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An improved divergence method has been developed to estimate annual methane (CH4) emissions from TROPOspheric Monitoring Instrument (TROPOMI) observations. It has been applied to the period of 2018 to 2021 over the Middle East, where the orography is complicated, and the mean mixing ratio of methane (XCH4) might be affected by albedos or aerosols over some locations. To adapt to extreme changes of terrain over mountains or coasts, winds are used with their divergent part removed. A temporal filter is introduced to identify highly variable emissions and to further exclude fake sources caused by retrieval artifacts. We compare our results to widely used bottom-up anthropogenic emission inventories: Emissions Database for Global Atmospheric Research (EDGAR), Community Emissions Data System (CEDS), and Global Fuel Exploitation Inventory (GFEI) over several regions representing various types of sources. The NOx emissions are from EDGAR and Daily Emissions Constrained by Satellite Observations (DECSO), and the industrial heat sources identified by Visible Infrared Imaging Radiometer Suite (VIIRS) are further used to better understand our resulting methane emissions. Our results indicate possibly large underestimations of methane emissions in metropolises like Tehran (up to 50 %) and Isfahan (up to 70 %) in Iran. The derived annual methane emissions from oil/gas production near the Caspian Sea in Turkmenistan are comparable to GFEI but more than 2 times higher than EDGAR and CEDS in 2019. Large discrepancies in the distribution of methane sources in Riyadh and its surrounding areas are found between EDGAR, CEDS, GFEI, and our emissions. The methane emission from oil/gas production to the east of Riyadh seems to be largely overestimated by EDGAR and CEDS, while our estimates as well as GFEI and DECSO NOx indicate much lower emissions from industrial activities. On the other hand, regions like Iran, Iraq, and Oman are dominated by sources from oil and gas exploitation that probably include more irregular releases of methane, with the result that our estimates, which include only invariable sources, are lower than the bottom-up emission inventories.

The Sentinel-5 Precursor (S5P) satellite operated by the European Space Agency has carried the TROPOspheric Monitoring Instrument (TROPOMI) on a Sun-synchronous low-Earth orbit since 13 October 2017. The S5P mission has acquired more than 5 years of TROPOMI nadir ozone profile data retrieved from the level 0 to 1B processor version 2.0 and the level 1B to 2 optimal-estimation-based processor version 2.4.0. The latter is described in detail in this work, followed by the geophysical validation of the resulting ozone profiles for the period May 2018 to April 2023. Comparison of TROPOMI ozone profile data to co-located ozonesonde and lidar measurements used as references concludes to a median agreement better than 5 % to 10 % in the troposphere. The bias goes up to −15 % in the upper stratosphere (35–45 km) where it can exhibit vertical oscillations. The comparisons show a dispersion of about 30 % in the troposphere and 10 % to 20 % in the upper troposphere to lower stratosphere and in the middle stratosphere, which is close to mission requirements. Chi-square tests of the observed differences confirm on average the validity of the ex ante (prognostic) satellite and ground-based data uncertainty estimates in the middle stratosphere above about 20 km. Around the tropopause and below, the mean chi-square value increases up to about four, meaning that the ex ante TROPOMI uncertainty is underestimated. The information content of the ozone profile retrieval is characterised by about five to six vertical subcolumns of independent information and a vertical sensitivity (i.e. the fraction of the information that originates from the measurement) nearly equal to unity at altitudes from about 20 to 50 km, decreasing rapidly at altitudes above and below. The barycentre of the retrieved information is usually close to the nominal retrieval altitude in the 20–50 km altitude range, with positive and negative offsets of up to 10 km below and above this range, respectively. The effective vertical resolution of the profile retrieval usually ranges within 10–15 km, with a minimum close to 7 km in the middle stratosphere. Increased sensitivities and higher effective vertical resolutions are observed at higher solar zenith angles (above about 60°), as can be expected, and correlate with higher retrieved ozone concentrations. The vertical sensitivity of the TROPOMI tropospheric ozone retrieval is found to depend on the solar zenith angle, which translates into a seasonal and meridian dependence of the bias with respect to reference measurements. A similar although smaller effect can be seen for the viewing zenith angle. Additionally, the bias is negatively correlated with the surface albedo for the lowest three ozone subcolumns (0–18 km), despite the albedo's apparently slightly positive correlation with the retrieval degrees of freedom in the signal. For the 5 years of TROPOMI ozone profile data that are available now, an overall positive drift is detected for the same three subcolumns, while a negative drift is observed above (24–32 km), resulting in a negligible vertically integrated drift.

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.

The Ozone Monitoring Instrument (OMI) was launched on 15 July 2004, with an expected mission lifetime of 5 years. After more than 17 years in orbit the instrument is still functioning satisfactorily and in principle can continue doing so until the expected decommissioning of its platform Aura in 2025. In order to continue the datasets acquired by OMI and the Microwave Limb Sounder, the mission was extended up to at least 2023. Actions have been taken to ensure the proper functioning of the OMI operations, the data processing, and the calibration monitoring system until the eventual end of the mission. For the data processing a new level-0 (L0) to level-1b (L1b) data processor was built based on the recent developments for the TROPOspheric Monitoring Instrument (TROPOMI). With corrections for the degradation of the instrument now included, it is feasible to generate a new data collection to supersede the current collection-3 data products and reprocess the data of the entire mission up to now. This paper describes the differences between the collection-3 and collection-4 data. It will be shown that the collection-4 L1b data comprise a clear improvement with respect to the previous collections. By correcting for the gentle optical and electronic aging that has occurred over the past 17 years, OMI's ability to make trend-quality ozone measurements has further improved.

Vertical ozone profiles from combined spectral measurements in the ultraviolet and infrared spectral range were retrieved by using data from the TROPOspheric Monitoring Instrument on the Sentinel-5 Precursor (TROPOMI/S5P) and the Cross-track Infrared Sounder on the Suomi National Polar-orbiting Partnership (CrIS/Suomi-NPP), which are flying in loose formation 3 min apart in the same orbit. A previous study of ozone profiles retrieved exclusively from TROPOMI UV spectra showed that the vertical resolution in the troposphere is clearly limited (Mettig et al., 2021). The vertical resolution and the vertical extent of the ozone profiles is improved by combining both wavelength ranges compared to retrievals limited to UV or IR spectral data only. The combined retrieval particularly improves the accuracy of the retrieved tropospheric ozone and to a lesser degree stratospheric ozone up to 30 km. An increase in the degrees of freedom (DOF) by 1 DOF was found in the UV + IR retrieval compared to the UV-only retrieval. Compared to previous publications, which investigated combinations of UV and IR observations from the Ozone Monitoring Instrument and Tropospheric Emission Spectrometer (OMI and TES) and Global Ozone Monitoring Experiment version 2 and Infrared Atmospheric Sounding Interferometer (GOME-2 and IASI) pairs, the degree of freedom is lower, which is attributed to the reduced spectral resolution of CrIS compared to TES or IASI. Tropospheric lidar and ozonesondes were used to validate the ozone profiles and tropospheric ozone content (TOC). In their comparison with tropospheric lidars, both ozone profiles and TOCs show smaller biases for the retrieved data from the combined UV + IR observation than from the UV observations alone. For the ozone profiles below 10 km, the mean differences are around ±10 % and the mean TOC varies around ±3 DU. We show that TOCs from the combined retrieval agree better with ozonesonde results at northern latitudes than the UV-only and IR-only retrievals and also have lower scatter. In the tropics, the IR-only retrieval shows the best agrement with TOCs derived from ozonesondes. While in general the TOCs show good agreement with ozonesonde data, the profiles have a positive bias of around 30 % between 10 and 15 km. The reason is probably a positive stratospheric bias from the IR retrieval. The comparison of the UV + IR and UV ozone profiles up to 30 km with the Microwave Limb Sounder (MLS) demonstrates the improvement of the UV + IR profile in the stratosphere above 18 km. In comparison to the UV-only approach the retrieval shows improvements of up to 10 % depending on latitude but can also show worse results in some regions and latitudes.

