The cloud variables provided by these products (radiometric cloud fraction, cloud (top) height, and cloud albedo/cloud optical thickness) are subsequently used in the retrieval of the TROPOMI trace gas products. The quality of cloud products and trace gas products is routinely assessed by the ESA/Copernicus Atmospheric Mission Performance Cluster (ATM-MPC) validation service, with ad hoc support from Sentinel-5p Validation Team (S5PVT) AO projects. Version upgrades have had a significant impact on the characteristics of S5P cloud data. The change of the wavelength window in the FRESCO product since version 1.4 (‘FRESCO-wide’) leads to a clear increase in the height of low clouds with a large impact on the tropospheric NO2 retrieval (van Geffen, 2022), and improving the validation results regarding the tropospheric and total NO2 column. The first upgrades of the ROCINN products (from v1 to v2.1-v2.3) led to an increase in correlation with CLOUDNET cloud height, but to a more negative bias for the low clouds, with ROCINN CRB cloud height even dropping below the CLOUDNET cloud base height on average. However, this effect seems alleviated with the latest upgrade to v2.4. The impact on the HCHO validation results is investigated but is less clear compared to the NO2 case. To resolve the discontinuities due to the processor version jumps, a full mission reprocessing is currently ongoing and largely carried out for the L2_CLOUD and FRESCO-S products. The reprocessed ROCINN data have a lower dispersion and higher correlation with respect to the CLOUDNET cloud heights. The bias of the L2_CLOUD OCRA/ROCINN CAL CTH becomes more negative, but that of L2_CLOUD OCRA/ROCINN CRB CH bias improves. Finally, we also discuss the impact of the FRESCO-S reprocessing on the validation results.

Within the framework of the EUMETSAT AC SAF project, the production of climate data records (CDRs) of short lived O3 precursors (HCHO and NO2) is under development, based on the three GOME-2 instruments and the future S5 and S4 Copernicus platforms.This work presents the development of the formaldehyde CDR, combining the current GOME-2 A, B and C operational AC SAF Level-2 orbital products to create one single L3 averaged product using advanced gridding tools. To this aim, the consistency between the three GOME-2 instrument needs to be improved by means of suitable correction schemes accounting for inter-sensor biases due to inconsistent auxiliary data or instrumental issues. The use of the CAMS model reanalysis as a source of prior HCHO profiles is explored with the aim to produce one consistent dataset from 2007 to now. Estimation of the random and systematic uncertainty is included for each grid cell. Furthermore, we plan to include meteorological re-analysis data (surface temperature and winds) in the output files to further support the interpretation of the data and of their observed variations.Validation results of this new monthly averaged CDR dataset will be presented, with a special focus on bias, precision and stability in time. To this aim, long-term ground-based HCHO measurements are collected and assessed.

This study presents a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula over the period 2018-2021 (using the recently released PAL product to ensure consistency).A first exploration of the impact of cloud cover on the availability of TROPOMI TrC-NO2 observations indicates that data gaps range between 20-30% in summer to 55-70% in April and November, with substantial spatial differences between northern and southern and/or arid areas. The spatial distribution of TrC-NO2 highlights strong hotspots over urban areas (especially Madrid and Barcelona), with additional enhancements along international maritime routes and major highways. A reasonable correlation with surface NO2 mixing ratios is found, around 0.7-0.8 depending on the averaging time.The weekly and monthly variability of TrC-NO2 over the peninsula is then analyzed at the light of the urban cover fraction (taken from the Copernicus Land Monitoring Service). From least to most urbanized areas, the weekend reduction is found to range from -10 to -40%. A detailed analysis at the intra-agglomeration scale highlights that strongest weekend effects do not always peak in the center but sometimes in surrounding cities, which suggests a larger contribution of commuting to total NOx anthropogenic emissions. Similarly, the monthly profiles strongly change depending on the level of urbanization, from -40%/+26% in summer/winter in most urbanized areas, to -10%/+20% in least urbanized ones. Interestingly, the same analysis applied to cropland fraction highlight an enhancement in June-July that could be due to natural soil NO emissions that are known to peak during the warm season. Beyond some specific discrepancies, a generally good consistency is found between the variability of NO2 seen from space with TROPOMI and the one observed at the surface.Our study thus illustrates the potential of TROPOMI TrC-NO2 to provide a valuable complement to surface monitoring network, especially in agricultural and maritime areas where surface NO2 observations are missing but yet crucial for better understanding the impact of local NOx emissions, especially for the production of tropospheric ozone. Petetin, H., Guevara, M., Compernolle, S., Bowdalo, D., Bretonnière, P.-A., Enciso, S., Jorba, O., Lopez, F., Soret, A., and Pérez García-Pando, C.: Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1056, 2022.

Nitrogen dioxide (NO2) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, which combines a high signal-to-noise ratio with daily global coverage and high spatial resolution. TROPOMI provides a valuable source of information to monitor emissions from local sources such as power plants, industry, cities, traffic and ships, and variability of these sources in time. Validation exercises of NO2 v1.2–v1.3 data, however, have revealed that TROPOMI's tropospheric vertical column densities (VCDs) are too low by up to 50 % over highly polluted areas. These findings are mainly attributed to biases in the cloud pressure retrieval, the surface albedo climatology and the low resolution of the a priori profiles derived from global simulations of the TM5-MP chemistry model. This study describes improvements in the TROPOMI NO2 retrieval leading to version v2.2, operational since 1 July 2021. Compared to v1.x, the main changes are the following. (1) The NO2-v2.2 data are based on version-2 level-1b (ir)radiance spectra with improved calibration, which results in a small and fairly homogeneous increase in the NO2 slant columns of 3 % to 4 %, most of which ends up as a small increase in the stratospheric columns. (2) The cloud pressures are derived with a new version of the FRESCO cloud retrieval already introduced in NO2-v1.4, which led to a lowering of the cloud pressure, resulting in larger tropospheric NO2 columns over polluted scenes with a small but non-zero cloud coverage. (3) For cloud-free scenes a surface albedo correction is introduced based on the observed reflectance, which also leads to a general increase in the tropospheric NO2 columns over polluted scenes of order 15 %. (4) An outlier removal was implemented in the spectral fit, which increases the number of good-quality retrievals over the South Atlantic Anomaly region and over bright clouds where saturation may occur. (5) Snow/ice information is now obtained from ECMWF weather data, increasing the number of valid retrievals at high latitudes. On average the NO2-v2.2 data have tropospheric VCDs that are between 10 % and 40 % larger than the v1.x data, depending on the level of pollution and season; the largest impact is found at mid and high latitudes in wintertime. This has brought these tropospheric NO2 closer to Ozone Monitoring Instrument (OMI) observations. Ground-based validation shows on average an improvement of the negative bias of the stratospheric (from −6 % to −3 %), tropospheric (from −32 % to −23 %) and total (from −12 % to −5 %) columns. For individual measurement stations, however, the picture is more complex, in particular for the tropospheric and total columns.

Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud optical thickness (OCRA/ROCINN_CAL), cloud height (OCRA/ROCINN_CRB and FRESCO-S), and radiometric cloud fraction (all three algorithms). The analysis combines (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS, and OMI O2–O2), and (iii) ground-based validation with respect to correlative observations (30 April 2018 to 27 February 2020) from the Cloudnet network of ceilometers, lidars, and radars. Zonal mean latitudinal variation of S5P cloud properties is similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud-top height and cloud optical thickness and with Cloudnet cloud-top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud-top height is below the cloud-top height of VIIRS and of Cloudnet, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the Cloudnet cloud mean height for the low clouds but match on average better with the Cloudnet cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the Cloudnet observations. Peculiar geographical patterns are identified in the cloud products and will be mitigated in future releases of the cloud data products.

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.

The QA4ECV (Quality Assurance for Essential Climate Variables) version 1.1 stratospheric and tropospheric NO2 vertical column density (VCD) climate data records (CDRs) from the OMI (Ozone Monitoring Instrument) satellite sensor are validated using NDACC (Network for the Detection of Atmospheric Composition Change) zenith-scattered light differential optical absorption spectroscopy (ZSL-DOAS) and multi-axis DOAS (MAX-DOAS) data as a reference. The QA4ECV OMI stratospheric VCDs have a small bias of ∼0.2 Pmolec.cm-2 (5 %–10 %) and a dispersion of 0.2 to 1 Pmolec.cm-2 with respect to the ZSL-DOAS measurements. QA4ECV tropospheric VCD observations from OMI are restricted to near-cloud-free scenes, leading to a negative sampling bias (with respect to the unrestricted scene ensemble) of a few peta molecules per square centimetre (Pmolec.cm-2) up to −10 Pmolec.cm-2 (−40 %) in one extreme high-pollution case. The QA4ECV OMI tropospheric VCD has a negative bias with respect to the MAX-DOAS data (−1 to −4 Pmolec.cm-2), which is a feature also found for the OMI OMNO2 standard data product. The tropospheric VCD discrepancies between satellite measurements and ground-based data greatly exceed the combined measurement uncertainties. Depending on the site, part of the discrepancy can be attributed to a combination of comparison errors (notably horizontal smoothing difference error), measurement/retrieval errors related to clouds and aerosols, and the difference in vertical smoothing and a priori profile assumptions.

The Sentinel-5P TROPOMI instrument provides unique observations of atmospheric trace gases at a high resolution of about 5 km with near-daily global coverage, resolving individual sources like thermal power plants, industrial complexes, fires, medium-scale towns, roads and shipping routes. These datasets are especially well suited to test high-resolution regional-scale air quality (AQ) models and provide valuable input for regional emission inversion systems. In Europe, the Copernicus Atmosphere Monitoring Service (CAMS) has implemented an operational regional AQ forecasting capability for Europe based on an ensemble of 7 up to 11 European models. In the presentation we show comparisons between TROPOMI observations of nitrogen dioxide (NO2) and the CAMS AQ forecasts and analyses of NO2. We discuss the different ways of making these comparisons, and present the quantitative results for time series for regions and cities between May 2018 to March 2021, for summer and winter months and individual days. We demonstrate the importance of the free tropospheric contribution to the tropospheric column, and include profiles from the CAMS configuration of the ECMWF’s global integrated model above 3 km altitude in the comparison. The models generally capture the fine-scale daily and averaged features observed by TROPOMI in much detail. In summer, the quantitative comparison of the NO2 tropospheric column shows a close agreement, but in winter we find a significant discrepancy in the average column amount over Europe. Recently a new TROPOMI NO2 reprocessing with processor version 2.3.1 has become available, and impact of this new version on the comparisons is discussed. As spin-off, we present a new TROPOMI NO2 level-2 data product for Europe, based on the replacement of the original TM5-MP generated global a priori profile (1x1 degree resolution) by the regional CAMS ensemble profile at 0.1x0.1 degree resolution. This a-priori replacement leads to significant changes in the TROPOMI retrieved tropospheric column, with typical increases at the emission hotspots in the order of 20%. The European NO2 product is compared with ground-based remote sensing measurements of 6 PANDORA instruments of the Pandonia global network and 8 MAX-DOAS instruments. As compared to the standard S5P tropospheric NO2 column data, the overall bias of the new product is smaller owing to a reduction of the multiplicative bias linked to the profile shape.

Nitrogen dioxide (NO2) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, which combines a high signal-to-noise ratio with daily global coverage and high spatial resolution. TROPOMI provides a valuable source of information to monitor emissions from local sources such as power plants, industry, cities, traffic and ships, and variability of these sources in time. Validation exercises of NO2 version v1.2-v1.3 data, however, have revealed that TROPOMI's tropospheric vertical columns (VCDs) are too low by up to 50 % over highly polluted areas. These findings are mainly attributed to biases in the cloud pressure retrieval, the surface albedo climatology and the low resolution of the a-priori profiles derived from global simulations of the TM5-MP chemistry model. This study describes improvements in the TROPOMI NO2 retrieval leading to version v2.2, operational since 1 July 2021. Compared to v1.x, the main changes are: (1) The NO2-v2.2 data is based on version 2 level-1B (ir)radiance spectra with improved calibration, which results in a small and fairly homogeneous increase of the NO2 slant columns of 3 to 4 %, most of which ends up as a small increase of the stratospheric columns; (2) The cloud pressures are derived with a new version of the FRESCO cloud retrieval already introduced in NO2-v1.4, which lead to a lowering of the cloud pressure, resulting in larger tropospheric NO2 columns over polluted scenes with a small but non-zero cloud coverage; (3) For cloud-free scenes a surface albedo correction is introduced based on the observed reflectance, which also leads to a general increase of the tropospheric NO2 columns over polluted scenes of order 15 %; (4) An outlier removal was implemented in the spectral fit, which increases the number of good quality retrievals over the South-Atlantic Anomaly region and over bright clouds where saturation may occur; (5) Snow-Ice information is now obtained from ECMWF weather data, increasing the number of valid retrievals at high latitudes. On average the NO2-v2.2 data have tropospheric VCDs that are between 10 and 40 % larger than the v1.x data, depending on the level of pollution and season; the largest impact is found at mid- and high-latitudes in wintertime. This has brought these tropospheric NO2 closer to OMI observations. Ground-based validation shows on average an improvement of the negative bias of the stratospheric (from −6 % to −3 %), tropospheric (from −32 % to −23 %) and total (from −12 % to −5 %) columns. For individual measurement stations, however, the picture is more complicated, in particular for the tropospheric and total columns.

