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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.

We introduce the new Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 product of total column ozone (O3), total and tropospheric column nitrogen dioxide (NO2), total column water vapour, total column bromine oxide (BrO), total column formaldehyde (HCHO), and total column sulfur dioxide (SO2) (daily products https://doi.org/10.15770/EUM_SAF_AC_0048, AC SAF, 2023a; monthly products https://doi.org/10.15770/EUM_SAF_AC_0049, AC SAF, 2023b). The GOME-2 level-3 products aim to provide easily translatable and user-friendly data sets to the scientific community for scientific progress as well as to satisfy public interest. The purpose of this paper is to present the theoretical basis as well as the verification and validation of the GOME-2 daily and monthly level-3 products. The GOME-2 level-3 products are produced using the overlapping area-weighting method. Details of the gridding algorithm are presented. The spatial resolution of the GOME-2 level-3 products is selected based on the sensitivity study. The consistency of the resulting level-3 products among three GOME-2 sensors is investigated through time series of global averages, zonal averages, and bias. The accuracy of the products is validated by comparison to ground-based observations. The verification and validation results show that the GOME-2 level-3 products are consistent with the level-2 data. Small discrepancies are found among three GOME-2 sensors, which are mainly caused by the differences in the instrument characteristic and level-2 processor. The comparison of GOME-2 level-3 products to ground-based observations in general shows very good agreement, indicating that the products are consistent and fulfil the requirements to serve the scientific community and general public.

Clouds have a strong impact on satellite measurements of tropospheric trace gases in the ultraviolet, visible, and near-infrared spectral ranges from space. Therefore, trace gas retrievals rely on information on cloud fraction, cloud albedo, and cloud height from cloud products. In this study, the cloud parameters from different cloud retrieval algorithms for the Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) are compared: the Optical Cloud Recognition Algorithm (OCRA) a priori cloud fraction, the Retrieval Of Cloud Information using Neural Networks (ROCINN) CAL (Clouds-As-Layers) cloud fraction and cloud top and base height, the ROCINN CRB (Clouds-as-Reflecting-Boundaries) cloud fraction and cloud height, the Fast Retrieval Scheme for Clouds from the Oxygen A-band (FRESCO) cloud fraction, the interpolated FRESCO cloud height from the TROPOMI NO2 product, the cloud fraction from the NO2 fitting window, the O2–O2 cloud fraction and cloud height, the Mainz Iterative Cloud Retrieval Utilities (MICRU) cloud fraction, and the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud fraction. Two different versions of the TROPOMI cloud products OCRA/ROCINN, FRESCO, and the TROPOMI NO2 product are included in the comparisons (processor version 1.x and 2.x). Overall, the cloud parameters retrieved by the different algorithms show qualitative consistency in version 1.x and good agreement in version 2.x with the exception of the VIIRS cloud fraction, which cannot be directly compared to the other data. Differences between the cloud retrievals are found especially for small cloud heights with a cloud fraction threshold of 0.2, i.e. clouds that are particularly relevant for tropospheric trace gas retrievals. The cloud fractions of the different version 2 cloud products primarily differ over snow- and ice-covered pixels and scenes with sun glint, for which only MICRU includes an explicit treatment. All cloud parameters show some systematic problems related to the across-track dependence, where larger values are found at the edges of the satellite view. The consistency between the cloud parameters from different algorithms depends strongly on how the data are filtered for the comparison, for example, what quality value is used or whether snow- and ice-covered pixels are excluded from the analysis. In summary, clear differences were found between the results of various algorithms, but these differences are reduced in the most recent versions of the cloud data.

We introduce the new GOME-2 daily and monthly level 3 product of total column ozone (O3), total and tropospheric column nitrogen dioxide (NO2), total column water vapour, total column bromine oxide (BrO), total column formaldehyde (HCHO) and total column sulphur dioxide (SO2). The GOME-2 level 3 products are aimed to provide easily translatable and user-friendly data sets to the scientific community for scientific progress as well as satisfying public interest. The purpose of this paper is to present the theoretical basis as well as the verification and validation of the GOME-2 daily and monthly level 3 products. The GOME-2 level 3 products are produced using the overlapping area weighting method. Details of the gridding algorithm are presented. The spatial resolution of the GOME-2 level 3 products is selected based on sensitivity study. The consistency of the resulting level 3 products among three GOME-2 sensors is investigated through time series of global averages, zonal averages, and bias. The accuracy of the products is validated by comparing to ground-based observations. The verification and validation results show that the GOME-2 level 3 products are consistent with the level 2 data. Small discrepancies are found among three GOME-2 sensors, which are mainly caused by the differences in instrument characteristic and level 2 processor. The comparison of GOME-2 level 3 products to ground-based observations in general shows very good agreement, indicating the products are consistent and fulfil the requirements to serve the scientific community and general public.

Since its first confirmed case in December 2019, coronavirus disease 2019 (COVID-19) has become a worldwide pandemic with more than 90 million confirmed cases by January 2021. Countries around the world have enforced lockdown measures to prevent the spread of the virus, introducing a temporal change of air pollutants such as nitrogen dioxide (NO2) that are strongly related to transportation, industry, and energy. In this study, NO2 variations over regions with strong responses to COVID-19 are analysed using datasets from the Global Ozone Monitoring Experiment-2 (GOME-2) sensor aboard the EUMETSAT Metop satellites and TROPOspheric Monitoring Instrument (TROPOMI) aboard the EU/ESA Sentinel-5 Precursor satellite. The global GOME-2 and TROPOMI NO2 datasets are generated at the German Aerospace Center (DLR) using harmonized retrieval algorithms; potential influences of the long-term trend and seasonal cycle, as well as the short-term meteorological variation, are taken into account statistically. We present the application of the GOME-2 data to analyze the lockdown-related NO2 variations for morning conditions. Consistent NO2 variations are observed for the GOME-2 measurements and the early afternoon TROPOMI data: regions with strong social responses to COVID-19 in Asia, Europe, North America, and South America show strong NO2 reductions of $\sim $ ∼ 30–50% on average due to restriction of social and economic activities, followed by a gradual rebound with lifted restriction measures.

Multi-axis differential optical absorption spectroscopy (MAX-DOAS) and direct sun NO2 vertical column network data are used to investigate the accuracy of tropospheric NO2 column measurements of the GOME-2 instrument on the MetOp-A satellite platform and the OMI instrument on Aura. The study is based on 23 MAX-DOAS and 16 direct sun instruments at stations distributed worldwide. A method to quantify and correct for horizontal dilution effects in heterogeneous NO2 field conditions is proposed. After systematic application of this correction to urban sites, satellite measurements are found to present smaller biases compared to ground-based reference data in almost all cases. We investigate the seasonal dependence of the validation results as well as the impact of using different approaches to select satellite ground pixels in coincidence with ground-based data. In optimal comparison conditions (satellite pixels containing the station) the median bias between satellite tropospheric NO2 column measurements and the ensemble of MAX-DOAS and direct sun measurements is found to be significant and equal to −34 % for GOME-2A and −24 % for OMI. These biases are further reduced to −24 % and −18 % respectively, after application of the dilution correction. Comparisons with the QA4ECV satellite product for both GOME-2A and OMI are also performed, showing less scatter but also a slightly larger median tropospheric NO2 column bias with respect to the ensemble of MAX-DOAS and direct sun measurements.

