Abstract. Tropospheric ozone, a key atmospheric pollutant and greenhouse gas, exhibits significant spatio-temporal variability on seasonal, inter-annual, and decadal scales, posing a challenge for satellite observation systems. Methods like the Convective Cloud Differential (CCD) and Cloud Slicing Algorithms (CSA) are standard for Tropospheric Column Ozone (TCO) retrievals but are limited to the tropical band (20° S–20° N). This study presents the first successful global application of CCD retrieval outside the tropical region. We introduce the CHORA-CCD (Cloud Height Ozone Reference Algorithm-CCD) for retrieving near-global TCO from TROPOMI. It utilises a local cloud reference sector (CLCD, CHORA Local Cloud Decision) to determine the stratospheric (above cloud) column ozone (ACCO). The ACCO is subtracted from the total column in clear-sky scenes to determine the TCO. The new approach presented here minimises the impact of stratospheric ozone variability, which is generally higher in the extratropics. An iterative approach is used to automatically select an optimal local cloud reference sector around each retrieval grid box, varying the radius from 60 to a maximum of 600 km, for which a mean TCO is determined until a sufficient number of ground pixels with nearly full cloud cover are found. Due to the prevalence of low-level clouds in mid-latitudes, the TCO calculation is constrained to the column from the surface up to the reference altitude of 450 hPa. There are two independent methods used: (I) CLCD-C, which uses an ozone climatology and (II) CLCD-T, an alternative method which estimates the ACCO at 450 hPa by linear regression (Theil-Sen) in cases where the cloud-top-heights in the local cloud sector vary sufficiently. The Theil-Sen approach is a combination of the CCD and CSA methods. The CLCD algorithm dynamically decides between the CLCD-C and CLCD-T to determine ACCO depending on the cloud characteristics. The CLCD algorithm is further refined by introducing a homogeneity criterion for total ozone to overcome inhomogeneities in stratospheric ozone. Monthly averaged CLCD TCOs have been determined over the tropics and mid-latitudes (60° S–60° N) using TROPOMI data from 2018 to 2022. The method’s accuracy was investigated by comparing spatially collocated SHADOZ/WOUDC/NDACC HEGIFTOM ozonesonde measurements from 36 stations. The validation results reveal that CLCD TCO retrievals are in good agreement with ozonesondes at most stations with an overall statistical bias of 0.6 DU and dispersion of 2.5 DU. Across all stations, the maximum bias and dispersion are around ∼5 DU and 4 DU, respectively. The CLCD approach effectively captures tropospheric ozone enhancements across diverse regions, including Northeast China and North America, with particular sensitivity to areas impacted by significant emission sources. Our results demonstrate the advantage of using the modified local cloud reference sector, providing an important basis for subsequent systematic applications in current and future missions, in particular, geostationary satellites with an emphasis on observing higher latitudes.