The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017 on board the Sentinel-5 Precursor (S5P) satellite, monitors the composition of the Earth's atmosphere at an unprecedented horizontal resolution as fine as 3.5 × 5.5 km2. This paper assess the performances of the TROPOMI formaldehyde (HCHO) operational product compared to its predecessor, the OMI HCHO QA4ECV product, at different spatial and temporal scales. The parallel development of the two algorithms favored the consistency of the products, which facilitates the production of long-term combined time series. The main difference between the two satellite products is related to the use of different cloud algorithms, leading to a positive bias of OMI compared to TROPOMI of up to 30 % in Tropical regions. We show that after switching off the explicit correction for cloud effects, the two datasets come into an excellent agreement. For medium to large HCHO vertical columns (larger than 5 × 1015 molec.cm−2) the median bias between OMI and TROPOMI HCHO columns is not larger than 10 % (

The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a unique instrument, combining daily global coverage, very high signal-to-noise, a broad spectral range and very small pixels up to 3.5 x 5.5 km2. Retrievals are available for a large number of species, including NO2. Due to the very small pixels and daily revisit, TROPOMI provides detailed information on individual sources and source sectors like individual power plants, industrial complexes, cities and suburbs, highways, and even individual ships. The TROPOMI Level-2 NO2 product is available from 30 April 2018 onwards.Validation exercises of TROPOMI v1.2 & v1.3 data (2018-2020) with OMI and ground-based remote sensing observations have shown that TROPOMI's tropospheric NO2 column are low by up to 50% over highly polluted areas compared to independent data. In contrast, the underlying slant columns of TROPOMI agree well with OMI and independent SAOZ observations. Differences between OMI and TROPOMI have been mainly attributed to the different cloud height retrieval, using the O2-O2 versus O2-A bands respectively.In our presentation we discuss recent improvements in the TROPOMI NO2 retrieval and the impact these have on the tropospheric columns and on the comparisons with OMI and ground-based remote-sensing data.Version v1.4, which became operational on 2 December 2020, entails a major improvement in the cloud height retrieval, based on a modification of the FRESCO-S cloud retrieval using the O2-A band observations. In particular the cloud height over scenes with a small cloud coverage have increased, resulting in larger tropospheric columns in the retrievals over polluted areas.Version v2.2, to become operational in April/May 2021, includes similar cloud retrieval modifications. Furthermore, it provides a better treatment of saturation issues and transients, is using improved (ir)radiance measurements (level-1b v2 spectra) including degradation corrections, and includes a new albedo treatment.The TROPOMI NO2 retrievals are compared with OMI retrievals (from the QA4ECV product) and to ground-based observations with MAXDOAS and PANDORA instruments.

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 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 (bias2 vs. ground-based data. This dispersion is larger than the mission requirement on data precision, but it can partly be attributed to comparison errors such as those due to differences in resolution. Total column data are found to be biased low by 20%, with a 30% station-to-station scatter. After gridding to monthly means on a 0.8°x0.4° grid, comparisons to OMI data yield a much smaller dispersion (within the requirement of 0.7Pmolec/cm2), and a minor relative bias. NRTI and OFFL perform similarly, even if they occasionally differ over specific scenes. Besides the impact of the processor upgrade to v1.4 on the bias in polluted scenes, we discuss the implications of the reported negative biases in S5P tropospheric (and total) columns on NO2 reduction estimates, e.g. in the context of SARS-CoV-2 lockdown measures. Feedback from this work on the ground-based reference data is also briefly reported.

Ecosystems and human health are severely harmed by elevated concentrations of tropospheric ozone, in the short and the long term. Monitoring ozone at all relevant spatial and temporal scales simultaneously is a challenge for a global observing system due to the large variability of ozone levels in the troposphere. Space-based sensors provide near-global coverage at the synoptic scale, but their accuracy is limited since the large stratospheric O3 column shields the view on the relatively small tropospheric ozone concentrations. In contrast, in-situ soundings by balloons are sparse, but these are more accurate and at a high vertical resolution. As a result, the geophysical information that can be inferred from tropospheric ozone data records differs.We present a comprehensive comparison of the spatial and temporal patterns in tropical tropospheric ozone column observations by nadir-viewing satellite sensors (Sentinel-5 Precursor/TROPOMI, EOS-Aura/OMI and Metop-B/GOME-2) and ozonesondes for the period 2018-2020. We discuss how each data record perceives well-known structures and cycles such as the zonal wave-one, the seasonal cycle and biomass burning periods. Imprints of (sensor-dependent) sampling characteristics are generally less relevant on large scales. However, these can dominate the uncertainty budget when satellite data are used at their finest sampling resolution. Nonetheless, we recognise the signature of the Madden-Julian Oscillation and hints of Kelvin wave activity.