This paper reports on ground-based validation of the atmospheric OClO data record produced in the framework of EUMETSAT’s Satellite Application Facility on Atmospheric Chemistry Monitoring (AC SAF) using the GOME2-A and -B instruments over the 2007–2016 and 2013–2016 periods, respectively. OClO slant column densities are compared to correlative measurements collected from 9 NDACC Zenith-Scattered-Light DOAS (ZSL-DOAS) instruments distributed in both the Arctic and Antarctic. Sensitivity tests are performed on the ground-based data to estimate the impact of the different OClO DOAS analysis settings. On this basis, we infer systematic uncertainties of about 25 % between the different ground-based data analysis, reaching total uncertainties ranging from about 26 % to 33 % for the different stations. Time-series at the different sites show good agreement between satellite and ground-based data, both for the inter-annual variability and the overall OClO seasonal behavior. GOME-2A results are found to be nosier than those of GOME-2B, especially after 2011, probably due to instrumental degradation effects. Daily linear regression analysis for OClO activated periods yield correlation coefficients of 0.8 for GOME-2A and 0.87 for GOME-2B, with slopes of 0.64 and 0.72, respectively. Biases are within 8 x 1013 molec/cm2 with some differences between GOME-2A and GOME-2B, depending on the station. Overall, considering all the stations, a median bias of about −2.2 x 1013 molec/cm2 is found for both GOME-2 instruments.

Lightning is one of the major sources of nitrogen oxides (NOx) in the atmosphere, contributing to the tropospheric concentration of ozone and to the oxidising capacity of the atmosphere. Lightning produces between 2–8 Tg N per year globally and on average about 250 ± 150 mol NOx per flash. In this work, we estimate the moles of NOx produced per flash (LNOx production efficiency) in the Pyrenees (Spain, France and Andorra) and in the Ebro Valley (Spain) by using nitrogen dioxide (NO2) and cloud properties from the TROPOspheric Monitoring Instrument (TROPOMI) and lightning data from the Earth Networks Global Lightning Network (ENGLN) and from the EUropean Co-operation for LIghtning Detection (EUCLID). The Pyrenees is one of the areas in Europe with the highest lightning frequency and, due to its remoteness as well as experiencing very low NOx background, enables us to better distinguish the LNOx signal produced by recent lightning in TROPOMI NO2 measurements. We compare the LNOx production efficiency estimates for 8 convective systems in 2018 using two different sets of TROPOMI research products, provided by the Royal Netherlands Meteorological Institute (KNMI) and the Deutsches Zentrum für Luft- und Raumfahrt (DLR), respectively. According to our results, the mean LNOx production efficiency in the Pyrenees and in the Ebro Valley, using a three-hour chemical lifetime, ranges between 14 and 103 mol NOx per flash from the 8 systems. The mean LNOx production efficiency estimates obtained using both TROPOMI products and ENGLN lightning data differ by ∼23 %, while it differs by ∼35 % when using EUCLID lightning data. The main sources of uncertainty when using ENGLN lightning data are the estimation of background NOx that is not produced by lightning and the time window before the TROPOMI overpass that is used to count the total number of lightning flashes contributing to fresh-produced LNOx. The main source of uncertainty when using EUCLID lightning data is the uncertainty in the detection efficiency of EUCLID.

In October 2017, the Sentinel-5 Precursor (S5P) mission was launched, carrying the TROPOspheric Monitoring Instrument (TROPOMI), which provides a daily global coverage at a spatial resolution as high as 7 km × 3.5 km and is expected to extend the European atmospheric composition record initiated with GOME/ERS-2 in 1995, enhancing our scientific knowledge of atmospheric processes with its unprecedented spatial resolution. Due to the ongoing need to understand and monitor the recovery of the ozone layer, as well as the evolution of tropospheric pollution, total ozone remains one of the leading species of interest during this mission. In this work, the TROPOMI near real time (NRTI) and offline (OFFL) total ozone column (TOC) products are presented and compared to daily ground-based quality-assured Brewer and Dobson TOC measurements deposited in the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Additional comparisons to individual Brewer measurements from the Canadian Brewer Network and the European Brewer Network (Eubrewnet) are performed. Furthermore, twilight zenith-sky measurements obtained with ZSL-DOAS (Zenith Scattered Light Differential Optical Absorption Spectroscopy) instruments, which form part of the SAOZ network (Système d'Analyse par Observation Zénitale), are used for the validation. The quality of the TROPOMI TOC data is evaluated in terms of the influence of location, solar zenith angle, viewing angle, season, effective temperature, surface albedo and clouds. For this purpose, globally distributed ground-based measurements have been utilized as the background truth. The overall statistical analysis of the global comparison shows that the mean bias and the mean standard deviation of the percentage difference between TROPOMI and ground-based TOC is within 0 –1.5 % and 2.5 %–4.5 %, respectively. The mean bias that results from the comparisons is well within the S5P product requirements, while the mean standard deviation is very close to those limits, especially considering that the statistics shown here originate both from the satellite and the ground-based measurements. Additionally, the TROPOMI OFFL and NRTI products are evaluated against already known spaceborne sensors, namely, the Ozone Mapping Profiler Suite, on board the Suomi National Polar-orbiting Partnership (OMPS/Suomi-NPP), NASA v2 TOCs, and the Global Ozone Monitoring Experiment 2 (GOME-2), on board the Metop-A (GOME-2/Metop-A) and Metop-B (GOME-2/Metop-B) satellites. This analysis shows a very good agreement for both TROPOMI products with well-established instruments, with the absolute differences in mean bias and mean standard deviation being below +0.7 % and 1 %, respectively. These results assure the scientific community of the good quality of the TROPOMI TOC products during its first year of operation and enhance the already prevalent expectation that TROPOMI/S5P will play a very significant role in the continuity of ozone monitoring from space.

Chlorine dioxide is an indicator for chlorine activation in the stratosphere, of importance for understanding spring-time ozone depletion processes in the polar regions of both hemispheres. Within the EUMETSAT AC SAF working group, chlorine dioxide (OClO) was retrieved from the GOME-2 instruments on MetOp-A and MetOp-B platforms, respectively over the time periods 2007-2016 and 2012-2016. Moreover, recent work performed as part of the S5p+ Innovation programme has led to the creation of an additional dataset derived from the TROPOMI instrument, extending the OClO time series in 2018-2020.This study analyses the quality of both OClO slant column (SCD) datasets by comparing them to ground-based DOAS zenith-sky measurements at a selection of 8 stations in Arctic and Antarctic regions: Eureka (80°N), Ny Alesund (79°N), Kiruna (68°N), Harestua (60°N), Marambio (64°S), Belgrano (78°S), Neumayer (71°S) and Arrival Heights (78°S). To allow for comparison with satellite data, ground-based OClO spectral analyses are performed using yearly fixed reference spectra recorded at low SZA in the absence of chlorine activation. Furthermore, an additional bias-correction is applied in post-processing to generate a consistent long-term OClO data record covering the 2007-2020 period.Daily comparisons of satellite and ground-based SCD data pairs corresponding to similar SZA conditions are performed, assuming similar stratospheric light paths in satellite nadir and ground-based zenith-sky geometries. Daily mean OClO SCD time-series show that satellite and ground-based observations agree well at all stations in terms of short-term variability and seasonal variation. Linear regression plots show a correlation coefficient R of about 0.97, a slope of 0.9 and an intercept of less than 1x1013 molec/cm² for TROPOMI, while for GOME-2 results are more noisy and tend to be biased low, with correlation coefficients between 0.76 and 0.88, slopes between 0.65 and 0.74 and intercepts up to 2.4 x1013 molec/cm².

The ESA S5p+Innovation programme aims at supporting the development of new TROPOMI scientific products. As part of this activity, a glyoxal tropospheric column algorithm, relying on heritage from SCIAMACHY, GOME-2 and OMI, has been adapted to TROPOMI and further developed. This product provides information on volatile organic compounds (VOC) emissions as glyoxal is mainly released in the atmosphere as an intermediate product of VOC oxidation, but also directly emitted from biomass burning events.We present here the BIRA-IASB S5p glyoxal product, which relies on a DOAS approach: spectral fits in the 435-460 nm window provide glyoxal slant columns, which are then converted into tropospheric columns by means of air mass factors and application of a background correction. In particular, the algorithm has been improved to mitigate the impact of scene brightness inhomogeneity and of non-linearity in case of strong NO2 absorption. The retrieved columns are provided along with total error estimates resulting from the propagation of uncertainties at every step in the algorithm chain.We also highlight the excellent consistency between the retrievals from TROPOMI and those from OMI and GOME-2A/B obtained with a similar algorithm. In addition, the good quality of the product is demonstrated with comparisons with MAX-DOAS glyoxal observations at a few stations in Asia and Europe.