Tropospheric ozone, a critical pollutant and greenhouse gas, exhibits spatio-temporal variability, challenging satellite observations. Existing methods like the Convective Cloud Differential (CCD) and Cloud Slicing Algorithms (CSA) are standard for Tropospheric Column Ozone (TCO) retrieval but are limited to the tropics (20°S-20°N). Notably, the CCD approach has proven successful with satellite sensors like Aura OMI, MetOp GOME-2, and Sentinel-5 Precursor TROPOMI.In this study, we present the first successful application of CCD retrieval outside the tropical region. We introduce CHORA-CCD (Cloud Height Ozone Reference Algorithm-CCD) for retrieving TCO from TROPOMI in middle latitudes. It utilises a local cloud reference sector (CLCD, CHORA-CCD Local Cloud Decision) to determine the stratospheric (above cloud) column (ACCO) ozone. This ACCO is later subtracted from the total column in clear-sky scenes to determine the TCO. The new approach minimises the impact of variances in stratospheric ozone.An iterative approach is used to automatically select an optimal local cloud reference sector around each retrieval grid point, varying the radius from 60 to a maximum of 600 km around the grid box, for which a mean TCO is determined until a sufficient number of ground pixels with nearly fill cloud cover are found. Due to the prevalence of low-level clouds in middle latitudes, the estimation of TCO is constrained to the column up to a reference altitude of 450 hPa. An alternative method is introduced to directly estimate the ACCO down to 450 hPa by Theil-Sen regression in cases where the cloud-top heights in the local cloud sector are variable. The algorithm dynamically decides between CCD and Theil-Sen method for ACCO estimation by analysing the cloud characteristics. The CLCD algorithm is further refined by introducing a homogeneity criterion for total ozone to overcome inhomogeneities in stratospheric ozone.Monthly averaged CLCD-TCOs have been determined over the middle latitudes (60◦S-60◦N) from TROPOMI for the time period from 2018 to 2022. The accuracy of the method was investigated by comparisons with spatially collocated SHADOZ/WOUDC/NDACC ozonesondes from thirty-one stations. The validation results reveal that TCO retrievals at 450 hPa using the CLCD algorithm exhibit good agreement with ozonesondes at most stations. At the tropical station Natal (5.4°S, 35.4°W), there is an outstanding agreement between CLCD and ozonesondes, showcasing minimal bias and scatter (0.3 ± 1.0 DU). Similarly, in the subtropics over Irene (25.9°S, 28.2°E), CLCD exhibits a significantly lower bias and scatter (0.0 ± 1.4 DU). Specifically, at one of the northernmost stations, Legionowo (52.4°N, 21°E), bias and dispersion are minimal (0.4 ± 2.2 DU). Across all stations, the maximum observed bias and dispersion are below around 5 DU and 4 DU, respectively. In this presentation, a detailed validation of the new local CCD retrievals will be given, underlining the advantage of using the local cloud reference sector in the middle latitudes, providing an important basis for subsequent systematic applications in current and future missions of geostationary satellites.

The first Tropospheric Ozone Assessment Report (TOAR, 2014–2019) 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 the consistency of satellite measurements obtained with different techniques from multiple sensors, leading to differences in spatiotemporal sampling, vertical smoothing, a-priori information, and uncertainty characterisation. This motivated the Committee on Earth Observation Satellites (CEOS) to initiate a coordinated activity VC-20-01 on improving the assessment and harmonisation of tropospheric ozone measured from space. Here, we report on work that contributes to this CEOS activity, as well as to the ongoing second TOAR assessment (TOAR-II, 2020–2025). Our objective is to harmonise the spatiotemporal perspective of (sixteen) satellite ozone data records, thereby accounting as much as possible for differences in vertical smoothing and sampling. Four harmonisation methods are presented to achieve this goal: two for ozone profiles obtained from nadir sounders (UV-visible, IR, and combined UV-IR), and two for tropospheric ozone column products derived by one of the residual methods (Convective Cloud Differential or Limb–Nadir Matching). We discuss to what extent harmonisation may affect assessments of the spatial distribution, seasonal cycle, and long-term changes in free tropospheric ozone, and we anchor the harmonised profile data to ozonesonde measurements recently homogenised as part of TOAR-II. We find that approaches that use global ozone fields as a transfer standard (here the Copernicus Atmosphere Monitoring Service ReAnalysis, CAMSRA) to constrain the harmonisation generally lead to the largest reduction of the inter-product dispersion (IPD) between satellite datasets. These harmonisation efforts, however, only partially account for the observed discrepancies between the satellite datasets, with a reduction of about 10 %–40 % of the IPD upon harmonisation, depending on the products involved and with strong spatiotemporal dependences. This work therefore provides evidence that it is not only the differences in spatiotemporal smoothing and sampling, but rather the differences in measurement uncertainty that pose the main challenge to the assessment of the spatial distribution and temporal evolution of free tropospheric ozone from satellite observations.