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

Ozone in the troposphere affects humans and ecosystems as a pollutant and as a greenhouse gas. Observing, understanding and modelling this dual role, as well as monitoring effects of international regulations on air quality and climate change, however, challenge measurement systems to operate at opposite ends of the spatio-temporal scale ladder. Aboard the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aspires to take the next leap forward by measuring ozone and its precursors at unprecedented horizontal resolution until at least the mid-2020s. In this work, we assess the quality of TROPOMI's first release (V01.01.05–08) of tropical tropospheric ozone column (TrOC) data. Derived with the convective cloud differential (CCD) method, TROPOMI daily TrOC data represent the 3 d moving mean ozone column between the surface and 270 hPa under clear-sky conditions gridded at 0.5∘ latitude by 1∘ longitude resolution. Comparisons to almost 2 years of co-located SHADOZ ozonesonde and satellite data (Aura OMI and MetOp-B GOME-2) conclude to TROPOMI biases between −0.1 and +2.3 DU (<+13 %) when averaged over the tropical belt. The field of the bias is essentially uniform in space (deviations DU) and stable in time at the 1.5–2.5 DU level. However, the record is still fairly short, and continued monitoring will be key to clarify whether observed patterns and stability persist, alter behaviour or disappear. Biases are partially due to TROPOMI and the reference data records themselves, but they can also be linked to systematic effects of the non-perfect co-locations. Random uncertainty due to co-location mismatch contributes considerably to the 2.6–4.6 DU (∼14 %–23 %) statistical dispersion observed in the difference time series. We circumvent part of this problem by employing the triple co-location analysis technique and infer that TROPOMI single-measurement precision is better than 1.5–2.5 DU (∼8 %–13 %), in line with uncertainty estimates reported in the data files. Hence, the TROPOMI precision is judged to be 20 %–25 % better than for its predecessors OMI and GOME-2B, while sampling at 4 times better spatial resolution and almost 2 times better temporal resolution. Using TROPOMI tropospheric ozone columns at maximal resolution nevertheless requires consideration of correlated errors at small scales of up to 5 DU due to the inevitable interplay of satellite orbit and cloud coverage. Two particular types of sampling error are investigated, and we suggest how these can be identified or remedied. Our study confirms that major known geophysical patterns and signals of the tropical tropospheric ozone field are imprinted in TROPOMI's 2-year data record. These include the permanent zonal wave-one pattern, the pervasive annual and semiannual cycles, the high levels of ozone due to biomass burning around the Atlantic basin, and enhanced convective activity cycles associated with the Madden–Julian Oscillation over the Indo-Pacific warm pool. TROPOMI's combination of higher precision and higher resolution reveals details of these patterns and the processes involved, at considerably smaller spatial and temporal scales and with more complete coverage than contemporary satellite sounders. If the accuracy of future TROPOMI data proves to remain stable with time, these hold great potential to be included in Climate Data Records, as well as serve as a travelling standard to interconnect the upcoming constellation of air quality satellites in geostationary and low Earth orbits.

The TOPAS (Tikhonov regularised Ozone Profile retrievAl with SCIATRAN) algorithm to retrieve vertical profiles of ozone from space-borne observations in nadir-viewing geometry has been developed at the Institute of Environmental Physics (IUP) of the University of Bremen and applied to the TROPOspheric Monitoring Instrument (TROPOMI) L1B spectral data version 2. Spectral data between 270 and 329 nm are used for the retrieval. A recalibration of the measured radiances is done using ozone profiles from MLS/Aura. Studies with synthetic spectra show that individual profiles in the stratosphere can be retrieved with an uncertainty of about 10 %. In the troposphere, the retrieval errors are larger depending on the a priori profile used. The vertical resolution above 18 km is about 6–10 km, and it degrades to 15–25 km below. The vertical resolution in the troposphere is strongly dependent on the solar zenith angle (SZA). The ozone profiles retrieved from TROPOMI with the TOPAS algorithm were validated using data from ozonesondes and stratospheric ozone lidars. Above 18 km, the comparison with sondes shows excellent agreement within less than ±5 % for all latitudes. The standard deviation of mean differences is about 10 %. Below 18 km, the relative mean deviation in the tropics and northern latitudes is still quite good, remaining within ±20 %. At southern latitudes, larger differences of up to +40 % occur between 10 and 15 km. The standard deviation is about 50 % between 7–18 km and about 25 % below 7 km. The validation of stratospheric ozone profiles with ground-based lidar measurements also shows very good agreement. The relative mean deviation is below ±5 % between 18–45 km, with a standard deviation of 10 %. TOPAS retrieval results for 1 d of TROPOMI observations were compared to ozone profiles from the Microwave Limb Sounder (MLS) on the Aura satellite and the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP). The relative mean difference was found to be largely below ±5 % between 20–50 km, except at very high latitudes.

This paper reports on consolidated ground-based validation results of the atmospheric NO2 data produced operationally since April 2018 by the TROPOspheric Monitoring Instrument (TROPOMI) on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite. Tropospheric, stratospheric, and total NO2 column data from S5P are compared to correlative measurements collected from, respectively, 19 Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), 26 Network for the Detection of Atmospheric Composition Change (NDACC) Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 Pandonia Global Network (PGN)/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatio-temporal co-location of the satellite and correlative data, e.g. by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5P data show, on average, (i) a negative bias for the tropospheric column data, of typically −23 % to −37 % in clean to slightly polluted conditions but reaching values as high as −51 % over highly polluted areas; (ii) a slight negative median difference for the stratospheric column data, of about −0.2 Pmolec cm−2, i.e. approx. −2 % in summer to −15 % in winter; and (iii) a bias ranging from zero to −50 % for the total column data, found to depend on the amplitude of the total NO2 column, with small to slightly positive bias values for columns below 6 Pmolec cm−2 and negative values above. The dispersion between S5P and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec cm−2) but exceed those for the tropospheric column data (0.7 Pmolec cm−2). While a part of the biases and dispersion may be due to representativeness differences such as different area averaging and measurement times, it is known that errors in the S5P tropospheric columns exist due to shortcomings in the (horizontally coarse) a priori profile representation in the TM5-MP chemical transport model used in the S5P retrieval and, to a lesser extent, to the treatment of cloud effects and aerosols. Although considerable differences (up to 2 Pmolec cm−2 and more) are observed at single ground-pixel level, the near-real-time (NRTI) and offline (OFFL) versions of the S5P NO2 operational data processor provide similar NO2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.

Sentinel-5 Precursor (S-5P), launched in October 2017, carrying the TROPOspheric Monitoring Instrument (TROPOMI) nadir-viewing spectrometer, is the first mission of the Copernicus Programme dedicated to the monitoring of air quality, climate, and ozone. In the presented study, the TROPOMI tropospheric nitrogen dioxide (NO2) level-2 (L2) product (OFFL v1.03.01; 3.5 km × 7 km at nadir observations) has been validated over strongly polluted urban regions by comparison with coincident high-resolution Airborne Prism EXperiment (APEX) remote sensing observations (∼ 75 m × 120 m). Satellite products can be optimally assessed based on (APEX) airborne remote sensing observations, as a large amount of satellite pixels can be fully mapped at high accuracy and in a relatively short time interval, reducing the impact of spatiotemporal mismatches. In the framework of the S-5P validation campaign over Belgium (S5PVAL-BE), the APEX imaging spectrometer has been deployed during four mapping flights (26–29 June 2019) over the two largest urban regions in Belgium, i.e. Brussels and Antwerp, in order to map the horizontal distribution of tropospheric NO2. For each flight, 10 to 20 TROPOMI pixels were fully covered by approximately 2700 to 4000 APEX measurements within each TROPOMI pixel. The TROPOMI and APEX NO2 vertical column density (VCD) retrieval schemes are similar in concept. Overall, for the ensemble of the four flights, the standard TROPOMI NO2 VCD product is well correlated (R = 0.92) but biased negatively by −1.2 ± 1.2 × 1015 molec cm−2 or −14 ± 12 %, on average, with respect to coincident APEX NO2 retrievals. When replacing the coarse 1∘ × 1∘ the massively parallel (MP) version of the Tracer Model version 5 (TM5) a priori NO2 profiles by NO2 profile shapes from the Copernicus Atmospheric Monitoring Service (CAMS) regional chemistry transport model (CTM) ensemble at 0.1∘ × 0.1∘, R is 0.94 and the slope increases from 0.82 to 0.93. The bias is reduced to −0.1 ± 1.0 × 1015 molec cm−2 or −1.0 ± 12 %. The absolute difference is on average 1.3 × 1015 molec cm−2 (16 %) and 0.7 × 1015 molec cm−2 (9 %), when comparing APEX NO2 VCDs with TM5-MP-based and CAMS-based NO2 VCDs, respectively. Both sets of retrievals are well within the mission accuracy requirement of a maximum bias of 25 %–50 % for the TROPOMI tropospheric NO2 product for all individual compared pixels. Additionally, the APEX data set allows the study of TROPOMI subpixel variability and impact of signal smoothing due to its finite satellite pixel size, typically coarser than fine-scale gradients in the urban NO2 field. For a case study in the Antwerp region, the current TROPOMI data underestimate localized enhancements and overestimate background values by approximately 1–2 × 1015 molec cm−2 (10 %–20 %).