In this paper, we present the estimation of surface NO2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO2 concentrations over Germany from 2018 to 2020. Estimated surface NO2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO2 in Germany. The estimated surface NO2 concentrations provide comprehensive information of NO2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 μμg/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 μμg/m3. In addition, we used the surface NO2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO2 levels in Germany. In general, 10–30% lower surface NO2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus.

The first European Sentinel satellite for monitoring the composition of the Earth’s atmosphere, the Sentinel 5 Precursor (S5p), carries the TROPOspheric Monitoring Instrument (TROPOMI) to map trace species of the global atmosphere at high spatial resolution. Retrievals of tropospheric trace gas columns from satellite measurements are strongly influenced by clouds. Thus, cloud retrieval algorithms were developed and implemented in the trace gas processing chain to consider this impact.In this study, different cloud products available for NO2 retrievals based on the TROPOMI level 1b data version 1 and an updated TROPOMI level 1b test data set of version 2 (Diagnostic Data Set 2B, DDS2B) are analyzed. The data sets include a) the TROPOMI level 2 OCRA/ROCINN (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks) cloud products CRB (cloud as reflecting boundaries) and CAL (clouds as layers), b) the FRESCO (Fast Retrieval Scheme for Clouds from Oxygen absorption bands) cloud product, c) the cloud fraction from the NO2 fitting window, d) the VIIRS (Visible Infrared Imaging Radiometer Suite) cloud product, and e) the MICRU (Mainz Iterative Cloud Retrieval Utilities) cloud fraction. The cloud products are compared with regard to cloud fraction, cloud height, cloud albedo/optical thickness, flagging and quality indicators in all 4 seasons. In particular, the differences of the cloud products under difficult situations such as snow or ice cover and sun glint are investigated.We present results of a statistical analysis on a limited data set comparing cloud products from the current and the upcoming lv2 data versions and their approaches. The aim of this study is to better understand TROPOMI cloud products and their quantitative impacts on trace gas retrievals.

We present the total column water vapor (TCWV) retrieval for the TROPOspheric Monitoring Instrument (TROPOMI) observations in the blue band. The retrieval was first developed to retrieve TCWV from Global Ozone Monitoring Experience 2 (GOME-2). We have modified the settings of the retrieval to adapt it for TROPOMI observations. The TROPOMI TCWV retrieval algorithm consists of two major steps. The first step is the retrieval of water vapor slant columns by applying the differential optical absorption spectroscopy (DOAS) technique to TROPOMI observations in the blue band. The retrieved water vapor slant columns are then converted to vertical columns using air mass factors (AMFs). An iterative optimization has been developed to dynamically find the optimal a priori water vapor profile for AMF calculation. The dynamic search algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column amount. This makes the algorithm better suited for climate studies compared to typical satellite retrievals with static a priori or vertical profile information from the chemistry transport model (CTM). Details of the TCWV retrieval are presented. The TCWV retrieval algorithm is applied to TROPOMI observations. The results are validated by comparing to Ozone Monitoring Instrument (OMI), GOME-2 and Special Sensor Microwave Imager Sounder (SSMIS) satellite observations. TCWV derived from TROPOMI observations agree well with the other data sets with Pearson correlation coefficient (R) ranging from 0.94 to 0.99. The correlation is slight better during winter time of the northern hemisphere. Small discrepancies are found among TROPOMI, OMI, GOME-2 and SSMIS observations. The discrepancies are mainly due to differences in measurement time and cloud filtering. More detailed validation against ground based sun-photometer observations are presented separately in this session*. *see the respective abstract by Katerina Garane.

Launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor provides the potential to monitor air quality over point sources across the globe with a spatial resolution as high as 5.5 km × 3.5 km (7 km × 3.5 km before 6 August 2019). The nitrogen dioxide (NO2) retrieval algorithm for the TROPOMI instrument consists of three steps: the spectral fitting of the slant column, the separation of stratospheric and tropospheric contributions, and the conversion of the slant column to a vertical column using an air mass factor (AMF) calculation. In this work, an improved tropospheric NO2 retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented using the STRatospheric Estimation Algorithm from Mainz (STREAM), which was developed as a verification algorithm for TROPOMI and does not require chemistry transport model data as input. A directionally dependent STREAM (DSTREAM) is developed to correct for the dependency of the stratospheric NO2 on the viewing geometry by up to 2 × 1014 molec/cm2. Applied to synthetic TROPOMI data, the uncertainty in the stratospheric column is 3.5 × 1014 molec/cm2 for polluted conditions. Applied to actual measurements, the smooth variation of stratospheric NO2 at low latitudes is conserved, and stronger stratospheric variation at higher latitudes are captured. For AMF calculation, the climatological surface albedo data is replaced by geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) obtained directly from TROPOMI measurements with a high spatial resolution. Mesoscale-resolution a priori NO2 profiles are obtained from the regional POLYPHEMUS/DLR chemistry transport model with the TNO-MACC emission inventory. Based on the latest TROPOMI operational cloud parameters, a more realistic cloud treatment is provided by a clouds-as-layers (CAL) model, which treats the clouds as uniform layers of water droplets, instead of the clouds-as-reflecting-boundaries (CRB) model, in which clouds are simplified as Lambertian reflectors. For the error analysis, the tropospheric AMF uncertainty, which is the largest source of NO2 uncertainty for polluted scenarios, ranges between 20 % and 50 %, leading to a total uncertainty in the tropospheric NO2 column in the 30–60 % range. From a validation performed with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements, the improved tropospheric NO2 data shows good correlations for nine European urban/suburban stations with an average correlation coefficient of 0.78. The implementation of the algorithm improvements leads to a decrease of the relative difference from −55.3 % to −34.7 % on average.

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

The first European Sentinel satellite for monitoring the composition of the Earth’s atmosphere, the Sentinel 5 Precursor (S5p), carries the TROPOspheric Monitoring Instrument (TROPOMI) on board to map trace species of the global atmosphere at high spatial resolution. Retrievals of tropospheric trace gas columns from satellite measurements are strongly influenced by clouds. Thus, cloud retrieval algorithms were developed and implemented in the trace gas processing chain to consider this impact.In this study, different cloud products available for NO2 retrievals from TROPOMI data are analyzed. The TROPOMI level 2 OCRA/ROCINN (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks) cloud products CRB (cloud as reflecting boundaries) and CAL (clouds as layers) as well as the FRESCO (Fast Retrieval Scheme for Clouds from Oxygen absorption bands) cloud product are compared with regard to e. g. cloud fraction, cloud height, cloud albedo/optical thickness, flagging and quality indicators. In particular, difficult situations such as snow or ice, sun glint, and high aerosol load are investigated.The eventual aim of this study is to better understand TROPOMI cloud products and their quantitative impacts on trace gas retrievals. Here, we present first results of a statistical analysis on a limited data set comparing currently existing cloud products and their approaches focusing on NO2.

We present a new total column water vapor (TCWV) retrieval algorithm in the visible blue spectral band for the Global Ozone Monitoring Experience 2 (GOME-2) instruments on board the EUMETSAT MetOp satellites. The blue band algorithm allows retrieval of water vapor from sensors which do not cover longer wavelengths, such as Ozone Monitoring Instrument (OMI) and the Copernicus atmospheric composition missions Sentinel-5 Precursor (S5P), Sentinel-4 (S4) and Sentinel-5 (S5). The blue band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical column using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from chemistry transport model (CTM). The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. The new algorithm is applied to GOME-2A and GOME-2B observations to retrieve TCWV. The data set is validated by comparing to the operational product retrieved in the red spectral band, sun-photometer and radiosonde measurements. Water vapor columns retrieved in the blue band are in good agreement with the other data sets, indicating that the new algorithm derives precise results, and can be used for the current and forthcoming Copernicus Sentinel missions S4 and S5.