Part of the space segment of EU’s Copernicus Earth Observation programme, the Sentinel-5 Precursor (S5P) mission is dedicated to global and European atmospheric composition measurements of air quality, climate and the stratospheric ozone layer. On board of the S5P early afternoon polar satellite, the imaging spectrometer TROPOMI (TROPOspheric Monitoring Instrument) performs nadir measurements of the Earth radiance within the UV-visible and near-infrared spectral ranges, from which atmospheric ozone profile data are retrieved. Developed at the Royal Netherlands Meteorological Institute (KNMI) and based on the optimal estimation method, TROPOMI’s operational ozone profile retrieval algorithm has recently been upgraded. With respect to early retrieval attempts, accuracy is expected to have improved significantly, also thanks to recent updates of the TROPOMI Level-1b data product. This work reports on the initial validation of the improved TROPOMI height-resolved ozone data in the troposphere and stratosphere, as collected both from the operational S5P Mission Performance Centre/Validation Data Analysis Facility (MPC/VDAF) and from the S5PVT scientific project CHEOPS-5p. Based on the same validation best practices as developed for and applied to heritage sensors like GOME-2, OMI and IASI (Keppens et al., 2015, 2018), the validation methodology relies on the analysis of data retrieval diagnostics – like the averaging kernels’ information content – and on comparisons of TROPOMI data with reference ozone profile measurements. The latter are acquired by ozonesonde, stratospheric lidar, and tropospheric lidar stations performing network operation in the context of WMO's Global Atmosphere Watch and its contributing networks NDACC and SHADOZ. The dependence of TROPOMI’s ozone profile uncertainty on several influence quantities like cloud fraction and measurement parameters like sun and scan angles is examined and discussed. This work concludes with a set of quality indicators, enabling users to verify the fitness-for-purpose of the S5P data.

Space-born atmospheric composition measurements, like those from Sentinel-5p TROPOMI, are strongly affected by the presence of clouds. Dedicated cloud data products, typically retrieved with the same sensor, are therefore an important tool for the provider of atmospheric trace gas retrievals. Cloud products are used for filtering and modification of the modelled radiative transfer.In this work, we assess the quality of the cloud data derived from Copernicus Sentinel-5 Precursor TROPOMI radiance measurements. Three cloud products are considered: (i) L2_CLOUD OCRA/ROCINN CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks; Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (same; Clouds-as Reflecting Boundaries), and (iii) the S5p support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands for Sentinel). These cloud products are used in the retrieval of several S5p trace gas products (e.g., ozone columns and profile, total and tropospheric nitrogen dioxide, sulfur dioxide, formaldehyde). The quality assessment of these cloud products is carried out within the framework of ESA’s Sentinel-5p Mission Performance Centre (MPC) with support from AO validation projects focusing on the respective atmospheric gases.Cloud height data from the three S5p cloud products is compared to radar/lidar based cloud profile information from the ground-based networks CLOUDNET and ARM. The cloud height from S5p CLOUD CRB and S5p FRESCO are on average 0.6 km below the cloud mid-height of CLOUDNET measurements, and the cloud top height from S5p CLOUD CAL is on average 1 km below CLOUDNET’s cloud top height. However, the comparison is different for low and high clouds, with S5p CLOUD CAL cloud top height being only 0.3 km below CLOUDNET’s for low clouds. The radiometric cloud fraction and cloud (top) height are compared to those of other satellite cloud products like Aura OMI O2-O2. While the latitudinal variation is often similar, offsets are encountered.Recently, major S5p cloud product upgrades were released for S5p OCRA/ROCINN (July 2020) and for S5p FRESCO (December 2020), leading to a decrease of the ROCINN CRB cloud height and an increase of the FRESCO cloud height on average. Moreover, a major change in the ROCINN surface albedo treatment leads to a clear improvement of the comparison with CLOUDNET at the complicated sea/land/ice/snow site Ny-Alesund.

Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data derived from Copernicus Sentinel-5 Precursor (S5P) TROPOMI radiance measurements: cloud top height and cloud optical thickness (retrieved with the S5P OCRA/ROCINN_CAL algorithm), cloud height (S5P OCRA/ROCINN_CRB and S5P FRESCO) and radiometric cloud fraction (all three algorithms). The analysis combines: (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS and OMI O2-O2), and (iii) ground-based validation with respect to correlative observations (2018-04-30 to 2020-02-27) from the CLOUDNET network of ceilometers, lidars and radars. Peculiar geographical patterns were identified, and will be mitigated in future releases of the cloud data products. Zonal mean latitudinal variation of S5P cloud properties are similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud top height and cloud optical thickness, and with CLOUDNET cloud top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud top height is below the cloud top height of VIIRS and of CLOUDNET, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the CLOUDNET cloud mean height for the low clouds, but match on an average better with the CLOUDNET cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the CLOUDNET observations.

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.

Tropospheric ozone damages ecosystems and causes human health problems. The high spatial and temporal variability of ozone concentrations in the troposphere challenges global observing systems to monitor ozone at all relevant scales. TROPOMI is a nadir-viewing UV-Vis-NIR-SWIR sensor that combines a high spatial resolution, a large swath width and the spectral measurement characteristics required to deliver trace gas data records at unprecedented detail. The first tropospheric data product was publicly released in Fall 2018, a year after launch on the Sentinel-5p platform (S5p). It is based on the convective-cloud differential technique (CCD) to infer 0.5°x1° resolved daily maps of 3-day moving mean values of the tropospheric ozone column (surface to 270 hPa) between 20°S and 20°N in clear-sky conditions. This makes it the highest resolved tropospheric ozone data set currently available for the tropical belt. About two years of data have been collected since the end of the commissioning phase in April 2018.We present an assessment of the quality of the Sentinel-5p TROPOMI convective-cloud differential tropospheric ozone column data products (O3_TCL OFFL v01.01.05-01.01.07), carried out within the context of ESA’s Sentinel-5p Mission Performance Center (MPC) and the S5PVT AO project CHEOPS-5p. Our assessment of the first two years of TROPOMI data is based on comparisons with (a) quality-assured co-located in-situ measurements by the SHADOZ ozonesonde network, and, (b) satellite data by the GOME-2 and OMI sensors. These well-characterized observational data records serve as references to evaluate the bias and the dispersion of S5p data, and their dependence on influence quantities. Additional visual inspections of the S5p tropospheric ozone maps unveiled non-geophysical structures introduced by the sampling pattern of sensor and clouds. We conclude by assessing the compliance of S5p tropospheric ozone data with respect to mission and user requirements for key data applications.