The ultraviolet (UV) Absorbing Aerosol Index (AAI) is widely used as an indicator for the presence of absorbing aerosols in the atmosphere. Here we consider the TROPOMI AAI based on the 340 nm/380 nm wavelength pair. We investigate the effects of clouds on the AAI observed at small and large scales. The large-scale effects are studied using an aggregate of TROPOMI measurements over an area mostly devoid of absorbing aerosols (Pacific Ocean). The study reveals that several structural features can be distinguished in the AAI, such as the cloud bow, viewing zenith angle dependence, sunglint, and a previously unexplained increase in AAI values at extreme viewing and solar geometries. We explain these features in terms of the bidirectional reflectance distribution function (BRDF) of the scene in combination with the different ratios of diffuse and direct illumination of the surface at 340 and 380 nm. To reduce the dependency on the BRDF and homogenize the AAI distribution across the orbit, we present three different AAI retrieval models: the traditional Lambertian scene model (LSM), a Lambertian cloud model (LCM), and a scattering cloud model (SCM). We perform a model study to assess the propagation of errors in auxiliary databases used in the cloud models. The three models are then applied to the same low-aerosol region. Results show that using the LCM and SCM gives on average a higher AAI than the LSM. Additionally, a more homogeneous distribution is retrieved across the orbit. At the small scale, related to the high spatial resolution of TROPOMI, strong local increases and decreases in AAI are observed in the presence of clouds. The BRDF effect presented here is a first step – more research is needed to explain the small-scale cloud effects on the AAI.

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

After the launch of the Sentinel-5 Precursor satellite on 13 October 2017, its single payload, the TROPOspheric Monitoring Instrument (TROPOMI), was commissioned for 6 months. In this time the instrument was tested and calibrated extensively. During this phase the geolocation calibration was validated using a dedicated measurement zoom mode. With the help of spacecraft manoeuvres the solar angle dependence of the irradiance radiometry was calibrated for both internal diffusers. This improved the results that were obtained on the ground significantly. Furthermore the orbital and long-term stability was tested for electronic gains, offsets, non-linearity, the dark current and the output of the internal light sources. The CCD output gain of the UV, UVIS and NIR detectors shows drifts over time which can be corrected in the Level 1b (L1b) processor. In-flight measurements also revealed inconsistencies in the radiometric calibration and degradation of the UV spectrometer. Degradation was also detected for the internal solar diffusers. Since the start of the nominal operations (E2) phase in orbit 2818 on 30 April 2018, regularly scheduled calibration measurements on the eclipse side of the orbit are used for monitoring and updates to calibration key data. This article reports on the main results of the commissioning phase, the in-flight calibration and the instrument's stability since launch. Insights from commissioning and in-flight monitoring have led to updates to the L1b processor and its calibration key data. The updated processor is planned to be used for nominal processing from late 2020 on.

The TROPOspheric Monitoring Instrument (TROPOMI) level-2 aerosol layer height (ALH) product has now been released to the general public. This product is retrieved using TROPOMI's measurements of the oxygen A-band, radiative transfer model (RTM) calculations augmented by neural networks and an iterative optimal estimation technique. The TROPOMI ALH product will deliver ALH estimates over cloud-free scenes over the ocean and land that contain aerosols above a certain threshold of the measured UV aerosol index (UVAI) in the ultraviolet region. This paper provides background for the ALH product and explores its quality by comparing ALH estimates to similar quantities derived from spaceborne lidars observing the same scene. The spaceborne lidar chosen for this study is the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, which flies in formation with NASA's A-train constellation since 2006 and is a proven source of data for studying ALHs. The influence of the surface and clouds is discussed, and the aspects of the TROPOMI ALH algorithm that will require future development efforts are highlighted. A case-by-case analysis of the data from the four selected cases (mostly around the Saharan region with approximately 800 co-located TROPOMI pixels and CALIOP profiles in June and December 2018) shows that ALHs retrieved from TROPOMI using the operational Sentinel-5 Precursor Level-2 ALH algorithm is lower than CALIOP aerosol extinction heights by approximately 0.5 km. Looking at data beyond these cases, it is clear that there is a significant difference when it comes to retrievals over land, where these differences can easily go over 1 km on average.

Before the launch of the TROPOspheric Monitoring Instrument (TROPOMI), only two other satellite instruments were able to observe aerosol plume heights globally, the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The TROPOMI aerosol layer height is a potential game changer, since it has daily global coverage, and the aerosol layer height retrieval is available in near real time. The aerosol layer height can be useful for aviation and air quality alerts, as well as for improving air quality forecasting related to wildfires. Here, TROPOMI's aerosol layer height product is evaluated with MISR and CALIOP observations for wildfire plumes in North America for the 2018 fire season (June to August). Further, observing system simulation experiments were performed to interpret the fundamental differences between the different products. The results show that MISR and TROPOMI are, in theory, very close for aerosol profiles with single plumes. For more complex profiles with multiple plumes, however, different plume heights are retrieved; the MISR plume height represents the top layer, and the plume height retrieved with TROPOMI tends to have an average altitude of several plume layers. The comparison between TROPOMI and MISR plume heights shows that, on average, the TROPOMI aerosol layer heights are lower, by approximately 600 m, compared to MISR, which is likely due to the different measurement techniques. From the comparison to CALIOP, our results show that the TROPOMI aerosol layer height is more accurate over dark surfaces, for thicker plumes, and plumes between approximately 1 and 4.5 km. MISR and TROPOMI are further used to evaluate the plume height of Environment and Climate Change Canada's operational forecasting system FireWork with fire plume injection height estimates from the Canadian Forest Fire Emissions Prediction System (CFFEPS). The modelled plume heights are similar compared to the satellite observations but tend to be slightly higher with average differences of 270–580 and 60–320 m compared to TROPOMI and MISR, respectively.

The Tropospheric Monitoring Instrument (TROPOMI), aboard the Sentinel-5 Precursor (S5P) satellite, launched on 13 October 2017, provides measurements of atmospheric trace gases and of cloud and aerosol properties at an unprecedented spatial resolution of approximately 7×3.5 km2 (approx. 5.5×3.5 km2 as of 6 August 2019), achieving near-global coverage in 1 d. The retrieval of nitrogen dioxide (NO2) concentrations is a three-step procedure: slant column density (SCD) retrieval, separation of the SCD in its stratospheric and tropospheric components, and conversion of these into vertical column densities. This study focusses on the TROPOMI NO2 SCD retrieval: the retrieval method used, the stability of the SCDs and the SCD uncertainties, and a comparison with the Ozone Monitoring Instrument (OMI) NO2 SCDs. The statistical uncertainty, based on the spatial variability of the SCDs over a remote Pacific Ocean sector, is 8.63 µmol m−2 for all pixels (9.45 µmol m−2 for clear-sky pixels), which is very stable over time and some 30 % less than the long-term average over OMI–QA4ECV data (since the pixel size reduction TROPOMI uncertainties are ∼8 % larger). The SCD uncertainty reported by the differential optical absorption spectroscopy (DOAS) fit is about 10 % larger than the statistical uncertainty, while for OMI–QA4ECV the DOAS uncertainty is some 20 % larger than its statistical uncertainty. Comparison of the SCDs themselves over the Pacific Ocean, averaged over 1 month, shows that TROPOMI is about 5 % higher than OMI–QA4ECV, which seems to be due mainly to the use of the so-called intensity offset correction in OMI–QA4ECV but not in TROPOMI: turning that correction off means about 5 % higher SCDs. The row-to-row variation in the SCDs of TROPOMI, the “stripe amplitude”, is 2.15 µmol m−2, while for OMI–QA4ECV it is a factor of ∼2 (∼5) larger in 2005 (2018); still, a so-called stripe correction of this non-physical across-track variation is useful for TROPOMI data. In short, TROPOMI shows a superior performance compared with OMI–QA4ECV and operates as anticipated from instrument specifications. The TROPOMI data used in this study cover 30 April 2018 up to 31 January 2020.

We present an observing simulated system experiment (OSSE) dedicated to evaluate the potential added value from the Sentinel-4 and the Sentinel-5P observations on tropospheric ozone composition. For this purpose, the ozone data of Sentinel-4 (Ultraviolet Visible Near-infrared) and Sentinel-5P (TROPOspheric Monitoring Instrument) on board a geostationary (GEO) and a low-Earth-orbit (LEO) platform, respectively, have been simulated using the DISAMAR inversion package for the summer 2003. To ensure the robustness of the results, the OSSE has been configured with conservative assumptions. We simulate the reality by combining two chemistry transport models (CTMs): the LOng Term Ozone Simulation – EURopean Operational Smog (LOTOS-EUROS) and the Transport Model version 5 (TM5). The assimilation system is based on a different CTM, the MOdèle de Chimie Atmosphérique à Grande Echelle (MOCAGE), combined with the 3-D variational technique. The background error covariance matrix does not evolve in time and its variance is proportional to the field values. The simulated data are formed of six eigenvectors to minimize the size of the dataset by removing the noise-dominated part of the observations. The results show that the satellite data clearly bring direct added value around 200 hPa for the whole assimilation period and for the whole European domain, while a likely indirect added value is identified but not for the whole period and domain at 500 hPa, and to a lower extent at 700 hPa. In addition, the ozone added value from Sentinel-5P (LEO) appears close to that from Sentinel-4 (GEO) in the free troposphere (200–500 hPa) in our OSSE. The outcome of our study is a result of the OSSE design and the choice within each of the components of the system.