We present the CHORA (Cloud Height Ozone Reference Algorithm) for retrieving tropospheric-ozone columns from S5P-TROPOMI (Sentinel-5 Precursor-TROPOspheric Monitoring Instrument). The method uses a local-cloud reference sector (CLC - CHORA Local Cloud) to determine the stratospheric (above-cloud) column, which is subtracted from the total column in clear-sky scenes in the same zonal band to retrieve the tropospheric column. The standard CCD (convective cloud differential) approach uses cloud data from the Pacific region (CPC - CHORA Pacific Cloud) instead. An important assumption for the standard method is the zonal invariance of stratospheric ozone. The local-cloud approach is the first step to diminish this constraint in order to extend the CCD method to mid-latitudes, where stratospheric-ozone variability is larger. An iterative approach has been developed for the automatic selection of an optimal local-cloud reference sector around each retrieval grid box varying latitudinally by +/- 1 degrees and longitudinally between +/- 5 and +/- 50 degrees. The optimised CLCT (CHORA Local Cloud Theil-Sen) algorithm, a follow-up from the CLC, employs a homogeneity criterion for total ozone from the cloud reference sector in order to overcome the inhomogeneities in stratospheric ozone. It directly estimates the above-cloud column ozone for a common reference altitude of 270 hPa using the Theil-Sen regression. The latter allows for the combination of the CCD method with the cloud-slicing algorithm that retrieves upper-tropospheric ozone volume mixing ratios. Monthly averaged tropospheric-column ozone (TCO) using the Pacific cloud reference sector (CPC) and the local-cloud reference sector (CLC, CLCT) has been determined over the tropics and subtropics (26 degrees S-22 degrees N) using TROPOMI for the time period from 2018 to 2022. The accuracy of the various methods was investigated by means of comparisons with spatially collocated NASA/GSFC SHADOZ (Southern Hemisphere Additional Ozonesondes) measurements and the ESA TROPOMI level-2 tropospheric-ozone product. At eight out of nine tropical stations, tropospheric-ozone columns using the CLCT yield better agreement with ozonesondes than the CPC. In the tropical region (20 degrees S-20 degrees N), the CLCT shows a significantly lower overall mean bias and dispersion of 1 +/- 7 %, outperforming both the CPC (12 +/- 10 %) and CCD-ESA (22 +/- 10 %). The CLCT surpasses the ESA operational product, providing more accurate tropospheric-ozone retrievals at eight out of nine stations in the tropics. For the Hilo station, with a larger stratospheric-ozone variability due to its proximity to the subtropics, the bias of +30 % (CPC) is effectively reduced to -5 % (CLCT). Similarly, in the subtropics (Reunion, Irene, Hanoi, and King's Park), the CLCT algorithm provides an overall bias and scatter of -11 +/- 9 % with respect to sondes. The CLCT effectively reduces the impact of stratospheric-ozone inhomogeneity, typically at higher latitudes. These results demonstrate the advantage of the local-cloud reference sector in the subtropics. The algorithm is therefore an important basis for subsequent systematic applications in current and future missions of geostationary satellites, like GEMS (Geostationary Environment Monitoring Spectrometer, Korea), ESA Sentinel-4, and NASA TEMPO (Tropospheric Emissions: Monitoring of POllution), predominantly covering the middle latitudes.