The Sentinel-5 Precursor (S5P) was launched on the 13th of October 2017, with on board the TROPOspheric Monitoring Instrument (TROPOMI). The formaldehyde (HCHO) L2 product is operational since the end of 2018. The prototype of the tropospheric HCHO retrieval algorithm is developed at BIRA-IASB and implemented at the German Aerospace Center (DLR) in the S5P operational processor (De Smedt et al., 2018).In this work, we investigate the quality of the HCHO tropospheric column product and its validation within the MPC framework (Mission Performance Center) and the S5PVT NIDFORVAL project (S5P NItrogen Dioxide and FORmaldehyde VALidation). Within NIDFORVAL, the S5P HCHO product has been validated using the full FTIR and MAXDOAS dataset. Validation results have been assessed against reported product uncertainties taking into account the full comparison error budget, showing that the product quality reaches its requirements.Here, we focus on satellite-satellite comparison based on the OMI QA4ECV HCHO product and on ground-based validation using MAX-DOAS and Pandora network observations. About 15 HCHO measuring stations are involved, providing data corresponding to a wide range of observation conditions at mid and low latitudes, and covering remote, sub-urban, and urban polluted sites. Comparison results show usually negative biases for large HCHO columns, while a positive offset is observed for the lowest columns. For the MAX-DOAS stations providing vertical profile retrievals, the impact of a priori profiles on the comparison is assessed. The dataset allows to discuss validation results as a function of emission source. Seasonal and diurnal variations are compared. Long term variation are also monitored using the OMI and MAX-DOAS QA4ECV dataset.

For more than two 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 operationally the total, tropospheric and stratospheric column abundance of atmospheric NO2. In support of these routine operations, the S5P Mission Performance Centre (MPC) performs continuous QA/QC of these data products and produces key Quality Indicators 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 monitoring mode in the S5P Automated Validation Server (AVS) and globally in more complex in-depth analyses. Complementary quality information is obtained from product intercomparisons (NRTI vs. OFFL) and from satellite-to-satellite comparisons. After two years of successful operation we present here a consolidated overview of the quality of the S5P TROPOMI NO2 data products delivered publicly.S5P NO2 data are compared routinely to ground-based network measurements collected through either the ESA Validation Data Centre (EVDC) or network data archives (NDACC, PGN). Direct-sun measurements from the Pandonia Global Network (PGN) serve as a reference for total NO2 validation, Multi-Axis DOAS network data for tropospheric NO2 validation, and NDACC zenith-scattered-light DOAS network data for stratospheric NO2 validation. Comparison methods are optimized to limit spatial and temporal mismatch to a minimum (information-based spatial co-location strategy, photochemical adjustment to account for local time measurement difference). Comparison results are analyzed to derive Quality Indicators and to conclude on the compliance w.r.t. the 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 irreducible mismatch errors, and (3) the dependence of bias and dispersion on key influence quantities (surface albedo, cloud cover…)Intercomparison of S5P products (NRTI vs. OFFL) and comparison to other satellite data, including a similar processing of OMI measurements, complement the ground-based validation with relative biases and spatio-temporal patterns/artefacts related to instrumental issues (e.g. striping) and to the sensitivity to geophysical features (e.g. clouds and sea/ice albedo contrast). Overall, the MPC quality assessment of S5P NO2 data concludes to an excellent performance for the stratospheric column data (bias2 vs. ground-based data. This dispersion larger than the mission requirement on data precision can partly be attributed to comparisons errors such as those due to differences in horizontal resolution. Total column data are found to be biased low by 20%, with a 30% station-to-station scatter. After gridding to monthly means on a 0.8°x0.4° grid, comparisons to OMI data yield a much smaller dispersion (within the requirement of 0.7Pmolec/cm2), and a minor relative bias. NRTI and OFFL perform similarly, even if they occasionally differ in specific cases of direct comparisons.

Ground-based remote sensing MAX-DOAS and Pandora direct-sun instruments measuring in the UV-Vis spectral region are nowadays widely used to monitor atmospheric NO2 columns. Owing to the multiple geometries used, these techniques can differentiate total, tropospheric and stratospheric NO2 content and therefore provide an appropriate source of correlative data for the validation of satellite instruments such as GOME-2, OMI and TROPOMI.In this study we combine ground-based remote sensing correlative measurements available from over 40 sites distributed worldwide to address the validation of GOME-2, OMI and TROPOMI data products. For GOME-2, we concentrate on the GDP operational product generated within the EUMETSAT AC SAF project and on the climate data record generated within the EU QA4ECV project, while for OMI we address both the TEMIS and QA4ECV data products. Regarding TROPOMI, the operational OFFL product is considered. To derive tropospheric NO2 columns from direct-sun total NO2 data, we use estimates of the stratospheric contribution available from each satellite data product.A negative bias is generally found between the different satellite data products and the ground-based tropospheric NO2 measurements, which is mostly prominent in urban sites characterized by strong localized emission sources (up to about -32% to -45%, e.g. for OMI TEMIS and GOME-2 GDP vs MAX-DOAS ensemble). In an attempt to quantify and correct for the horizontal dilution happening around urban stations (due to diffusion and transport and to the spatial averaging of high resolution structures), we use high-resolution gridded NO2 maps obtained from one year of QA4ECV data. Results from applying this dilution correction show a clear improvement of the agreement between GOME-2 and OMI data at polluted urban locations. Further, the impact of the satellite ground pixel size (GOME-2 40x80km², OMI 13x24km²) and site location is investigated.

Part of the space segment of EU’s Copernicus Earth Observation programme, the Sentinel-5 Precursor (S5P) mission is dedicated to global and European atmospheric composition measurements of air quality, climate and the stratospheric ozone layer. On board of the S5P early afternoon polar satellite, the imaging spectrometer TROPOMI (TROPOspheric Monitoring Instrument) performs nadir measurements of the Earth radiance within the UV-visible and near-infrared spectral ranges, from which atmospheric ozone profile data are retrieved. Developed at the Royal Netherlands Meteorological Institute (KNMI) and based on the optimal estimation method, TROPOMI’s operational ozone profile retrieval algorithm has recently been upgraded. With respect to early retrieval attempts, accuracy is expected to have improved significantly, also thanks to recent updates of the TROPOMI Level-1b data product. This work reports on the initial validation of the improved TROPOMI height-resolved ozone data in the troposphere and stratosphere, as collected both from the operational S5P Mission Performance Centre/Validation Data Analysis Facility (MPC/VDAF) and from the S5PVT scientific project CHEOPS-5p. Based on the same validation best practices as developed for and applied to heritage sensors like GOME-2, OMI and IASI (Keppens et al., 2015, 2018), the validation methodology relies on the analysis of data retrieval diagnostics – like the averaging kernels’ information content – and on comparisons of TROPOMI data with reference ozone profile measurements. The latter are acquired by ozonesonde, stratospheric lidar, and tropospheric lidar stations performing network operation in the context of WMO's Global Atmosphere Watch and its contributing networks NDACC and SHADOZ. The dependence of TROPOMI’s ozone profile uncertainty on several influence quantities like cloud fraction and measurement parameters like sun and scan angles is examined and discussed. This work concludes with a set of quality indicators enabling users to verify the fitness-for-purpose of the S5P data.