To retrieve aerosol properties from satellite measurements of the oxygen A-band in the near-infrared, a line-by-line radiative transfer model implementation requires a large number of calculations. These calculations severely restrict a retrieval algorithm's operational capability as it can take several minutes to retrieve the aerosol layer height for a single ground pixel. This paper proposes a forward modelling approach using artificial neural networks to speed up the retrieval algorithm. The forward model outputs are trained into a set of neural network models to completely replace line-by-line calculations in the operational processor. Results comparing the forward model to the neural network alternative show an encouraging outcome with good agreement between the two when they are applied to retrieval scenarios using both synthetic and real measured spectra from TROPOMI (TROPOspheric Monitoring Instrument) on board the European Space Agency (ESA) Sentinel-5 Precursor mission. With an enhancement of the computational speed by 3 orders of magnitude, TROPOMI's operational aerosol layer height processor is now able to retrieve aerosol layer heights well within operational capacity.

The purpose of this study is to demonstrate the role of aerosol layer height (ALH) in quantifying the single scattering albedo (SSA) from ultraviolet satellite observations for biomass burning aerosols. In the first experiment, we retrieve SSA by minimizing the near-ultraviolet (near-UV) absorbing aerosol index (UVAI) difference between observed values and those simulated by a radiative transfer model. With the recently released S-5P TROPOMI ALH product constraining forward simulations, a significant gap in the retrieved SSA (0.25) is found between radiative transfer simulations with spectral flat aerosols and those with strong spectrally dependent aerosols, implying that inappropriate assumptions regarding aerosol absorption spectral dependence may cause severe misinterpretations of the aerosol absorption. In the second part of this paper, we propose an alternative method to retrieve SSA based on a long-term record of co-located satellite and ground-based measurements using the support vector regression (SVR) approach. This empirical method is free from the uncertainties due to the imperfection of a priori assumptions on aerosol microphysics seen in the first experiment. We present the potential capabilities of SVR using several fire events that have occurred in recent years. For all cases, the difference between SVR-retrieved SSA and AERONET are generally within ±0.05, and over half of the samples are within ±0.03. The results are encouraging, although in the current phase the model tends to overestimate the SSA for relatively absorbing cases and fails to predict SSA for some extreme situations. The spatial contrast in SSA retrieved by radiative transfer simulations is significantly higher than that retrieved by SVR, and the latter better agrees with SSA from MERRA-2 reanalysis. In the future, more sophisticated feature selection procedures and kernel functions should be taken into consideration to improve the SVR model accuracy. Moreover, the high-resolution TROPOMI UVAI and co-located ALH products will guide us to more reliable training data sets and more powerful algorithms to quantify aerosol absorption from UVAI records.

Global mapping of satellite tropospheric NO2 vertical column density (VCD), a key gas in air quality monitoring, requires accurate retrievals over complex urban and industrialized areas and under any atmospheric conditions. The high abundance of aerosol particles in regions dominated by anthropogenic fossil fuel combustion, e.g. megacities, and/or biomass-burning episodes, affects the space-borne spectral measurement. Minimizing the tropospheric NO2 VCD biases caused by aerosol scattering and absorption effects is one of the main retrieval challenges from air quality satellite instruments. In this study, the reference Ozone Monitoring Instrument (OMI) DOMINO-v2 product was reprocessed over cloud-free scenes, by applying new aerosol correction parameters retrieved from the 477 nm O2−O2 band, over eastern China and South America for 2 years (2006–2007). These new parameters are based on two different and separate algorithms developed during the last 2 years in view of an improved use of the OMI 477 nm O2−O2 band: the updated OMCLDO2 algorithm, which derives improved effective cloud parameters, the aerosol neural network (NN), which retrieves explicit aerosol parameters by assuming a more physical aerosol model. The OMI aerosol NN is a step ahead of OMCLDO2 because it primarily estimates an explicit aerosol layer height (ALH), and secondly an aerosol optical thickness τ for cloud-free observations. Overall, it was found that all the considered aerosol correction parameters reduce the biases identified in DOMINO-v2 over scenes in China with high aerosol abundance dominated by fine scattering and weakly absorbing particles, e.g. from [-20%:-40%] to [0 %:20 %] in summertime. The use of the retrieved OMI aerosol parameters leads in general to a more explicit aerosol correction and higher tropospheric NO2 VCD values, in the range of [0 %:40 %], than from the implicit correction with the updated OMCLDO2. This number overall represents an estimation of the aerosol correction strategy uncertainty nowadays for tropospheric NO2 VCD retrieval from space-borne visible measurements. The explicit aerosol correction theoretically includes a more realistic consideration of aerosol multiple scattering and absorption effects, especially over scenes dominated by strongly absorbing particles, where the correction based on OMCLDO2 seems to remain insufficient. However, the use of ALH and τ from the OMI NN aerosol algorithm is not a straightforward operation and future studies are required to identify the optimal methodology. For that purpose, several elements are recommended in this paper. Overall, we demonstrate the possibility of applying a more explicit aerosol correction by considering aerosol parameters directly derived from the 477 nm O2−O2 spectral band, measured by the same satellite instrument. Such an approach can, in theory, easily be transposed to the new-generation of space-borne instruments (e.g. TROPOMI on board Sentinel-5 Precursor), enabling a fast reprocessing of tropospheric NO2 data over cloud-free scenes (cloudy pixels need to be filtered out), as well as for other trace gas retrievals (e.g. SO2, HCHO).

The Sentinel-5 Precursor satellite was successfully launched on 13 October 2017, carrying the Tropospheric Monitoring Instrument (TROPOMI) as its single payload. TROPOMI is the next-generation atmospheric sounding instrument, continuing the successes of GOME, SCIAMACHY, OMI, and OMPS, with higher spatial resolution, improved sensitivity, and extended wavelength range. The instrument contains four spectrometers, divided over two modules sharing a common telescope, measuring the ultraviolet, visible, near-infrared, and shortwave infrared reflectance of the Earth. The imaging system enables daily global coverage using a push-broom configuration, with a spatial resolution as low as 7×3.5 km2 in nadir from a Sun-synchronous orbit at 824 km and an Equator crossing time of 13:30 local solar time. This article reports the pre-launch calibration status of the TROPOMI payload as derived from the on-ground calibration effort. Stringent requirements are imposed on the quality of on-ground calibration in order to match the high sensitivity of the instrument. A new methodology has been employed during the analysis of the obtained calibration measurements to ensure the consistency and validity of the calibration. This was achieved by using the production-grade Level 0 to 1b data processor in a closed-loop validation set-up. Using this approach the consistency between the calibration and the L1b product, as well as confidence in the obtained calibration result, could be established. This article introduces this novel calibration approach and describes all relevant calibrated instrument properties as they were derived before launch of the mission. For most of the relevant properties compliance with the calibration requirements could be established, including the knowledge of the instrument spectral and spatial response functions. Partial compliance was established for the straylight correction; especially the out-of-spectral-band correction for the near-infrared channel needs future validation. The absolute radiometric calibration of the radiance and irradiance responsivity is compliant with the high-level mission requirements, but not with the stricter calibration requirements as the available on-ground validation shows. The relative radiometric calibration of the Sun port was non-compliant. The non-compliant subjects will be addressed during the in-flight commissioning phase in the first 6 months following launch.

The absorbing aerosol index (AAI) is a qualitative parameter directly calculated from satellite-measured reflectance. Its sensitivity to absorbing aerosols in combination with a long-term data record since 1978 makes it an important parameter for climate research. In this study, we attempt to quantify aerosol absorption by retrieving the single-scattering albedo (ω0) at 550 nm from the satellite-measured AAI. In the first part of this study, AAI sensitivity studies are presented exclusively for biomass-burning aerosols. Later on, we employ a radiative transfer model (DISAMAR) to simulate the AAI measured by the Ozone Monitoring Instrument (OMI) in order to derive ω0 at 550 nm. Inputs for the radiative transfer calculations include satellite measurement geometry and surface conditions from OMI, aerosol optical thickness (τ) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and aerosol microphysical parameters from the AErosol RObotic NETwork (AERONET), respectively. This approach is applied to the Chile wildfires for the period from 26 to 30 January 2017, when the OMI-observed AAI of this event reached its peak. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) overpasses missed the evolution of the smoke plume over the research region; therefore the aerosol profile is parameterized. The simulated plume is at an altitude of 4.5–4.9 km, which is in good agreement with available CALIOP backscatter coefficient measurements. The data may contain pixels outside the plume, so an outlier detection criterion is applied. The results show that the AAI simulated by DISAMAR is consistent with satellite observations. The correlation coefficients fall into the range between 0.85 and 0.95. The retrieved mean ω0 at 550 nm for the entire plume over the research period from 26 to 30 January 2017 varies from 0.81 to 0.87, whereas the nearest AERONET station reported ω0 between 0.89 and 0.92. The difference in geolocation between the AERONET site and the plume, the assumption of homogeneous plume properties, the lack of the aerosol profile information and the uncertainties in the inputs for radiative transfer calculation are primarily responsible for this discrepancy in ω0.