Tropospheric ozone is an important pollutant and greenhouse gas in the Earth’s atmosphere. Due to its short lifespan and dependence on sunlight and precursor emissions from natural and anthropogenic sources, tropospheric ozone exhibits a high spatio-temporal variability on seasonal, inter-annual and decadal time scales, which, in turn, poses a clear challenge to the satellite observing system. The Convective Cloud Differential (CCD) and Cloud Slicing Algorithms (CSA) are two standard tropospheric ozone retrieval methods limited to the tropical band (20◦S-20◦N). In particular, the CCD approach has been successfully applied to currently operating satellite sensors such as Aura OMI, MetOp GOME-2 and Sentinel-5 Precursor TROPOMI to derive tropical tropospheric column ozone (TTCO). In this study, we present the CHORA-CCD (Cloud Height Ozone Reference Algorithm-CCD) for retrieving TTCOs from TROPOMI. It uses a local cloud reference sector (CLC, CHORA-CCD Local Cloud) rather than the more common CCD approach using the Pacific region (CPC, CHORA-CCD Pacific Cloud) to determine the TTCO by subtracting the stratospheric (above cloud) column from the total column in clear-sky scenes in the same zonal band. An important assumption for this method is the zonal invariance of stratospheric ozone, which is only valid in the tropics. The local cloud approach is the first step to avoid this constraint and to extend the CCD method to middle latitudes, where stratospheric ozone variations are larger. An iterative approach has been developed for the automatic selection of an optimal local cloud reference sector around each retrieval grid box varying longitudinally from ±5◦ to a maximum of ±50◦. The CLC algorithm is further adapted and optimised in the CLCT algorithm by introducing a homogeneity criterion for total ozone to overcome the inhomogeneities in stratospheric ozone. An alternative method to directly estimate the above cloud column down to a reference altitude (270 hPa) is also introduced based on the Theil-Sen regression. The latter allows combining the CCD method with the CSA. Monthly averaged TTCOs using the Pacific cloud reference sector (CPC) and local cloud reference sector (CLC, CLCT) have been determined over the tropics and subtropics (26◦S-21◦N) from TROPOMI for the time period from 2018 to 2021. The accuracy of the various methods was investigated by comparisons with collocated NASA/GSFC SHADOZ ozonesonde retrievals. At eight out of twelve stations, TTCOs using CLC and CLCT yields better agreement with ozonesondes than CPC. In the tropics, the overall mean CLCT bias and dispersion of -6±8% is lower than the 11±12% of CPC. Similarly, in the subtropics, the CLCT algorithm significantly improves overall bias and scatter (-15±9%) compared to CPC (-25±19%). The overall statistical dispersion is effectively reduced to 2DU using CLCT from 5DU using CPC. In this presentation, a detailed validation of the new local CCD retrievals will be given. Our results demonstrate the advantage of using the local cloud reference sector in the subtropics, thereby providing an important basis for subsequent systematic applications in current and future missions of geostationary satellites, like ESA Sentinel 4, NASA Tempo, and GEMS covering only middle latitudes.

Satellite observations of relevant trace gases and meteorological data from ERA5 were used to describe the dynamics and chemistry of the spectacular Arctic 2019/20 winter/spring season. Exceptionally low total ozone values of slightly less than 220 DU were observed in mid‐March within an unusually large stratospheric polar vortex. Very high OClO and very low NO 2 column amounts observed by GOME‐2A were indicative of unusually large active chlorine levels and significant denitrification, which likely contributed to large chemical ozone loss. Using results from the TOMCAT chemical transport model (CTM) and ozone observations from S5P/TROPOMI, GOME‐2 (total column), SCIAMACHY, and OMPS‐LP (vertical profiles), chemical ozone loss was evaluated and compared with the previous record Arctic winter 2010/11. The polar‐vortex‐averaged total column ozone loss in 2019/20 reached 88 DU (23%) and 106 DU (28%) based upon observations and model, respectively, by the end of March, which was similar to that derived for 2010/11. The maximum ozone loss (∼80%) observed by OMPS‐LP was near the 450 K potential temperature level (∼18 km altitude). Because of the larger polar vortex area in March 2020 compared to March 2011 (about 25% at 450 K), ozone mass loss was larger in Arctic winter 2019/20. It is shown that Arctic cap temperatures were at a record 40‐year low in winter, suggesting that Arctic cold winters are getting colder. Tropical UTLS temperatures show an apparent upward trend during the last two decades that may indicate a weakening of the Brewer‐Dobson circulation and strengthening of the polar vortex.