We present a new total column water vapor (TCWV) retrieval algorithm in the visible blue band for the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5P) satellite. Retrieving water vapor columns in the blue band has numerous advantages over longer wavelengths. Measurements in the blue band are more sensitive at lower troposphere over oceans due to higher surface albedo at this wavelength band. In addition, no correction for spectral saturation effects is required as water vapor is optically thin in this spectral band. The blue band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical column using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from chemistry transport model (CTM).The new algorithm is applied to TROPOMI observations to retrieve TCWV. Due to the long measurement record of GOME-2, the new algorithm is also used to retrieve TCWV from GOME-2. The TCWV data set is validated by comparing to the GOME-2 TCWV operational product retrieved in the red spectral band, MODIS and SSMIS satellite observations. In addition, the new TCWV data set is also compared to ground based sun-photometer and radiosonde measurements. Water vapor columns retrieved in the blue band are in good agreement with the other data sets, indicating that the new algorithm derives precise results. Therefore, it was selected for the S5P Processor Algorithm Laboratory (PAL) project as a future operational product. This algorithm can also be used for the forthcoming Copernicus Sentinel S4 and S5 missions.

14 pags., 12 figs., 1 tab., High resolution satellite maps are useful for the identification of pollution hotspots. In this work, nitrogen dioxide (NO) and sulfur dioxide (SO) observations from the Ozone Monitoring Instrument (OMI) have been averaged and gridded for Shanghai and surrounding areas with a spatial resolution of 0.01° × 0.01°. The pollution maps have been used to investigate the spatio-temporal variations in NO and SO from 2008 to 2017. The averaged tropospheric NO and planetary boundary layer (PBL) SO columns in Shanghai are on average 1.40 × 10 molec·cm and 1.21 × 10 molec·cm, respectively. As the government has put a lot of effort to improve the air quality, both the NO and SO columns reveal a significant decline in Shanghai during the past decade, with a reduction of ∼24% and ∼56%, respectively, which is also confirmed by the good agreement between satellite observations and ground-based in-situ measurements. The NO and SO time series also show distinct seasonal characteristics with higher pollution level during winter and lower in the summer. Due to the impact of ship and industrial emissions, the hotspots of NO and SO are concentrated in the northern part of Shanghai, while the pollution level is lower in the southern and eastern regions. Decreasing trends in NO and SO are found for most areas of Shanghai during the past decade, though the strongest decrease in the northern part of the city (1.4 × 10 molec·cm·year for NO and 3.0 × 10 molec·cm·year for SO). Comparisons with the China Multi-Resolution Emissions Inventory (MEIC) indicate that the driving factors for the decline of NO and SO are mainly denitration and desulfurization in the power sector, and the reduction of emissions in the industry sector. We also use aerosol optical depth (AOD) measurements from MODerate-resolution Imaging Spectroradiometer (MODIS) to investigate the relationship between gas phase pollutants and formation of secondary aerosol. Compared to SO, the contribution of NO to secondary aerosol formation increased in the central part of Shanghai. Although Chongming Eco-Island has developed rapidly in recent years, both the SO and NO levels decreased significantly. This is mainly related to the strict emission control policy and improved urban planning, and indicates that ecological development is achievable under the guidance of policies., This research was supported by grants from National Key Research and Development Program of China (2016YFC0200401,2017YFC0210002), National Natural Science Foundation of China(41775113, 21777026), Shanghai Pujiang Talent Program(17PJC015). This work was also supported by The Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning and Shanghai Thousand Talents Program.

Onboard the Chinese GaoFen-5 (GF5) satellite, the Environmental trace gases Monitoring Instrument (EMI) is a nadir-viewing wide-field spectrometer that was launched on May 9, 2018. EMI measures the back-scattered earthshine solar radiance in the ultraviolet and visible spectral range. By using the differential optical absorption spectrometry (DOAS) method and the EMI measurements in the VIS1 band (405–465 nm), we performed retrievals of NO2. Some first retrieval results of NO2 from EMI and a comparison with OMI and TROPOMI products are presented in this paper. The monthly mean total vertical column densities (VCD) of NO2 show similar spatial distributions to OMI and TROPOMI (r > 0.88) and their difference is less than 27%. A comparison of the daily total VCD shows that EMI could detect the NO2 patterns in good agreement with OMI (r = 0.93) and TROPOMI (r = 0.95). However, the slant column density (SCD) uncertainty (0.79 × 1015 molec cm−2) of the current EMI algorithm is relatively larger than OMI. The daily variation pattern of NO2 from EMI in Beijing in January 2019 is consistent with TROPOMI (r = 0.96). The spatial distribution correlation of the tropospheric NO2 VCD of EMI with OMI and TROPOMI is 0.88 and 0.89, respectively, but shows an overestimate compared to OMI (15%) and TROPOMI (23%), respectively. This study demonstrates the capability of using EMI for global NO2 monitoring.

An improved tropospheric nitrogen dioxide (NO2) retrieval algorithm from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument based on air mass factor (AMF) calculations performed with more realistic model parameters is presented. The viewing angle-dependency of surface albedo is taken into account by improving the GOME-2 Lambertian-equivalent reflectivity (LER) climatology with a directionally dependent LER (DLER) dataset over land and an ocean surface albedo parametrization over water. A priori NO2 profiles with higher spatial and temporal resolutions are obtained from the IFS(CB05BASCOE) chemistry transport model based on recent emission inventories. A more realistic cloud treatment is provided by a Cloud-As-Layers (CAL) approach, which treats the clouds as uniform layers of water droplets, instead of the current Clouds-as-Reflecting-Boundaries (CRB) model, which assumes the clouds as Lambertian reflectors. Improvements in the AMF calculation affect the tropospheric NO2 columns on average within ±15 % in winter and ±5 % in summer over largely polluted regions. In addition, the impact of aerosols on our tropospheric NO2 retrieval is investigated by comparing the concurrent retrievals based on ground-based aerosol measurements (explicit aerosol correction) and aerosol-induced cloud parameters (implicit aerosol correction). Compared to the implicit aerosol correction through the CRB cloud parameters, the use of CAL reduces the AMF errors by more than 10 %. Finally, to evaluate the improved GOME-2 tropospheric NO2 columns, a validation is performed using ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAXDOAS) measurements at the BIRA-IASB Xianghe station. The improved tropospheric NO2 dataset shows good agreement with coincident ground-based measurements with a correlation coefficient of 0.94 and a relative difference of −9.9 % on average.