This paper presents a weighted least squares approach to retrieve aerosol layer height from top-of-atmosphere reflectance measurements in the oxygen A band (758–770 nm) over bright surfaces. A property of the measurement error covariance matrix is discussed, due to which photons travelling from the surface are given a higher preference over photons that scatter back from the aerosol layer. This is a potential source of biases in the estimation of aerosol properties over land, which can be mitigated by revisiting the design of the measurement error covariance matrix. The alternative proposed in this paper, which we call the dynamic scaling method, introduces a scene-dependent and wavelength-dependent modification in the measurement signal-to-noise ratio in order to influence this matrix. This method is generally applicable to other retrieval algorithms using weighted least squares. To test this method, synthetic experiments are done in addition to application to GOME-2A and GOME-2B measurements of the oxygen A band over the August 2010 Russian wildfires and the October 2017 Portugal wildfire plume over western Europe.

Aerosol-layer height is essential for understanding the impact of aerosols on the climate system. As part of the European Space Agency Aerosol_cci project, aerosol-layer height as derived from passive thermal and solar satellite sensors measurements have been compared with aerosol-layer heights estimated from CALIOP measurements. The Aerosol_cci project targeted dust-type aerosol for this study. This ensures relatively unambiguous aerosol identification by the CALIOP processing chain. Dust-layer height was estimated from thermal IASI measurements using four different algorithms (from BIRA-IASB, DLR, LMD, LISA) and from solar GOME-2 (KNMI) and SCIAMACHY (IUP) measurements. Due to differences in overpass time of the various satellites, a trajectory model was used to move the CALIOP-derived dust heights in space and time to the IASI, GOME-2 and SCIAMACHY dust height pixels. It is not possible to construct a unique dust-layer height from the CALIOP data. Thus two CALIOP-derived layer heights were used: the cumulative extinction height defined as the height where the CALIOP extinction column is half of the total extinction column, and the geometric mean height, which is defined as the geometrical mean of the top and bottom heights of the dust layer. In statistical average over all IASI data there is a general tendency to a positive bias of 0.5–0.8 km against CALIOP extinction-weighted height for three of the four algorithms assessed, while the fourth algorithm has almost no bias. When comparing geometric mean height there is a shift of −0.5 km for all algorithms (getting close to zero for the three algorithms and turning negative for the fourth). The standard deviation of all algorithms is quite similar and ranges between 1.0 and 1.3 km. When looking at different conditions (day, night, land, ocean), there is more detail in variabilities (e.g. all algorithms overestimate more at night than during the day). For the solar sensors it is found that on average SCIAMACHY data are lower by −1.097 km (−0.961 km) compared to the CALIOP geometric mean (cumulative extinction) height, and GOME-2 data are lower by −1.393 km (−0.818 km).

A global picture of atmospheric aerosol vertical distribution with a high temporal resolution is of key importance not only for climate, cloud formation, and air quality research studies but also for correcting scattered radiation induced by aerosols in absorbing trace gas retrievals from passive satellite sensors. Aerosol layer height (ALH) was retrieved from the OMI 477 nm O2 − O2 band and its spatial pattern evaluated over selected cloud-free scenes. Such retrievals benefit from a synergy with MODIS data to provide complementary information on aerosols and cloudy pixels. We used a neural network approach previously trained and developed. Comparison with CALIOP aerosol level 2 products over urban and industrial pollution in eastern China shows consistent spatial patterns with an uncertainty in the range of 462–648 m. In addition, we show the possibility to determine the height of thick aerosol layers released by intensive biomass burning events in South America and Russia from OMI visible measurements. A Saharan dust outbreak over sea is finally discussed. Complementary detailed analyses show that the assumed aerosol properties in the forward modelling are the key factors affecting the accuracy of the results, together with potential cloud residuals in the observation pixels. Furthermore, we demonstrate that the physical meaning of the retrieved ALH scalar corresponds to the weighted average of the vertical aerosol extinction profile. These encouraging findings strongly suggest the potential of the OMI ALH product, and in more general the use of the 477 nm O2 − O2 band from present and future similar satellite sensors, for climate studies as well as for future aerosol correction in air quality trace gas retrievals.

On board the Copernicus Sentinel-5 Precursor (S5P) platform, the TROPOspheric Monitoring Instrument (TROPOMI) is a double-channel, nadir-viewing grating spectrometer measuring solar back-scattered earthshine radiances in the ultraviolet, visible, near-infrared, and shortwave infrared with global daily coverage. In the ultraviolet range, its spectral resolution and radiometric performance are equivalent to those of its predecessor OMI, but its horizontal resolution at true nadir is improved by an order of magnitude. This paper introduces the formaldehyde (HCHO) tropospheric vertical column retrieval algorithm implemented in the S5P operational processor and comprehensively describes its various retrieval steps. Furthermore, algorithmic improvements developed in the framework of the EU FP7-project QA4ECV are described for future updates of the processor. Detailed error estimates are discussed in the light of Copernicus user requirements and needs for validation are highlighted. Finally, verification results based on the application of the algorithm to OMI measurements are presented, demonstrating the performances expected for TROPOMI.

Retrieving aerosol optical thickness and aerosol layer height over a bright surface from measured top-of-atmosphere reflectance spectrum in the oxygen A band is known to be challenging, often resulting in large errors. In certain atmospheric conditions and viewing geometries, a loss of sensitivity to aerosol optical thickness has been reported in the literature. This loss of sensitivity has been attributed to a phenomenon known as critical surface albedo regime, which is a range of surface albedos for which the top-of-atmosphere reflectance has minimal sensitivity to aerosol optical thickness. This paper extends the concept of critical surface albedo for aerosol layer height retrievals in the oxygen A band, and discusses its implications. The underlying physics are introduced by analysing the top-of-atmosphere reflectance spectrum as a sum of atmospheric path contribution and surface contribution, obtained using a radiative transfer model. Furthermore, error analysis of an aerosol layer height retrieval algorithm is conducted over dark and bright surfaces to show the dependence on surface reflectance. The analysis shows that the derivative with respect to aerosol layer height of the atmospheric path contribution to the top-of-atmosphere reflectance is opposite in sign to that of the surface contribution – an increase in surface brightness results in a decrease in information content. In the case of aerosol optical thickness, these derivatives are anti-correlated, leading to large retrieval errors in high surface albedo regimes. The consequence of this anti-correlation is demonstrated with measured spectra in the oxygen A band from the GOME-2 instrument on board the Metop-A satellite over the 2010 Russian wildfires incident.

The Dutch–Finnish Ozone Monitoring Instrument (OMI) is an imaging spectrograph flying on NASA's EOS Aura satellite since 15 July 2004. OMI is primarily used to map trace-gas concentrations in the Earth's atmosphere, obtaining mid-resolution (0.4–0.6 nm) ultraviolet–visible (UV–VIS; 264–504 nm) spectra at multiple (30–60) simultaneous fields of view. Assessed via various approaches that include monitoring of radiances from selected ocean, land ice and cloud areas, as well as measurements of line profiles in the solar spectra, the instrument shows low optical degradation and high wavelength stability over the mission lifetime. In the regions relatively free from the slowly unraveling row anomaly (RA) the OMI irradiances have degraded by 3–8 %, while radiances have changed by 1–2 %. The long-term wavelength calibration of the instrument remains stable to 0.005–0.020 nm.