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 spatiotemporal 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 TROPOspheric Monitoring Instrument (TROPOMI), on board the Sentinel 5 precursor (S5p) satellite, was launched in October 2017. The TROPOMI instrument has high spatial resolution and daily coverage of the Earth. About two years of level 2 data (versions up to 2.1.4) of OFFL GODFIT ozone and OCRA/ROCINN CRB (fraction and height) are available. Using these datasets, we derive tropical tropospheric ozone using the convective CCD cloud differential method for tropical tropospheric column ozone (TTCO) [DU] and the CSL cloud slicing method for upper tropospheric ozone volume mixing ratios (TUTO) [ppbv]. The CCD algorithm was optimized for TROPOMI with respect to the reference sector Above Cloud Column Ozone field (ACCO) by adjusting it in time and latitude space in order to reduce data gaps in the daily ACCO vectors. Daily total ozone gridded data with a latitude/longitude resolution of 0.5°/1° are used to minimize the error from stratospheric ozone changes. The CSL algorithm (CHOVA: Cloud Height induced Ozone Variation Analysis) was developed to fully exploit the S5p instruments characteristics. The data is spatially sampled to a 2° latitude/longitude grid. A temporal sampling of cloud/ozone data is not necessary anymore due to the high amount of S5p measurements. Comparisons with NASA/GSFC SHADOZ ozone sondes show good agreement (low bias and high dispersion) for both methods taking into account the principal differences between sonde point measurements and satellite sampled mean value. The CHOVA results from the pacific sector are now used as input for the CCD method to adjust the height dependent columns to a fixed pressure level. The work on TROPOMI/S5P geophysical products is funded by ESA and national contributions from the Netherlands, Germany, Belgium, and Finland.

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 assesses the performances of the TROPOMI formaldehyde (HCHO) operational product compared to its predecessor, the OMI (Ozone Monitoring Instrument) HCHO QA4ECV product, at different spatial and temporal scales. The parallel development of the two algorithms favoured 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 % (< 0.4 × 1015 molec. cm−2). For lower columns, OMI observations present a remaining positive bias of about 20 % (< 0.8 × 1015 molec. cm−2) compared to TROPOMI in midlatitude regions. Here, we also use a global network of 18 MAX-DOAS (multi-axis differential optical absorption spectroscopy) instruments to validate both satellite sensors for a large range of HCHO columns. This work complements the study by Vigouroux et al. (2020), where a global FTIR (Fourier transform infrared) network is used to validate the TROPOMI HCHO operational product. Consistent with the FTIR validation study, we find that for elevated HCHO columns, TROPOMI data are systematically low (−25 % for HCHO columns larger than 8 × 1015 molec. cm−2), while no significant bias is found for medium-range column values. We further show that OMI and TROPOMI data present equivalent biases for large HCHO levels. However, TROPOMI significantly improves the precision of the HCHO observations at short temporal scales and for low HCHO columns. We show that compared to OMI, the precision of the TROPOMI HCHO columns is improved by 25 % for individual pixels and by up to a factor of 3 when considering daily averages in 20 km radius circles. The validation precision obtained with daily TROPOMI observations is comparable to the one obtained with monthly OMI observations. To illustrate the improved performances of TROPOMI in capturing weak HCHO signals, we present clear detection of HCHO column enhancements related to shipping emissions in the Indian Ocean. This is achieved by averaging data over a much shorter period (3 months) than required with previous sensors (5 years) and opens new perspectives to study shipping emissions of VOCs (volatile organic compounds) and related atmospheric chemical interactions.

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 TROPOspheric Monitoring Instrument (TROPOMI), on board the Sentinel 5 precursor (S5p) satellite, was launched in October 2017. The TROPOMI instrument has high spatial resolution and daily coverage of the Earth. About two years of level 2 data (version 1.1.5/1.1.7) of ozone and cloud properties (fraction and height) are available. Using the OFFL GODFIT ozone and OCRA/ROCINN CRB cloud dataset, we derived tropical tropospheric ozone using the convective cloud differential method for tropical tropospheric column ozone (TTCO) [DU] and the cloud slicing method for upper tropospheric ozone volume mixing ratios (TUTO) [ppbv]. The CCD algorithm was optimized for TROPOMI with respect to the reference sector Above Cloud Column Ozone field (ACCO). It was adjusted in time and latitude space in order to reduce data gaps in the daily ACCO vectors. Also, daily total ozone maps were used to minimize the error in stratospheric ozone differences. The CHOVA algorithm (Cloud Height induced Ozone Variation Analysis) was developed to fully exploit with the S5p instruments characteristics. A temporal sampling of cloud/ozone data is not necessary for the high amount of S5p measurements. The spatial sampling is 2° latitude/longitude grid boxes. CHOVA results are quality checked based on the statistical properties of cloud, ozone and retrieval parameters to exclude unreliable TUTO values. Comparisons with ozone sondes show a good agreement for both methods taking into account the principal differences between a sonde point measurement and a satellite sampled mean value. The work on TROPOMI/S5P geophysical products is funded by ESA and national contributions from the Netherlands, Germany, Belgium, and Finland.