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5P) satellite launched in October 2017 yields a wealth of atmospheric composition data, including retrievals of total column ozone (TCO3) that are provided in near-real time (NRT) and off-line. These NRT TCO3 retrievals (V1.0.0) have been included in the data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS), and tests to monitor the data and to carry out first assimilation experiments with them have been performed for the period 26 November 2017 to 3 May 2018. TROPOMI was still in its commissioning phase until 24 April 2018. Nevertheless, the results show that, even at this early stage, the TROPOMI TCO3 data generally agree well with the CAMS analysis over large parts of the Globe and also with TCO3 retrievals from the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experiment-2 (GOME-2) that are routinely assimilated by CAMS. However, the TCO3 NRT data from TROPOMI show some retrieval anomalies at high latitudes, at low solar elevations and over snow/ice (e.g. Antarctica) where the differences with the CAMS analysis and the other datasets are larger. These differences come mainly from the surface albedo climatology that is used in the NRT TROPOMI TCO3 retrieval. This climatology has a coarser horizontal resolution than the TROPOMI TCO3 data which leads to problems in areas where there are large changes in reflectivity from pixel to pixel, e.g. pixels covered by snow/ice or not. The assimilation of TROPOMI TCO3 has been tested in the CAMS system for data between 60°N and 60°S and for solar elevations less than 10° and is found to have only little impact on the ozone analysis, because the CAMS analysis is already well constrained by several other ozone retrievals that are routinely assimilated. Variational bias correction is applied to the TROPOMI NRT TCO3 data and successfully corrects for the biases seen in the data. Averaged over the period 26 November 2017 to 3 May 2018, difference between experiments with and without assimilation of TROPOMI data are less than 2% for TCO3 and less than 1% in the vertical for averaged zonal mean O3 mixing ratios. Compared to independent observation (Brewer spectrometers, ozone sondes, IAGOS ozone profiles and GAW surface measurements) the differences between the assimilation run and a run without TROPOMI assimilation are also small. The only noteworthy differences between the experiment with and without assimilation of TROPOMI data are seen compared to IAGOS profiles at West African airports where the assimilation of TROPOMI improves the fit of the CAMS analysis to the independent data. Despite the small impact of TROPOMI TCO3 in the CAMS analysis it will be beneficial to include the TROPOMI TCO3 NRT data actively in the operational NRT CAMS analysis after more tests. This will add some redundancy and resilience in the system and will allow us to use a more robust observation system in case some of the other older instruments, whose retrievals are currently assimilated by CAMS, stop working.

In preparation of the TROPOMI/S5P launch in early 2017, a tropospheric ozone retrieval based on the convective cloud differential method was developed. For intensive tests we applied the algorithm to the total ozone columns and cloud data of the satellite instruments GOME, SCIAMACHY, OMI, GOME-2A and GOME-2B. Thereby a time series of 20 years (1995–2015) of tropospheric column ozone was generated. To have a consistent total ozone data set for all sensors, one common retrieval algorithm, namely GODFITv3, was applied and the L1 reflectances were also soft calibrated. The total ozone columns and the cloud data were input into the tropospheric ozone retrieval. However, the tropical tropospheric column ozone (TCO) for the individual instruments still showed small differences and, therefore, we harmonised the data set. For this purpose, a multilinear function was fitted to the averaged difference between SCIAMACHY's TCO and those from the other sensors. The original TCO was corrected by the fitted offset. GOME-2B data were corrected relative to the harmonised data from OMI and GOME-2A. The harmonisation leads to a better agreement between the different instruments. Also, a direct comparison of the TCO in the overlapping periods proves that GOME-2A agrees much better with SCIAMACHY after the harmonisation. The improvements for OMI were small. Based on the harmonised observations, we created a merged data product, containing the TCO from July 1995 to December 2015. A first application of this 20-year record is a trend analysis. The tropical trend is 0.7 ± 0.12 DU decade−1. Regionally the trends reach up to 1.8 DU decade−1 like on the African Atlantic coast, while over the western Pacific the tropospheric ozone declined over the last 20 years with up to 0.8 DU decade−1. The tropical tropospheric data record will be extended in the future with the TROPOMI/S5P data, where the TCO is part of the operational products.

This paper assesses the quality of IASI/Metop-A (IASI-A) and IASI/Metop-B (IASI-B) ozone (O3) products (total and partial O3 columns) retrieved with the Fast Optimal Retrievals on Layers for IASI Ozone (FORLI-O3) v20151001 software for nine years (2008–2017) through an extensive inter-comparison and validation exercise using independent observations (satellite, ground-based and ozonesonde). IASI-A and IASI-B Total O3 Columns (TOCs) are generally consistent, with a global mean difference less than 0.3 % for both day- and nighttime measurements, IASI-A being slightly higher than IASI-B. A global difference less than 2.4 % is found for the tropospheric (TROPO) O3 column product (IASI-A being lower than IASI-B), which is partly due to a temporary issue related to IASI-A viewing angle in 2015. Our validation shows that IASI-A and IASI-B TOCs are consistent with GOME-2, Dobson, Brewer and SAOZ retrieved ones, with global mean differences in the range 0.1–2 % depending on the instruments. The IASI-A and ground-based TOC comparison for the period 2008–July 2017 shows good long-term stability (negative trends within 3 % decade−1). The comparison results between IASI-A and IASI-B against smoothed ozonesonde partial O3 columns vary in altitude and latitude, with maximum standard deviation for the 300–150 hPa column (20–40 %) due to strong ozone variability and a priori uncertainty. The worst agreement with the ozonesondes and with UV-vis retrieved TOC [satellite and ground] is found at the southern high latitudes. Compared to ozonesonde data, IASI-A and IASI-B O3 products overestimate the O3 abundance in the stratosphere (up to 20 % for the 150–25 hPa column) and underestimates the O3 abundance in the troposphere (within 10 % for the mid-latitudes and ~ 18 % for the tropics). Based on the period 2011–2016, non-significant drift is found for the northern hemispheric tropospheric columns while a small drift prevails for the period before 2011.

An improved algorithm for the retrieval of total and tropospheric nitrogen dioxide (NO2) columns from the Global Ozone Monitoring Experiment-2 (GOME-2) is presented. The refined retrieval will be implemented in a future version of the GOME Data Processor (GDP) as used by the EUMETSAT Satellite Application Facility on Atmospheric Composition and UV Radiation (AC-SAF). The first main improvement is the application of an extended 425–497 nm wavelength fitting window in the differential optical absorption spectroscopy (DOAS) retrieval of the NO2 slant column density. Updated absorption cross-sections and a linear offset correction are used for the large fitting window. An improved slit function treatment is applied to compensate for both long-term and in-orbit drift of the GOME-2 slit function. Compared to the current operational (GDP 4.8) dataset, the use of these new features increases the NO2 columns by 1–3 × 1014 molec/cm2 and reduces the slant column error by ∼ 24 %. In addition, the bias between GOME-2A and GOME-2B measurements is largely reduced by adopting a new level 1b data version in the DOAS retrieval. The retrieved NO2 slant columns show good consistency with the Quality Assurance for Essential Climate Variables (QA4ECV) retrieval with a good overall quality. Second, the STRatospheric Estimation Algorithm from Mainz (STREAM), which was originally developed for the TROPOspheric Monitoring Instrument (TROPOMI) instrument, was optimized for GOME-2 measurements to determine the stratospheric NO2 column density. Applied to synthetic GOME-2 data, the estimated stratospheric NO2 columns from STREAM shows a good agreement with the a priori truth. An improved latitudinal correction is introduced in STREAM to reduce the biases over the subtropics. Applied to GOME-2 measurements, STREAM largely reduces the overestimation of stratospheric NO2 columns over polluted regions in the GDP 4.8 dataset. Third, the calculation of AMF applies an updated box-air mass factor (box-AMF) look-up table (LUT) calculated using the latest version of VLIDORT model with an increased number of reference points and vertical layers, a new GOME-2 surface albedo climatology, improved a priori NO2 profiles obtained from the TM5-MP chemistry transport model, and improved GOME-2 cloud parameters. A large effect on the retrieved tropospheric NO2 columns (more than 10 %) is found over polluted regions. To evaluate the GOME-2 tropospheric NO2 columns, an end-to-end validation is performed using ground-based multiple-axis DOAS (MAXDOAS) measurements. The validation is illustrated for 6 stations covering urban, suburban, and background situations. Compared to the GDP 4.8 product, the new dataset presents an improved agreement with the MAXDOAS measurements for all the stations.

NO2 is an important trace gas in both the stratosphere and troposphere. Since January 2007, the nadir-looking GOME-2 satellite instrument has been providing NO2 data on a global scale which is of great interest for climate research and atmospheric chemistry studies. In this contribution, we report on an improved algorithm for the retrieval of total and tropospheric NO2 columns from the GOME-2 instrument.