This paper presents an exploratory study on the aerosol layer height (ALH) retrieval from the OMI 477 nm O2 − O2 spectral band. We have developed algorithms based on the multilayer perceptron (MLP) neural network (NN) approach and applied them to 3-year (2005–2007) OMI cloud-free scenes over north-east Asia, collocated with MODIS Aqua aerosol product. In addition to the importance of aerosol altitude for climate and air quality objectives, our long-term motivation is to evaluate the possibility of retrieving ALH for potential future improvements of trace gas retrievals (e.g. NO2, HCHO, SO2) from UV–visible air quality satellite measurements over scenes including high aerosol concentrations. This study presents a first step of this long-term objective and evaluates, from a statistic point of view, an ensemble of OMI ALH retrievals over a long time period of 3 years covering a large industrialized continental region. This ALH retrieval relies on the analysis of the O2 − O2 slant column density (SCD) and requires an accurate knowledge of the aerosol optical thickness, τ. Using MODIS Aqua τ(550 nm) as a prior information, absolute seasonal differences between the LIdar climatology of vertical Aerosol Structure for space-based lidar simulation (LIVAS) and average OMI ALH, over scenes with MODIS τ(550 nm) ≥ 1. 0, are in the range of 260–800 m (assuming single scattering albedo ω0 = 0. 95) and 180–310 m (assuming ω0 = 0. 9). OMI ALH retrievals depend on the assumed aerosol single scattering albedo (sensitivity up to 660 m) and the chosen surface albedo (variation less than 200 m between OMLER and MODIS black-sky albedo). Scenes with τ ≤ 0. 5 are expected to show too large biases due to the little impact of particles on the O2 − O2 SCD changes. In addition, NN algorithms also enable aerosol optical thickness retrieval by exploring the OMI reflectance in the continuum. Comparisons with collocated MODIS Aqua show agreements between −0. 02 ± 0. 45 and −0. 18 ± 0. 24, depending on the season. Improvements may be obtained from a better knowledge of the surface albedo and higher accuracy of the aerosol model. Following the previous work over ocean of Park et al.(2016), our study shows the first encouraging aerosol layer height retrieval results over land from satellite observations of the 477 nm O2 − O2 absorption spectral band.

The OMI (Ozone Monitoring Instrument on board NASA's Earth Observing System (EOS) Aura satellite) OMCLDO2 cloud product supports trace gas retrievals of for example ozone and nitrogen dioxide. The OMCLDO2 algorithm derives the effective cloud fraction and effective cloud pressure using a DOAS (differential optical absorption spectroscopy) fit of the O2–O2 absorption feature around 477 nm. A new version of the OMI OMCLDO2 cloud product is presented that contains several improvements, of which the introduction of a temperature correction on the O2–O2 slant columns and the updated look-up tables have the largest impact. Whereas the differences in the effective cloud fraction are on average limited to 0.01, the differences of the effective cloud pressure can be up to 200 hPa, especially at cloud fractions below 0.3. As expected, the temperature correction depends on latitude and season. The updated look-up tables have a systematic effect on the cloud pressure at low cloud fractions. The improvements at low cloud fractions are very important for the retrieval of trace gases in the lower troposphere, for example for nitrogen dioxide and formaldehyde. The cloud pressure retrievals of the improved algorithm are compared with ground-based radar–lidar observations for three sites at mid-latitudes. For low clouds that have a limited vertical extent the comparison yields good agreement. For higher clouds, which are vertically extensive and often contain several layers, the satellite retrievals give a lower cloud height. For high clouds, mixed results are obtained.

The Measurements of Pollution in the Troposphere (MOPITT) instrument is the only satellite-borne sensor in operation that uses both thermal (TIR) and near-infrared (NIR) channels to estimate CO profiles. With more than 15 years (2000 to present) of validated multispectral observations, MOPITT provides the unique capability to separate CO in the lowermost troposphere (LMT, surface to 3 km (∼ 700 hPa)) from the free-tropospheric abundance. To extend this record, a new, hyper-spectral approach is presented here that will provide CO data products exceeding the capabilities of MOPITT by combining the short-wavelength infrared (SWIR, equivalent to the MOPITT NIR) channels from the TROPOspheric Monitoring Instrument (TROPOMI) to be launched aboard the European Sentinel 5 Precursor (S5p) satellite in 2016 and the TIR channels from the Cross-track Infrared Sounder (CrIS) aboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. We apply the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES) retrieval algorithm to quantify the potential of this joint CO product. CO profiles are retrieved from a single-footprint, full-spectral-resolution CrIS transect over Africa on 27–28 August 2013 coincident with significant biomass burning. Comparisons of collocated CrIS and MOPITT CO observations for the LMT show a mean difference of 2.8 ± 24.9 ppb, which is well within the estimated measurement uncertainty of both sensors. The estimated degrees of freedom (DOF) for CO signals from synergistic CrIS–TROPOMI retrievals are approximately 0.9 in the LMT and 1.3 above the LMT, which indicates that the LMT CO can be distinguished from the free troposphere, similar to MOPITT multispectral observations (0.8 in the LMT, and 1.1 above the LMT). In addition to increased sensitivity, the combined retrievals reduce measurement uncertainty, with ∼ 15 % error reduction in the LMT. With a daily global coverage and a combined spatial footprint of 14 km, the joint CrIS–TROPOMI measurements have the potential to extend and improve upon the MOPITT multispectral CO data records for the coming decade.

The Ozone Monitoring Instrument (OMI) has provided daily global measurements of tropospheric NO2 for more than a decade. Numerous studies have drawn attention to the complexities related to measurements of tropospheric NO2 in the presence of aerosols. Fine particles affect the OMI spectral measurements and the length of the average light path followed by the photons. However, they are not explicitly taken into account in the current operational OMI tropospheric NO2 retrieval chain (DOMINO – Derivation of OMI tropospheric NO2) product. Instead, the operational OMI O2 − O2 cloud retrieval algorithm is applied both to cloudy and to cloud-free scenes (i.e. clear sky) dominated by the presence of aerosols. This paper describes in detail the complex interplay between the spectral effects of aerosols in the satellite observation and the associated response of the OMI O2 − O2 cloud retrieval algorithm. Then, it evaluates the impact on the accuracy of the tropospheric NO2 retrievals through the computed Air Mass Factor (AMF) with a focus on cloud-free scenes. For that purpose, collocated OMI NO2 and MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua aerosol products are analysed over the strongly industrialized East China area. In addition, aerosol effects on the tropospheric NO2 AMF and the retrieval of OMI cloud parameters are simulated. Both the observation-based and the simulation-based approach demonstrate that the retrieved cloud fraction increases with increasing Aerosol Optical Thickness (AOT), but the magnitude of this increase depends on the aerosol properties and surface albedo. This increase is induced by the additional scattering effects of aerosols which enhance the scene brightness. The decreasing effective cloud pressure with increasing AOT primarily represents the shielding effects of the O2 − O2 column located below the aerosol layers. The study cases show that the aerosol correction based on the implemented OMI cloud model results in biases between −20 and −40 % for the DOMINO tropospheric NO2 product in cases of high aerosol pollution (AOT ≥ 0.6) at elevated altitude. These biases result from a combination of the cloud model error, used in the presence of aerosols, and the limitations of the current OMI cloud Look-Up-Table (LUT). A new LUT with a higher sampling must be designed to remove the complex behaviour between these biases and AOT. In contrast, when aerosols are relatively close to the surface or mixed with NO2, aerosol correction based on the cloud model results in an overestimation of the DOMINO tropospheric NO2 column, between 10 and 20 %. These numbers are in line with comparison studies between ground-based and OMI tropospheric NO2 measurements in the presence of high aerosol pollution and particles located at higher altitudes. This highlights the need to implement an improved aerosol correction in the computation of tropospheric NO2 AMFs.

An algorithm setup for the operational Aerosol Layer Height product for TROPOMI on the Sentinel-5 Precursor mission is described and discussed, applied to GOME-2A data, and evaluated with lidar measurements. The algorithm makes a spectral fit of reflectance at the O2 A band in the near-infrared and the fit window runs from 758 to 770 nm. The aerosol profile is parameterised by a scattering layer with constant aerosol volume extinction coefficient and aerosol single scattering albedo and with a fixed pressure thickness. The algorithm's target parameter is the height of this layer. In this paper, we apply the algorithm to observations from GOME-2A in a number of systematic and extensive case studies, and we compare retrieved aerosol layer heights with lidar measurements. Aerosol scenes cover various aerosol types, both elevated and boundary layer aerosols, and land and sea surfaces. The aerosol optical thicknesses for these scenes are relatively moderate. Retrieval experiments with GOME-2A spectra are used to investigate various sensitivities, in which particular attention is given to the role of the surface albedo. From retrieval simulations with the single-layer model, we learn that the surface albedo should be a fit parameter when retrieving aerosol layer height from the O2 A band. Current uncertainties in surface albedo climatologies cause biases and non-convergences when the surface albedo is fixed in the retrieval. Biases disappear and convergence improves when the surface albedo is fitted, while precision of retrieved aerosol layer pressure is still largely within requirement levels. Moreover, we show that fitting the surface albedo helps to ameliorate biases in retrieved aerosol layer height when the assumed aerosol model is inaccurate. Subsequent retrievals with GOME-2A spectra confirm that convergence is better when the surface albedo is retrieved simultaneously with aerosol parameters. However, retrieved aerosol layer pressures are systematically low (i.e., layer high in the atmosphere) to the extent that retrieved values no longer realistically represent actual extinction profiles. When the surface albedo is fixed in retrievals with GOME-2A spectra, convergence deteriorates as expected, but retrieved aerosol layer pressures become much higher (i.e., layer lower in atmosphere). The comparison with lidar measurements indicates that retrieved aerosol layer heights are indeed representative of the underlying profile in that case. Finally, subsequent retrieval simulations with two-layer aerosol profiles show that a model error in the assumed profile (two layers in the simulation but only one in the retrieval) is partly absorbed by the surface albedo when this parameter is fitted. This is expected in view of the correlations between errors in fit parameters and the effect is relatively small for elevated layers (less than 100 hPa). If one of the scattering layers is near the surface (boundary layer aerosols), the effect becomes surprisingly large, in such a way that the retrieved height of the single layer is above the two-layer profile. Furthermore, we find that the retrieval solution, once retrieval converges, hardly depends on the starting values for the fit. Sensitivity experiments with GOME-2A spectra also show that aerosol layer height is indeed relatively robust against inaccuracies in the assumed aerosol model, even when the surface albedo is not fitted. We show spectral fit residuals, which can be used for further investigations. Fit residuals may be partly explained by spectroscopic uncertainties, which is suggested by an experiment showing the improvement of convergence when the absorption cross section is scaled in agreement with Butz et al. (2013) and Crisp et al. (2012), and a temperature offset to the a priori ECMWF temperature profile is fitted. Retrieved temperature offsets are always negative and quite large (ranging between −4 and −8 K), which is not expected if temperature offsets absorb remaining inaccuracies in meteorological data. Other sensitivity experiments investigate fitting of stray light and fluorescence emissions. We find negative radiance offsets and negative fluorescence emissions, also for non-vegetated areas, but from the results it is not clear whether fitting these parameters improves the retrieval. Based on the present results, the operational baseline for the Aerosol Layer Height product currently will not fit the surface albedo. The product will be particularly suited for elevated, optically thick aerosol layers. In addition to its scientific value in climate research, anticipated applications of the product for TROPOMI are providing aerosol height information for aviation safety and improving interpretation of the Absorbing Aerosol Index.