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.

Tropospheric ozone is a pollutant that damages ecosystems and triggers human health problems. Ozone concentrations are highly variable over time and across the troposphere, which poses clear challenges to deepen our understanding of the processes involved in the production and transport of ozone. Further progress depends on the availability of instruments capable of measuring tropospheric ozone and its distribution at finer spatio-temporal scales. The TROPOMI instrument on the Sentinel-5p platform, launched into an early afternoon polar orbit in October 2017, combines a high spatial resolution, a large swath width and the spectral measurement characteristics required to deliver tropospheric ozone data records at unprecedented detail. The first of these products was released during Fall 2018. It consists in 0.5° (latitude) by 1° (longitude) resolved daily maps of 3-day moving mean values of the tropospheric ozone column between 20°S and 20°N, and it is computed using the convective-cloud method (CCD). A second data product consists in maps of tropical upper tropospheric ozone mixing ratio at a coarser spatial and temporal resolution. It is based on a cloud slicing algorithm (CSA), which is currently being fine-tuned for release in the very near future. A third data product, also under development at the time of this abstract, consists of the vertical profile of ozone concentration in the global troposphere and stratosphere, retrieved with the classical Optimal Estimation (OE) technique. We present here an assessment of the quality of the first year of the different tropospheric ozone column data sets retrieved from Sentinel-5p TROPOMI measurements, carried out within the context of ESA’s Sentinel-5p Mission Performance Center (MPC) and the S5PVT AO project CHEOPS-5p (Validation of Copernicus Height-resolved Ozone data Products from Sentinel-5p TROPOMI using global sonde and lidar networks, #28587). The first stage of this analysis consists of an inspection of the tropospheric ozone fields for structures which are potentially introduced in the measurement process. Sampling effects, in particular, are a possible source of uncertainty as the CCD product is derived from binned TROPOMI total ozone column data. Another structure introduced by retrieval assumptions would be the dependence of the quality of Sentinel-5p retrieved total column data to cloud parameters. In a second stage the satellite data are confronted to quality-assured ozonesonde and –tentatively– ground-based lidar measurements from the NDACC, SHADOZ and TOLNET networks. These well-characterized observational data records serve as a reference to evaluate the bias and uncertainty of the Sentinel-5p data, and their dependence on influence quantities. The study concludes with an assessment of the compliance of Sentinel-5p tropospheric ozone data with respect to mission and user requirements for key data applications.

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric ChartographY) aboard ESA's environmental satellite ENVISAT observed the Earth's atmosphere in limb, nadir, and solar/lunar occultation geometries covering the UV-Visible to NIR spectral range. It is a joint project of Germany, the Netherlands and Belgium and was launched in February 2002. SCIAMACHY doubled its originally planed in-orbit lifetime of five years before the communication to ENVISAT was severed in April 2012, and the mission entered its post-operational phase. In order to preserve the best quality of the outstanding data recorded obtained by SCIAMACHY, the data processors were updated in phase F and the whole mission was reprocessed. In addition to the usual updates, the following items were added to the processor 1. Tropospheric BrO, a new retrieval based on the scientific algorithm of (Theys et al., 2011). This algorithm had been originally developed for the GOME-2 sensor and later adapted for SCIAMACHY. 2. Improved cloud flagging using limb measurements. Limb cloud flags are already part of the SCIAMACHY L2 product. They are currently calculated based on the scientific algorithm by (Eichmann et al., 2015). Clouds are categorized into four types: water, ice, polar stratospheric and noctilucent clouds. 3. A new, future-proof file format for the level 2 product based on NetCDF. We will present results from the verification and the mission re-processing.