Satellite measurements of tropospheric or total column trace gas species, including those from Sentinel-5p TROPOMI, are affected by the presence of clouds. Therefore, cloud data products retrieved with the same sensor play an essential role, as they allow the data provider to take an estimated cloud impact on the trace gas retrieval into account. Examples are the modification of the radiative transfer and associated quantities such as the air mass factor, and the partial masking of the measurement scene. Evidently, the accuracy of these corrections relies on the accuracy of the retrieved cloud properties, like radiometric cloud fraction (CF), cloud top height (CTH) or cloud height (CH), and cloud optical thickness (COT) or cloud albedo (CA).We consider here three S5p TROPOMI-based cloud products: (i) L2_CLOUD OCRA/ROCINN CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks; Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (Clouds-as Reflecting Boundaries), and (iii) the S5p support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands). These are input to the S5p operational processors for several trace gas products, including ozone columns and profile, total and tropospheric NO2, formaldehyde, sulfur dioxide. The quality assessment of these cloud products is carried out within the framework of ESA’s Sentinel-5p Mission Performance Centre (MPC) with support from AO validation projects focusing on the respective trace gases.In this work, cloud height (from S5p CLOUD CRB and S5p FRESCO algorithms) and cloud top height (from S5p CLOUD CAL) S5p data is validated with radar/lidar-based cloud profile information from the ground-based networks CLOUDNET and ARM at 17 sites. For some sites the comparison is difficult due to e.g., orography or snow/ice cover. S5P and CLOUDNET report similar cloud height variations at several sites, and the correlation between the S5p cloud products and CLOUDNET can be high (Pearson R up to 0.9). However, there is a site-dependent negative bias of the S5p cloud (top) height with respect to the CLOUDNET data: up to -2.5 km for S5p CLOUD CAL cloud top height and -1.5 km for S5p CLOUD CRB and S5p FRESCO cloud height. The dependence on other parameters measured by S5p and CLOUDNET (e.g., radiometric cloud fraction, cloud phase,…) is investigated.

The QA4ECV version 1.1 stratospheric and tropospheric NO2 vertical column density (VCD) climate data records (CDR) from the satellite sensor OMI are validated, using NDACC zenith scattered light DOAS (ZSL-DOAS) and Multi Axis-DOAS (MAX-DOAS) data as a reference. The QA4ECV OMI stratospheric VCD have a small bias of ~ 0.2 Pmolec cm-2 (5–10 %) and a dispersion of 0.2 to 1 Pmolec cm-2 with respect to the ZSL-DOAS measurements. QA4ECV tropospheric VCD observations from OMI are restricted to near-cloud-free scenes, leading to a negative sampling bias (with respect to the unrestricted scene ensemble) of a few Pmolec cm-2 up to −10 Pmolec cm-2 (−40 %) in one extreme high-pollution case. QA4ECV OMI tropospheric VCD has a negative bias with respect to the MAX-DOAS data (−1 to −4 Pmolec cm-2), a feature also found for the OMI OMNO2 standard data product. The tropospheric VCD discrepancies between satellite and ground-based data exceed by far the combined measurement uncertainties. Depending on the site, part of the discrepancy can be attributed to a combination of comparison errors (notably horizontal smoothing difference error), measurement/retrieval errors related to clouds and aerosols, and to the difference in vertical smoothing and a priori profile assumptions.

A new module for calculating the dry deposition of trace gases is presented and implemented in the Model of Atmospheric composition at Global and Regional scales using Inversion Techniques for Trace gas Emissions (MAGRITTE v1.0). The dry deposition velocities are calculated using Wesely's classical resistance-in-series approach. While relying on analyses of the European Centre for Medium-range Weather Forecasts (ECMWF) for meteorological fields, the aerodynamic resistance calculation module is based on the ECMWF model equations for turbulent transfer within the surface layer. The stomatal resistance for water vapour is calculated using a Jarvis-type parameterization in a multi-layer canopy environment model accounting for the leaf area index (LAI). The gas-phase diffusion coefficients needed to relate the stomatal resistances of different species are calculated from molecular structure. The cuticular, mesophyll and soil resistances depend on the species reactivity and Henry's Law constant (HLC). The HLCs of organic species for which no experimental data is available are estimated using a newly-developed prediction method based on existing methods for vapour pressures (EVAPORATION, Estimation of VApour Pressure of Organics) and infinite dilution activity coefficients (AIOMFAC, Aerosol Inorganic Organic Mixtures Functional groups Activity Coefficients). Acknowledging the dominance of stomatal uptake for ozone dry deposition, the stomatal resistance model parameters for 6 of the 7 major plant functional types (PFT) are adjusted based on extensive model comparisons with field measurements of ozone deposition velocity at 24 sites worldwide. The modelled OVOC deposition velocities for 25 different OVOCs are evaluated against field data from a total of 20 studies. The comparison shows the need for a species-dependent adjustment of the canopy resistances in order to match the observed variability among different species. This is realized by multiplying the HLC of each OVOC by a species-dependent parameter f1 adjusted based on the comparisons. The values of f1 span a wide range, from values of the order of unity or less for formaldehyde and several trifunctional compounds, to > 104 for compounds seen to deposit rapidly despite their low water-solubility, like MVK, MACR, CH3CHO and PAN. Despite the acknowledged caveats of the approach, the resulting modelled deposition velocities are consistent with the existing experimental data. The results of global-scale MAGRITTE model simulations demonstrate the importance of OVOC dry deposition on their global abundance. It is found to remove from the atmosphere the equivalent of 27 % of the global NMVOC emissions on a carbon basis, as well as about 8 % of NOx emissions in the form of organic nitrates and PAN-like compounds.