An improved nitrogen dioxide (NO2) slant column density retrieval for the Ozone Monitoring Instrument (OMI) in the 405–465 nm spectral region is presented. Since the launch of OMI on board NASA's EOS-Aura satellite in 2004, differential optical absorption spectroscopy (DOAS) retrievals of NO2 slant column densities have been the starting point for the KNMI DOMINO and NASA SP NO2 vertical column data as well as the OMI NO2 data of some other institutes. However, recent intercomparisons between NO2 retrievals from OMI and other UV/Vis and limb spectrometers, as well as ground-based measurements, suggest that OMI stratospheric NO2 is biased high. This study revises and, for the first time, fully documents the OMI NO2 retrieval in detail. The representation of the OMI slit function to convolve high-resolution reference spectra onto the relevant spectral grid is improved. The window used for the wavelength calibration is optimised, leading to much-reduced fitting errors. Ozone and water vapour spectra used in the fit are updated, reflecting the recently improved knowledge of their absorption cross section in the literature. The improved spectral fit also accounts for absorption by the O2–O2 collision complex and by liquid water over clear-water areas. The main changes in the improved spectral fitting result from the updates related to the wavelength calibration: the RMS error of the fit is reduced by 23% and the NO2 slant column by 0.85 × 1015 molec cm−2, independent of latitude, solar zenith angle and NO2 value. Including O2–O2 and liquid water absorption and updating the O3 and water vapour cross-section spectra further reduces NO2 slant columns on average by 0.35 × 1015 molec cm−2, accompanied by a further 9% reduction in the RMS error of the fit. The improved OMI NO2 slant columns are consistent with independent NO2 retrievals from other instruments to within a range that can be explained by photochemically driven diurnal increases in stratospheric NO2 and by small differences in fitting window and approach. The revisions indicate that current OMI NO2 slant columns suffered mostly from an additive positive offset, which is removed by the improved wavelength calibration and representation of the OMI slit function. It is therefore anticipated that the improved NO2 slant columns are most important to retrievals of spatially homogeneous stratospheric NO2 rather than to heterogeneous tropospheric NO2.

We have assessed the sensitivity of the operational Ozone Monitoring Instrument (OMI) ozone profile retrieval algorithm to a number of a priori and radiative transfer assumptions. We studied the effect of stray light correction, surface albedo assumptions and a priori ozone profiles on the retrieved ozone profile. Then, we studied how to modify the algorithm to improve the retrieval of tropospheric ozone. We found that stray light corrections have a significant effect on the retrieved ozone profile but mainly at high altitudes. Surface albedo assumptions, on the other hand, have the largest impact at the lowest layers. Choice of an ozone profile climatology which is used as a priori information has small effects on the retrievals at all altitudes. However, the usage of climatological a priori covariance matrix has a significant effect. Based on these sensitivity tests, we made several modifications to the retrieval algorithm: the a priori ozone climatology was replaced with a new tropopause-dependent climatology, the a priori covariance matrix was calculated from the climatological ozone variability values, and the surface albedo was assumed to be linearly dependent on wavelength in the 311.5–330 nm channel. As expected, we found that the a priori covariance matrix basically defines the vertical distribution of degrees of freedom for a retrieval. Moreover, our case study over Europe showed that the modified version produced over 10% smaller ozone abundances in the troposphere which reduced the systematic overestimation of ozone in the retrieval algorithm and improved correspondence with Infrared Atmospheric Sounding Instrument (IASI) retrievals. The comparison with ozonesonde measurements over North America showed that the operational retrieval performed better in the upper troposphere/lower stratosphere (UTLS), whereas the modified version improved the retrievals in the lower troposphere and upper stratosphere. These comparisons showed that the systematic biases in the OMI ozone profile retrievals are not caused by the a priori information but by some still unidentified problem in the radiative transfer modelling. Instead, the a priori information pushes the systematically wrong ozone profiles towards the true values. The smaller weight of the a priori information in the modified retrieval leads to better visibility of tropospheric ozone structures, because it has a smaller tendency to damp the variability of the retrievals in the troposphere. In summary, the modified retrieval unmasks systematic problems in the radiative transfer/instrument model and is more sensitive to tropospheric ozone variation; that is, it is able to capture the tropospheric ozone morphology better.

This paper evaluates the agreement between stratospheric NO2 retrievals from infrared limb sounders (Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and High Resolution Dynamics Limb Sounder (HIRDLS)) and solar UV/VIS backscatter sensors (Ozone Monitoring Instrument (OMI), Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) limb and nadir) over the 2005–2007 period and across the seasons. The observational agreement is contrasted with the representation of NO2 profiles in 3-D chemical transport models such as the Whole Atmosphere Community Climate Model (WACCM) and TM4. A conclusion central to this work is that the definition of a reference for stratospheric NO2 columns formed by consistent agreement among SCIAMACHY, MIPAS and HIRDLS limb records (all of which agree to within 0.25 × 1015 molecules cm−2 or better than 10%) allows us to draw attention to relative errors in other data sets, e.g., (1) WACCM overestimates NO2 densities in the extratropical lower stratosphere, particularly in the springtime and over northern latitudes by up to 35% relative to limb observations, and (2) there are remarkable discrepancies between stratospheric NO2 column estimates from limb and nadir techniques, with a characteristic seasonally and latitudinally dependent pattern. We find that SCIAMACHY nadir and OMI stratospheric columns show overall biases of −0.5 × 1015 molecules cm−2 (−20%) and +0.6 × 1015 molecules cm−2 (+20%) relative to limb observations, respectively. It is argued that additive biases in nadir stratospheric columns are not expected to affect tropospheric retrievals significantly, and that they can be attributed to errors in the total slant column density, related either to algorithmic or instrumental effects. In order to obtain accurate and long-term time series of stratospheric NO2, an effort towards the harmonization of currently used differential optical absorption spectroscopy (DOAS) approaches to nadir retrievals becomes essential, as well as their agreement to limb and ground-based observations, particularly now that limb techniques are giving way to nadir observations as the next generation of climate and air quality monitoring instruments pushes forth.

Satellite instruments are nowadays successfully utilised for measuring atmospheric aerosol in many applications as well as in research. Therefore, there is a growing need for rigorous error characterisation of the measurements. Here, we introduce a methodology for quantifying the uncertainty in the retrieval of aerosol optical thickness (AOT). In particular, we concentrate on two aspects: uncertainty due to aerosol microphysical model selection and uncertainty due to imperfect forward modelling. We apply the introduced methodology for aerosol optical thickness retrieval of the Ozone Monitoring Instrument (OMI) on board NASA's Earth Observing System (EOS) Aura satellite, launched in 2004. We apply statistical methodologies that improve the uncertainty estimates of the aerosol optical thickness retrieval by propagating aerosol microphysical model selection and forward model error more realistically. For the microphysical model selection problem, we utilise Bayesian model selection and model averaging methods. Gaussian processes are utilised to characterise the smooth systematic discrepancies between the measured and modelled reflectances (i.e. residuals). The spectral correlation is composed empirically by exploring a set of residuals. The operational OMI multi-wavelength aerosol retrieval algorithm OMAERO is used for cloud-free, over-land pixels of the OMI instrument with the additional Bayesian model selection and model discrepancy techniques introduced here. The method and improved uncertainty characterisation is demonstrated by several examples with different aerosol properties: weakly absorbing aerosols, forest fires over Greece and Russia, and Sahara desert dust. The statistical methodology presented is general; it is not restricted to this particular satellite retrieval application.