The total column water vapour product from the Global Ozone Monitoring Experiment-2 on board Metop-A and Metop-B satellites (GOME-2/Metop-A and GOME-2/Metop-B) produced by the Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M SAF) is compared with co-located radiosonde observations and global positioning system (GPS) retrievals. The validation is performed using recently reprocessed data by the GOME Data Processor (GDP) version 4.7. The time periods for the validation are January 2007–July 2013 (GOME-2A) and December 2012–July 2013 (GOME-2B). The radiosonde data are from the Integrated Global Radiosonde Archive (IGRA) maintained by the National Climatic Data Center (NCDC). The ground-based GPS observations from the COSMIC/SuomiNet network are used as the second independent data source. We find a good general agreement between the GOME-2 and the radiosonde/GPS data. The median relative difference of GOME-2 to the radiosonde observations is −2.7 % for GOME-2A and −0.3 % for GOME-2B. Against the GPS, the median relative differences are 4.9 % and 3.2 % for GOME-2A and B, respectively. For water vapour total columns below 10 kg m−2, large wet biases are observed, especially against the GPS retrievals. Conversely, at values above 50 kg m−2, GOME-2 generally underestimates both ground-based observations.

Knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors aboard EUMETSAT's MetOp-A and MetOp-B satellites and perform an extensive inter-comparison in order to evaluate their consistency and temporal stability. For the analysis, the GOME-2 data sets are generated by DLR in the framework of the EUMETSAT O3M-SAF project using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines a H2O and O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O total column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. The overall consistency between measurements from the newer GOME-2 instrument on board of the MetOp-B platform and the GOME-2/MetOp-A data is evaluated in the overlap period (December 2012–June 2014). Furthermore, we compare GOME-2 results with independent TCWV data from the ECMWF ERA-Interim reanalysis, with SSMIS satellite measurements during the full period January 2007–June 2014 and against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project (January 2007–December 2008). Global mean biases as small as ±0.035 g cm−2 are found between GOME-2A and all other data sets. The combined SSM/I-MERIS sample and the ECMWF ERA-Interim data set are typically drier than the GOME-2 retrievals, while on average GOME-2 data overestimate the SSMIS measurements by only 0.006 g cm−2. However, the size of these biases is seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, which include only data over ocean. The seasonal behaviour is not as evident when comparing GOME-2 TCWV to the ECMWF ERA-Interim and the SSM/I+MERIS data sets, since the different biases over land and ocean surfaces partly compensate each other. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three data sets, especially for land areas, although some discrepancies (bias larger than ±0.5 g cm−2) over ocean and over land areas with high humidity or a relatively large surface albedo are observed.

The two Global Ozone Monitoring Instrument (GOME-2) sensors operated in tandem are flying onboard EUMETSAT's (European Organisation for the Exploitation of Meteorological Satellites) MetOp-A and MetOp-B satellites, launched in October 2006 and September 2012 respectively. This paper presents the operational GOME-2/MetOp-A (GOME-2A) and GOME-2/MetOp-B (GOME-2B) total ozone products provided by the EUMETSAT Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M-SAF). These products are generated using the latest version of the GOME Data Processor (GDP version 4.7). The enhancements in GDP 4.7, including the application of Brion–Daumont–Malicet ozone absorption cross sections, are presented here. On a global scale, GOME-2B has the same high accuracy as the corresponding GOME-2A products. There is an excellent agreement between the ozone total columns from the two sensors, with GOME-2B values slightly lower with a mean difference of only 0.55±0.29%. First global validation results for 6 months of GOME-2B total ozone using ground-based measurements show that on average the GOME-2B total ozone data obtained with GDP 4.7 are slightly higher than, both, Dobson observations by about 2.0±1.0% and Brewer observations by about 1.0±0.8%. It is concluded that the total ozone columns (TOCs) provided by GOME-2A and GOME-2B are consistent and may be used simultaneously without introducing systematic effects, which has been illustrated for the Antarctic ozone hole on 18 October 2013. GOME-2A total ozone data have been used operationally in the Copernicus atmospheric service project MACC-II (Monitoring Atmospheric Composition and Climate – Interim Implementation) near-real-time (NRT) system since October 2013. The magnitude of the bias correction needed for assimilating GOME-2A ozone is reduced (to about −6 DU in the global mean) when the GOME-2 ozone retrieval algorithm changed to GDP 4.7.

This paper presents an extensive intercomparison and validation for the ozone (O3) product measured by the two Infrared Atmospheric Sounding Interferometers (IASIs) launched on board the MetOp-A and MetOp-B satellites in 2006 and in 2012 respectively. IASI O3 total columns and vertical profiles obtained from Fast Optimal Retrievals on Layers for IASI (FORLI) v20140922 software (running up until recently) are validated against independent observations during the period 2008-2014 on a global scale. On average for the period 2013-2014, IASI-A and IASI-B total ozone columns (TOCs) retrieved using FORLI are consistent, with IASI-B providing slightly lower values with a global difference of only 0.2±0.8%. The comparison between IASI-A and IASI-B O3 vertical profiles shows differences within ±2% over the entire altitude range. Global validation results for 7 years of IASI TOCs from FORLI against the Global Ozone Monitoring Experiment-2 (GOME-2) launched on board MetOp-A and Brewer-Dobson data show that, on average, IASI overestimates the ultraviolet (UV) data by 5-6% with the largest differences found in the southern high latitudes. The comparison with UV-visible SAOZ (Système d'Analyse par Observation Zénithale) measurements shows a mean bias between IASI and SAOZ TOCs of 2-4% in the midlatitudes and tropics and 7% at the polar circle. Part of the discrepancies found at high latitudes can be attributed to the limited information content in the observations due to low brightness temperatures. The comparison with ozonesonde vertical profiles (limited to 30km) shows that on average IASI with FORLI processing underestimates O3 by ∼ 5-15% in the troposphere while it overestimates O3 by ∼ 10-40% in the stratosphere, depending on the latitude. The largest relative differences are found in the tropical tropopause region; this can be explained by the low O3 amounts leading to large relative errors. In this study, we also evaluate an updated version of FORLI-O3 retrieval software (v20151001), using look-up tables recalculated to cover a larger spectral range using the latest HITRAN spectroscopic database (HITRAN 2012) and implementing numerical corrections. The assessment of the new O3 product with the same set of observations as that used for the validation exercise shows a correction of ∼ 4% for the TOC positive bias when compared to the UV ground-based and satellite observations, bringing the overall global comparison to ∼ 1-2% on average. This improvement is mainly associated with a decrease in the retrieved O3 concentration in the middle stratosphere (above 30hPa/25km) as shown by the comparison with ozonesonde data., SCOPUS: ar.j, info:eu-repo/semantics/published

This paper presents an extensive inter-comparison and validation for the ozone (O3) product measured by the two Infrared Atmospheric Sounding Interferometers (IASI) launched onboard the Metop-A and Metop-B satellites in 2006 and in 2012, respectively. IASI O3 total columns and vertical profiles obtained from Fast Optimal Retrievals on Layers for IASI (FORLI-O3) v20140922 software (running up until recently) are validated against independent observations during the period 2008–2014 on a global scale. On average for the period 2013–2014, IASI-A and IASI-B TOCs retrieved using FORLI are consistent, with IASI-B providing slightly lower values with a global difference of only 0.2±0.8 %. The comparison between IASI-A and IASI-B O3 vertical profiles shows differences within ±2 % over the entire altitude range. Global validation results for seven years of IASI TOCs from FORLI against GOME-2/Metop-A, Dobson and Brewer data show that, on average, IASI overestimates the UV data by 5–6 % with the largest differences found in the Southern high latitudes. The comparison with UV-vis SAOZ measurements shows a mean bias between IASI and SAOZ TOCs of 2–4 % in the mid-latitudes and tropics, and 7 % at the polar circle. Part of the discrepancies found at high latitudes can be attributed to the limited information content in the observations, due to low brightness temperatures. The comparison with ozonesonde vertical profiles (limited to 30 km) shows that on average IASI with FORLI processing underestimates O3 by ~5–15 % in the troposphere while it overestimates O3 by ~10–40 % in the stratosphere depending on the latitude. In the Northern middle latitudes, the bias varies within ±20 % for the entire altitude range. The largest relative differences are found in the tropical tropopause region; this can be explained by the low O3 amounts leading to large relative errors. In this study, we also evaluate an updated version of FORLI-O3 retrieval software (v20151001), using look-up tables recalculated to cover a larger spectral range using the latest HITRAN spectroscopic database (HITRAN 2012), and implementing numerical corrections. The assessement of the new O3 product with the same set of observations as that used for the validation exercise shows a correction of ~4 % for the TOC positive bias when compared to the UV ground-based and satellite observations, bringing the overall global comparison to ~1–2 % on average. This improvement is mainly associated with a decrease in the retrieved O3 concentration in the stratosphere (above 30 hPa/25 km) as shown by the comparison with ozonesonde data.