Global observations of tropospheric nitrogen dioxide (NO2) columns have been shown to be feasible from space, but consistent multi-sensor records do not yet exist, nor are they covered by planned activities on the international level. Harmonised, multi-decadal records of NO2 columns and their associated uncertainties can provide crucial information how the emissions and concentrations of nitrogen oxides evolve over time. Here we describe the development of a new, community best practice NO2 retrieval algorithm based on a synthesis of existing approaches. Detailed comparisons of these approaches led us to implement an enhanced spectral fitting method for NO2, a 1° × 1° TM5-MP data assimilation scheme to estimate the stratospheric background, and improve air mass factor calculations. Guided by the needs expressed by data users, producers, and WMO GCOS guidelines, we incorporated detailed per-pixel uncertainty information in the data product, along with easily traceable information on the relevant quality aspects of the retrieval. We applied the improved QA4ECV NO2 algorithm on the most actual level-1 data sets to produce a complete 22-year data record that includes GOME (1995-2003), SCIAMACHY (2002–2012), GOME-2(A) (2007 onwards) and OMI (2004 onwards). The QA4ECV NO2 spectral fitting recommendations and TM5-MP stratospheric column and air mass factor approach are currently also applied to S5P-TROPOMI. The uncertainties in the QA4ECV tropospheric NO2 columns amount to typically 40 % over polluted scenes. First validation results of the QA4ECV OMI NO2 columns and their uncertainties over Tai’an, China in June 2006 suggests little bias (−27thinsp;%) and better precision than suggested by uncertainty propagation. We conclude that our improved QA4ECV NO2 long-term data record is providing valuable information to quantitatively constrain emissions, deposition, and trends in nitrogen oxides on a global scale.

Atmospheric ozone plays a key role in air quality and the radiation budget of the Earth, both directly and through its chemical influence on other trace gases. Assessments of the atmospheric ozone distribution and associated climate change therefore demand accurate vertically-resolved ozone observations with both stratospheric and tropospheric sensitivity, both on the global and regional scales, and both in the long term and at shorter timescales. Such observations have been acquired by two series of European nadir-viewing ozone profilers, namely the scattered-light UV-visible spectrometers of the GOME family, launched regularly since 1995 (GOME, SCIAMACHY, OMI, GOME-2A/B, TROPOMI, and the upcoming Sentinel-5 series), and the thermal infrared emission sounders of the IASI type, launched regularly since 2006 (IASI on Metop platforms and the upcoming IASI-NG on Metop-SG). In particular, several Level-2 retrieved, Level-3 monthly gridded, and Level-4 assimilated nadir ozone profile data products have been improved and harmonised in the context of the ozone project of the European Space Agency’s Climate Change Initiative (ESA Ozone_cci). To verify their fitness-for-purpose, these ozone datasets must undergo a comprehensive quality assessment (QA), including (a) detailed identification of their geographical, vertical and temporal domains of validity, (b) quantification of their potential bias, noise and drift and their dependences on major influence quantities, and (c) assessment of the mutual consistency of data from different sounders. For this purpose we have applied to the Ozone_cci Climate Research Data Package (CRDP) released in 2017 the versatile QA/validation system Multi-TASTE which has been developed in the context of several heritage projects (ESA’s Multi-TASTE, EUMETSAT’s O3M-SAF, and the European Commission’s FP6 GEOmon and FP7 QA4ECV). This work, as the second in a series of four Ozone_cci validation papers, reports for the first time on data content studies, information content studies and ground-based validation for both the GOME- and IASI-type climate data records combined. The ground-based reference measurements have been provided by the Network for the Detection of Atmospheric Composition Change (NDACC), NASA’s Southern Hemisphere Additional Ozonesonde programme (SHADOZ), and other ozonesonde and lidar stations contributing to the World Meteorological Organisation’s Global Atmosphere Watch (WMO GAW). Dependence of the Ozone_cci data quality on major influence quantities – resulting in data screening suggestions to users – and perspectives for the Copernicus Sentinel missions are discussed.

The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) is a Level-3 data record, which combines individual sensor products into one single cohesive record covering the 22 year period from 1995 to 2017, generated in the frame of the European Space Agency's Climate Change Initiative Phase-II. It is based on Level-2 total ozone data produced by the GODFIT v4 algorithm as applied to the GOME/ERS-2, OMI/Aura, SCIAMACHY/Envisat and GOME-2/MetopA and /MetopB observations. In this paper we examine whether GTO-ECV meets the specific requirements set by the international climate-chemistry modelling community for decadal stability, long-term and short term accuracy. In the following, we present the validation of the 2017 release of the Climate Research Data Package Total Ozone Column (CRDP TOC), both at Level-2 and Level-3. The individual Level-2 data sets show excellent inter-sensor consistency with mean differences generally within 0.5 % at moderate latitudes (±50°), whereas the Level-3 data sets show mean differences with respect to the OMI reference data record that span between −0.2 ± 0.9 % (for GOME-2B) and 1.0 ± 1.4&htinsp;% (for SCIAMACHY). Very similar findings are reported for the Level-2 validation against independent ground-based TOC observations reported by Brewer, Dobson and SAOZ instruments; the mean bias between GODFIT v4 satellite TOC and ground instrument is well within 1.0 ± 1.0 % for all sensors, the drift per decade spans between −0.5 % to 1.0 ± 1.0 % depending on the sensor, and the peak-to-peak seasonality of the differences ranges between ~ 1 % for GOME and OMI, to ~ 2 % for SCIAMACHY. For the Level-3 validation, as a first step the aim was to show that the Level-3 CRDP produces consistent findings as the Level-2 individual sensor comparisons. We show an excellent agreement with 0.5 to 2 % peak-to-peak amplitude for the monthly mean difference time series and a negligible drift per decade in the Northern Hemisphere differences at −0.11 ± 0.10 % per decade for Dobson and +0.22 ± 0.08 % per decade for Brewer collocations. The exceptional quality of the Level-3 GTO-ECV v3 TOC record temporal stability well satisfies the requirements for the total ozone measurement decadal stability of between 1–3 % and the short term and long-term accuracy requirements of 2 % and 3 % respectively, showing an excellent inter-sensor consistency both in the Level-2 GODFIT v4 as well as in the Level-3 GTO-ECV v3 datasets and thus can be used for longer term analysis of the ozone layer, such as decadal trend studies, chemistry-climate model evaluation and data assimilation applications.

In spite of the importance of diacids and functionalised diacids for organic aerosol formation through aqueous-phase processes in droplets and aerosol water, there seems to be no reliable set of experimental values for their Henry's law constants (HLCs). We show that their estimation through the use of infinite dilution activity coefficients is also prone to error. Here we present HLC values for diacids and hydroxy polyacids determined from solubilities, water activities and vapour pressures of solids or solutions, by employing thermodynamic relationships. The vapour pressures are found to be the largest source of error, but the analysis of the obtained HLC points to inconsistencies among specific vapour pressure data sets. Although there is considerable uncertainty, the HLC defined as aqueous concentration per unit gaseous partial pressure of linear α- and ω-diacids appear to be higher than estimated by the often cited review work of Saxena and Hildemann (1996).