In preparation of the TROPOMI/S5P launch in autumn 2016 a tropospheric ozone retrieval based on the convective cloud differential method was developed. For intensive tests we applied the algorithm to the total ozone columns and cloud data of the satellites GOME, SCIAMACHY, OMI, GOME-2A and GOME-2B. Thereby a time series of 20 years (1995–2015) of tropospheric ozone columns was retrieved. To have a consistent total ozone data set for all sensors one common retrieval algorithm, namely GODFITv3, has been applied to all sensors and the L1 reflectances have also been soft calibrated. These data were input into the tropospheric ozone retrieval. However, the Tropical Tropospheric Ozone Columns (TTOC) for the individual instruments still showed small differences and therefore we harmonised the data set. For this purpose a multi-variant function was fitted to the averaged difference between SCIAMACHY's TTOC and those from the other sensors. The original TTOC was corrected by the fitted offset. GOME-2B data were corrected relative to the harmonised data from OMI and GOME-2A. The harmonisation leads to a better agreement between the different instruments. Also a direct comparison of the TTOCs in the overlapping periods proves that GOME-2A agrees much better with SCIAMACHY after the harmonisation. The improvements for OMI were small. The GOME and SCIAMACHY data overlap for one year for the complete tropics, this turned out to be insufficient to extrapolate back until 1995. Based on the harmonised observations, we created a merged data product, containing the TTOC from July 1995 to Dec. 2015. A first application of this 20 years record is a trend analysis. The global tropical trend is 0.75 ± 0.12 DU decade−1. Regionally the trends reaches up to 1.8 DU decade−1 like on the African Atlantic coast, over the Western Pacific the tropospheric ozone declined over the last 20 years with up to 0.8 DU decade−1. The tropical tropospheric data record will be extended in the future with the TROPOMI/S5P data, where the TTOC is part of the operational products.

The main aim of the paper is to assess the consistency of five years of Global Ozone Monitoring Experiment-2/Metop-A [GOME-2] total ozone columns and the long-term total ozone satellite monitoring database already in existence through an extensive inter-comparison and validation exercise using as reference Brewer and Dobson ground-based measurements. The behaviour of the GOME-2 measurements is being weighed against that of GOME (1995–2011), Ozone Monitoring Experiment [OMI] (since 2004) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY [SCIAMACHY] (since 2002) total ozone column products. Over the background truth of the ground-based measurements, the total ozone columns are inter-evaluated using a suite of established validation techniques; the GOME-2 time series follow the same patterns as those observed by the other satellite sensors. In particular, on average, GOME-2 data underestimate GOME data by about 0.80%, and underestimate SCIAMACHY data by 0.37% with no seasonal dependence of the differences between GOME-2, GOME and SCIAMACHY. The latter is expected since the three datasets are based on similar DOAS algorithms. This underestimation of GOME-2 is within the uncertainty of the reference data used in the comparisons. Compared to the OMI sensor, on average GOME-2 data underestimate OMI_DOAS (collection 3) data by 1.28%, without any significant seasonal dependence of the differences between them. The lack of seasonality might be expected since both the GOME data processor [GDP] 4.4 and OMI_DOAS are DOAS-type algorithms and both consider the variability of the stratospheric temperatures in their retrievals. Compared to the OMI_TOMS (collection 3) data, no bias was found. We hence conclude that the GOME-2 total ozone columns are well suitable to continue the long-term global total ozone record with the accuracy needed for climate monitoring studies.

This paper presents the algorithm for the operational near real time retrieval of total and tropospheric NO2 columns from the Global Ozone Monitoring Experiment (GOME-2). The retrieval is performed with the GOME Data Processor (GDP) version 4.4 as used by the EUMETSAT Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M-SAF). The differential optical absorption spectroscopy (DOAS) method is used to determine NO2 slant columns from GOME-2 (ir)radiance data in the 425–450 nm range. Initial total NO2 columns are computed using stratospheric air mass factors, and GOME-2 derived cloud properties are used to calculate the air mass factors for scenarios in the presence of clouds. To obtain the stratospheric NO2 component, a spatial filtering approach is used, which is shown to be an improvement on the Pacific reference sector method. Tropospheric air mass factors are computed using monthly averaged NO2 profiles from the MOZART-2 chemistry transport model. An error analysis shows that the random error in the GOME-2 NO2 slant columns is approximately 0.45 × 1015 molec cm−2. As a result of the improved quartz diffuser plate used in the GOME-2 instrument, the systematic error in the slant columns is strongly reduced compared to GOME/ERS-2. The estimated uncertainty in the GOME-2 tropospheric NO2 column for polluted conditions ranges from 40 to 80 %. An end-to-end ground-based validation approach for the GOME-2 NO2 columns is illustrated based on multi-axis MAXDOAS measurements at the Observatoire de Haute Provence (OHP). The GOME-2 stratospheric NO2 columns are found to be in good overall agreement with coincident ground-based measurements at OHP. A time series of the MAXDOAS and the GOME-2 tropospheric NO2 columns shows that pollution episodes at OHP are well captured by GOME-2. Monthly mean tropospheric columns are in very good agreement, with differences generally within 0.5 × 1015 molec cm−2.

International audience; Satellite-based remote sensing measurements of volcanic sulfur dioxide provide critical information for reducing volcanic hazards. This paper describes the use of SO2 measurements from the thermal infrared sounder IASI and the UV-VIS instrument GOME-2 in services related to aviation hazard and early warning of volcanic unrest. The high sensitivity of both instruments to SO2 allows the detection and global tracking of volcanic eruption plumes and makes them a valuable tool for volcanic aviation hazard mitigation. The GOME-2 and IASI SO2 data are produced in near-real time and distributed to the Volcanic Ash Advisory Centers (VAACS) to assist them in issuing alerts to airlines and air traffic control organizations. Examples of recent eruptions affecting air traffic are presented including Jebel al Tair (Yemen, September 2007), Mount Okmok (Alaska, July 2008), and Mount Kasatochi (Alaska, August 2008). In addition, GOME-2 can detect changes in the SO2 emissions from passively degassing volcanoes and, therefore, provide critical information for hazard assessment. The monitoring of pre-eruptive degassing by GOME-2 is used in early warning of volcanic activity by a mobile volcano fast response system in combination with numerous other parameters, such as seismicity, deformation, and thermal anomalies.