A new parameter model for α-pinene secondary organic aerosol (SOA) is presented, based on simulations with the detailed model BOREAM (Biogenic hydrocarbon Oxidation and Related Aerosol formation Model). The parameterisation takes into account the influence of temperature, type of oxidant, NOx-regime, photochemical ageing and water uptake, and is suitable for use in global chemistry transport models. BOREAM is validated against recent photooxidation smog chamber experiments, for which it reproduces SOA yields to within a factor of 2 in most cases. In the simple chemical mechanism of the parameter model, oxidation of α-pinene generates peroxy radicals, which, upon reaction with NO or HO2, yield products corresponding to high or low-NOx conditions, respectively. The model parameters – i.e. the temperature-dependent stoichiometric coefficients and partitioning coefficients of 10 semi-volatile products – are obtained from simulations with BOREAM, including a prescribed diurnal cycle for the radiation, oxidant and emission levels, as well as a deposition sink for the particulate and gaseous products. The effects of photooxidative ageing are implicitly included in the parameterisation, since it is based on near-equilibrium SOA concentrations, obtained through simulations of a two-week period. In order to mimic the full BOREAM model results both during SOA build-up and when SOA has reached an equilibrium concentration, the revolatilisation of condensable products due to photochemical processes is taken into account through a fitted pseudo-photolysis reaction of the lumped semi-volatile products. Modelled SOA mass yields are about ten times higher in low-NOx than in high-NOx conditions, with yields of more than 50% in the low-NOx OH-initiated oxidation of α-pinene, considerably more than in previous parameterisations based on smog chamber experiments. Sensitivity calculations indicate that discrepancies between the full model and the parameterisation due to variations in assumed oxidant levels are limited, but that changes in the radiation levels can lead to larger deviations. Photolysis of species in the particulate phase is found to strongly reduce SOA yields in the full model. Simulations of ambient conditions at 17 different sites (using oxidant, radiation and meteorological data from a global chemistry-transport model) show that overall, the parameterisation displays only little bias (2%) compared with the full model, whereas averaged relative deviations amount to about 11%. Water uptake is parameterised using fitted activity coefficients, resulting in a good agreement with the full model.

We present EVAPORATION (Estimation of VApour Pressure of ORganics, Accounting for Temperature, Intramolecular, and Non-additivity effects), a method to predict (subcooled) liquid pure compound vapour pressure p0 of organic molecules that requires only molecular structure as input. The method is applicable to zero-, mono- and polyfunctional molecules. A simple formula to describe log10p0(T) is employed, that takes into account both a wide temperature dependence and the non-additivity of functional groups. In order to match the recent data on functionalised diacids an empirical modification to the method was introduced. Contributions due to carbon skeleton, functional groups, and intramolecular interaction between groups are included. Molecules typically originating from oxidation of biogenic molecules are within the scope of this method: aldehydes, ketones, alcohols, ethers, esters, nitrates, acids, peroxides, hydroperoxides, peroxy acyl nitrates and peracids. Therefore the method is especially suited to describe compounds forming secondary organic aerosol (SOA).

Multicomponent organic aerosol (OA) is likely to be liquid, or partially liquid. Hence, to describe the partitioning of these components, their liquid vapour pressure is desired. Functionalised acids (e.g. diacids) can be a significant part of OA. But often measurements are available only for solid state vapour pressure, which can differ by orders of magnitude from their liquid counterparts. To convert such a sublimation pressure to a subcooled liquid vapour pressure, fusion properties (two out of these three quantities: fusion enthalpy, fusion entropy, fusion temperature) are required. Unfortunately, experimental knowledge of fusion properties is sometimes missing in part or completely, hence an estimation method is required. Several fusion data estimation methods are tested here against experimental data of functionalised acids, and a simple estimation method is developed, specifically for this family of compounds, with a significantly smaller estimation error than the literature methods.

Secondary organic aerosol (SOA) is a complex mixture of water and organic molecules. Its composition is determined by the presence of semi-volatile or non-volatile compounds, their saturation vapor pressure and activity coefficient. The activity coefficient is a non-ideality effect and is a complex function of SOA composition. In a previous publication, the detailed chemical mechanism (DCM) for α-pinene oxidation and subsequent aerosol formation BOREAM was presented. In this work, we investigate with this DCM the impact of non-ideality by simulating smog chamber experiments for α-pinene degradation and aerosol formation and taking the activity coefficient into account of all molecules in the aerosol phase. Several versions of the UNIFAC method are tested for this purpose, and missing parameters for e.g. hydroperoxides and nitrates are inferred from fittings to activity coefficient data generated using the SPARC model. Alternative approaches to deal with these missing parameters are also tested, as well as an activity coefficient calculation method based on Hansen solubility parameters (HSP). It turns out that for most experiments, non-ideality has only a limited impact on the interaction between the organic molecules, and therefore on SOA yields and composition, when water uptake is ignored. The reason is that often, the activity coefficient is on average close to 1 and, specifically for high-VOC experiments, partitioning is not very sensitive on the activity coefficient because the equilibrium is shifted strongly towards condensation. Still, for ozonolysis experiments with low amounts of volatile organic carbon (low-VOC), the UNIFAC parameterization of Raatikainen et al. leads to significantly higher SOA yields (by up to a factor 1.6) compared to the ideal case and to other parameterizations. Water uptake is model dependent, in the order: ideal > UNIFAC-Raatikainen > UNIFAC-Peng > UNIFAC-Hansen ≈ UNIFAC-Magnussen ≈ UNIFAC-Ming. In the absence of salt dissolution, phase splitting from pure SOA is unlikely.

We applied and compared seven vapor pressure estimation methods to the condensable compounds generated in the oxidation of α-pinene, as described by the state-of-the-art mechanism of the BOREAM model (Capouet et al., 2008). Several of these methods had to be extended in order to treat functional groups such as hydroperoxides and peroxy acyl nitrates. Large differences in the estimated vapor pressures are reported, which will inevitably lead to large differences in aerosol formation simulations. Cautioning remarks are given for some vapor pressure estimation methods.