This paper compares total ozone measurements from five Brewer spectroradiometers located at the Iberian Peninsula with satellite observations given by the GOME (Global Ozone Monitoring Experiment) sensor. The analyzed period covers simultaneous ozone values from July 1995 until December 2004. The regression analysis shows an excellent agreement between Brewer-GOME values in the five locations; the coefficient of correlation is always higher than 0.92 and the root mean square error is about 3%. Moreover, the comparison shows that the satellite retrieval accuracy is within the uncertainty of current ground-based instruments. In addition, the effects of several variables, such as cloudiness, solar zenith angle (SZA), effective temperature and total ozone values in Brewer-GOME differences are analyzed. The results indicate that clouds induce a minor dependence of GOME values on the SZA. For example, during heavy cloudy conditions in Madrid station, GOME observations overestimate ground-based Brewer data for low AMF (low SZA values) by 2% while for high AMF (high SZA values) the satellite underestimates ground-based ozone values by 1%. Moreover, the dependence of Brewer-GOME differences with respect to SZA for cloud-free conditions may be due to the variability of effective temperature. This fact could indicate that the effective temperature estimated by GOME does not fully reflect the actual atmospheric temperature variability. Finally, GOME ozone observations slightly underestimate the highest values measured by the Brewer spectrophotometers and overestimates the lowest ground-based measurements. This work has been partially supported by INM with a postgraduate grant (project #16-2005/2006) and the visiting scientist program of EUMETSAT satellite application facility for ozone and atmospheric chemistry monitoring (O3M-SAF).

This paper presents the operational retrieval of tropical tropospheric ozone columns (TOC) from the Second Global Ozone Monitoring Experiment (GOME-2) instruments using the convective-cloud-differential (CCD) method. The retrieval is based on total ozone and cloud property data provided by the GOME Data Processor (GDP) 4.7, and uses above-cloud and clear-sky ozone column measurements to derive a monthly mean TOC between 20° N and 20° S. Validation of the GOME-2 TOC with several tropical ozonesonde sites shows good agreement, with a high correlation between the GOME-2 and sonde measurements, and small biases within ~ 3 DU. The TOC data have been used in combination with tropospheric NO2 measurements from GOME-2 to analyse the effect of the 2009–2010 El Niño–Southern Oscillation (ENSO) on the tropospheric ozone distribution in the tropics. El-Niño induced dry conditions in September–October 2009 resulted in relatively high tropospheric ozone columns over the southern Indian Ocean and northern Australia, while La Niña conditions in September–October 2010 resulted in a strong increase in tropospheric NO2 in South America, and enhanced ozone in the eastern Pacific and South America. Comparisons of the GOME-2 tropospheric ozone data with simulations of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model for 2009 El Nino conditions, illustrate the usefulness of the GOME-2 TOC measurements in evaluating chemistry climate models. Evaluation of CCMs with appropriate satellite observations helps to identify strengths and weaknesses of the model systems, providing a better understanding of driving mechanisms and adequate relations and feedbacks in the Earth atmosphere, and finally leading to improved models.

Mt. Etna volcano in Italy is one of the most active degassing volcanoes worldwide, emitting a mean of 1.7 Mt/year of Sulphur Dioxide (SO2) in quiescent periods. In this work, SO2 measurements retrieved by Moderate Resolution Imaging Spectroradiometer (MODIS), hyper-spectral Infrared Atmospheric Sounding Interferometer (IASI) and the second Global Ozone Monitoring Experiment (GOME-2) data are compared with the ground-based data from the FLux Automatic MEasurement monitoring network (FLAME). Among the eighteen lava fountain episodes occurring at Mt. Etna in 2011, the 10 April paroxysmal event has been selected as a case-study for the simultaneous observation of the SO2 cloud by satellite and ground-based sensors. For each data-set two retrieval techniques were adopted and the measurements of SO2 mass and flux with their respective uncertainty were obtained. With respect to the FLAME SO2 mass of 4.5 Gg, MODIS, IASI and GOME-2 differ by about 10%, 15% and 30%, respectively. The SO2 flux correlation coefficient between MODIS and FLAME is 0.84. All the retrievals within the respective errors are in agreement with the ground-based measurements supporting the validity of these space measurements.

Volcanic eruptions emit plumes of ash and gases into the atmosphere, potentially at very high altitudes. Ash-rich plumes are hazardous for airplanes as ash is very abrasive and easily melts inside their engines. With more than 50 active volcanoes per year and the ever-increasing number of commercial flights, the safety of airplanes is a real concern. Satellite measurements are ideal for monitoring global volcanic activity and, in combination with atmospheric dispersion models, to track and forecast volcanic plumes. Here we present the Support to Aviation Control Service (SACS, http://sacs.aeronomie.be), which is a free online service initiated by the European Space Agency (ESA) for the near-real-time (NRT) satellite monitoring of volcanic plumes of SO2 and ash. It combines data from three ultraviolet (UV)-visible and three infrared (IR) spectrometers. The UV-vis sensors are the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experiment-2 (GOME-2) on-board the two polar orbiting meteorological satellites (MetOp-A & MetOp-B) operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The IR sensors are the Atmospheric InfraRed Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI) on-board MetOp-A & MetOp-B. This new multi-sensor warning system of volcanic emissions is based on the selective detection of SO2 and ash. This system is optimised to avoid false alerts while at the same time limiting the number of notifications in case of large plumes. A successful rate with more than 95% of notifications corresponding to true volcanic activity is obtained by the SACS system. copyright © Author(s) 2014., SCOPUS: ar.j, info:eu-repo/semantics/published

A region of extreme low ozone values passed over North-western Europe during November 30, and December 1, 1999. The total ozone values were measured from the ground, with a Brewer Spectrophotometer, and from space with the GOME and TOMS satellite instruments. The ozone sonde measurement and the retrieved GOME ozone profile have shown that the main reduction in the ozone column occurred between the ozone maximum (22 km) and the tropopause. The low temperatures found in the stratosphere during this event have significant consequences for the ozone retrieval algorithm, both for satellite retrievals and for the Brewer measurements.

[1] The mobile DOAS technique was used to quantify the NOxvehicle emission of the central urban area, enclosed by the Inner Ring Viaduct Road (IRVR), of Shanghai, China. Three field measurement campaigns were performed during the pre-EXPO period in October 2009, the EXPO opening ceremony, and the closing ceremony period in 2010. The spatial and temporal distributions of NO2 Vertical Column Densities were derived, and the effects of traffic and wind conditions were studied. The averaged NO2 emissions rates from the IRVR area were determined to be 2.1 ± 0.9 ton/h, 2.8 ± 1.4 ton/h and 2.7 ± 1.4 ton/h for these three campaigns. The annual vehicle NOx emissions for the central urban area were estimated to be (2.3 ± 1.0) × 104 ton in 2009 and (3.0 ± 1.5) × 104 ton in 2010, and those for the whole city were (13.4 ± 5.9) × 104 ton in 2009 and (17.6 ± 8.4) × 104 ton in 2010, by considering a typical NO/NO2 ratio, NOx lifetime, as well as the traffic emission share of central urban area with respect to the entire city. The vehicle NOx emissions quantified by mobile DOAS were found to exhibit an increasing trend in the central urban area of Shanghai, by 1.6 times and 2.1 times from 2006 to 2009 and 2010. Due to wind field variations, unknown NOx chemical transformation, and uncertainties in AMFs, the systematic derivation of the final NOx emission rates obtained was about 55% for averaged several consecutive days.

The current discerning capability of nadir viewing satellite instruments is mainly providing information on large volcanic events, such as the Kasatochi 2008 and the Eyjafjoll 2010 eruptions, and areas with high anthropogenic SO2 sources such as Peruvian smeltering regions. Consequently, there exists a constant need to improve the algorithms in order to provide satellite information on the megacities’ SO2 levels for air quality purposes. In the current study, we aim to assess the observational capability of the GOME2/MetopA instrument by analysing the total SO2 load estimated over the extended Balkan region with the use of the high spatial resolution Comprehensive Air Quality Model with extensions (CAMx) modelling results. Two years of satellite and modelling estimates have been analysed so as to pin-point locations of constantly high SO2 loading, locations with a marked seasonal variability as well as locations with high expected loading that might not be visible from the satellite orbit. Regions of specific interest will be chosen for further investigation and algorithm development based on updated modelling input parameters such as the SO2 loading profile.