Peak stratospheric chlorofluorocarbon (CFC) and other ozone depleting substance (ODS) concentrations were reached in the mid- to late 1990s. Detection and attribution of the expected recovery of the stratospheric ozone layer in an atmosphere with reduced ODSs as well as efforts to understand the evolution of stratospheric ozone in the presence of increasing greenhouse gases are key current research topics. These require a critical examination of the ozone changes with an accurate knowledge of the spatial (geographical and vertical) and temporal ozone response. For such an examination, it is vital that the quality of the measurements used be as high as possible and measurement uncertainties well quantified. In preparation for the 2014 United Nations Environment Programme (UNEP)/World Meteorological Organization (WMO) Scientific Assessment of Ozone Depletion, the SPARC/IO3C/IGACO-O3/NDACC (SI2N) Initiative was designed to study and document changes in the global ozone profile distribution. This requires assessing long-term ozone profile data sets in regards to measurement stability and uncertainty characteristics. The ultimate goal is to establish suitability for estimating long-term ozone trends to contribute to ozone recovery studies. Some of the data sets have been improved as part of this initiative with updated versions now available. This summary presents an overview of stratospheric ozone profile measurement data sets (ground and satellite based) available for ozone recovery studies. Here we document measurement techniques, spatial and temporal coverage, vertical resolution, native units and measurement uncertainties. In addition, the latest data versions are briefly described (including data version updates as well as detailing multiple retrievals when available for a given satellite instrument). Archive location information for each data set is also given.

We describe a new algorithm for the retrieval of nitrogen dioxide (NO2) vertical columns from nadir-viewing satellite instruments. This algorithm (SP2) is the basis for the Version 2.1 OMI This algorithm (SP2) is the basis for the Version 2.1 Ozone Monitoring Instrument (OMI) NO2 Standard Product and features a novel method for separating the stratospheric and tropospheric columns. NO2 Standard Product and features a novel method for separating the stratospheric and tropospheric columns. The approach estimates the stratospheric NO2 directly from satellite data without using stratospheric chemical transport models or assuming any global zonal wave pattern. Tropospheric NO2 columns are retrieved using air mass factors derived from high-resolution radiative transfer calculations and a monthly climatology of NO2 profile shapes. We also present details of how uncertainties in the retrieved columns are estimated. The sensitivity of the retrieval to assumptions made in the stratosphere–troposphere separation is discussed and shown to be small, in an absolute sense, for most regions. We compare daily and monthly mean global OMI NO2 retrievals using the SP2 algorithm with those of the original Version 1 Standard Product (SP1) and the Dutch DOMINO product. The SP2 retrievals yield significantly smaller summertime tropospheric columns than SP1, particularly in polluted regions, and are more consistent with validation studies. SP2 retrievals are also relatively free of modeling artifacts and negative tropospheric NO2 values. In a reanalysis of an INTEX-B validation study, we show that SP2 largely eliminates an ~20% discrepancy that existed between OMI and independent in situ springtime NO2 SP1 measurements.

The TROPOspheric Monitoring Instrument (TROPOMI) is anticipated to provide high-quality and timely global atmospheric composition information through observations of atmospheric constituents such as ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, methane, formaldehyde and aerosol properties. The methane and the aerosol retrievals require very precise cloud clearing, which is difficult to achieve at the TROPOMI spatial resolution (7 by 7 km) and without thermal IR measurements. The TROPOMI carrier – the Sentinel 5 Precursor (S5P), does not include a cloud imager, thus it is planned to fly the S5P mission in a constellation with an instrument yielding an accurate cloud mask. The cloud imagery data will be provided by the US NPOESS Preparatory Project (NPP) mission, which will have the Visible Infrared Imager Radiometer Suite (VIIRS) on board (Scalione, 2004). This paper investigates the temporal co-registration requirements for suitable time differences between the VIIRS measurements of clouds and the TROPOMI methane and aerosol measurements, so that the former could be used for cloud clearing. The temporal co-registration is studied using Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data with 15 min temporal resolution (Veefkind, 2008b), and with data from the Geostationary Operational Environmental Satellite – 10 (GOES-10) having 1 min temporal resolution. The aim is to understand and assess the relation between the amount of allowed cloud contamination and the required time difference between the two satellites' overflights. Quantitative analysis shows that a time difference of approximately 5 min is sufficient (in most conditions) to use the cloud information from the first instrument for cloud clearing in the retrievals using data from the second instrument. In recent years the A-train constellation demonstrated the benefit of flying satellites in formation. Therefore this study's findings will be useful for designing future Low Earth Orbit (LEO) satellite constellations.

The cloud Optical Centroid Pressure (OCP) is a satellite-derived parameter that is commonly used in trace-gas retrievals to account for the effects of clouds on near-infrared through ultraviolet radiance measurements. Fast simulators are desirable to further expand the use of cloud OCP retrievals into the operational and climate communities for applications such as data assimilation and evaluation of cloud vertical structure in general circulation models. In this paper, we develop and validate fast simulators that provide estimates of the cloud OCP given a vertical profile of optical extinction. We use a pressure-weighting scheme where the weights depend upon optical parameters of clouds and/or aerosols. A cloud weighting function is easily extracted using this formulation. We then use fast simulators to compare two different satellite cloud OCP retrievals, from the Ozone Monitoring Instrument (OMI), with estimates based on collocated cloud extinction profiles from a combination of CloudSat radar and MODIS visible radiance data. These comparisons are made over a wide range of conditions to provide a comprehensive validation of the OMI cloud OCP retrievals. We find generally good agreement between OMI cloud OCPs and those predicted by CloudSat. However, the OMI cloud OCPs from the two independent algorithms agree better with each other than either does with the estimates from CloudSat/MODIS. Differences between OMI cloud OCPs and those based on CloudSat/MODIS may result from undetected snow/ice at the surface, cloud 3-D effects, cases of low clouds obscurred by ground-clutter in CloudSat observations and by opaque high clouds in CALIPSO lidar observations, and the fact that CloudSat/CALIPSO only observes a relatively small fraction of an OMI field-of-view.

We retrieve slant column NO2 from the super-zoom mode of the Ozone Monitoring Instrument (OMI) to explore its utility for understanding NOx emissions and variability. Slant column NO2 is operationally retrieved from OMI (Boersma et al., 2007; Bucsela et al., 2006) with a nadir footprint of 13 × 24 km2, the result of averaging eight detector elements on board the instrument. For 85 orbits in late 2004, OMI reported observations from individual "super-zoom" detector elements (spaced at 13 × 3 km2 at nadir). We assess the spatial response of these individual detector elements in-flight and determine an upper-bound on spatial resolution of 9 km, in good agreement with on-ground calibration (7 km FWHM). We determine the precision of the super-zoom mode to be 2.1 × 1015 molecules cm−2, approximately a factor of √8 lower than an identical retrieval at operational scale as expected if random noise dominates the uncertainty. We retrieve slant column NO2 over the Satpura power plant in India; Seoul, South Korea; Dubai, United Arab Emirates; and a set of large point sources on the Rihand Reservoir in India using differential optical absorption spectroscopy (DOAS). Over these sources, the super-zoom mode of OMI observes variation in slant column NO2 of up to 30 × the instrumental precision within one operational footprint.

We present an improved tropospheric nitrogen dioxide column retrieval algorithm (DOMINO v2.0) for OMI based on better air mass factors (AMFs) and a correction for across-track stripes resulting from calibration errors in the OMI backscattered reflectances. Since October 2004, NO2 retrievals from the Ozone Monitoring Instrument (OMI), a UV/Vis nadir spectrometer onboard NASA's EOS-Aura satellite, have been used with success in several scientific studies focusing on air quality monitoring, detection of trends, and NOx emission estimates. Dedicated evaluations of previous DOMINO tropospheric NO2 retrievals indicated their good quality, but also suggested that the tropospheric columns were susceptible to high biases (by 0–40%), probably because of errors in the air mass factor calculations. Here we update the DOMINO air mass factor approach. We calculate a new look-up table (LUT) for altitude-dependent AMFs based on more realistic atmospheric profile parameters, and include more surface albedo and surface pressure reference points than before. We improve the sampling of the TM4 model, resulting in a priori NO2 profiles that are better mixed throughout the boundary layer. We evaluate the NO2 profiles simulated with the improved TM4 sampling as used in the AMF calculations and show that they are highly consistent with in situ NO2 measurements from aircraft during the INTEX-A and INTEX-B campaigns in 2004 and 2006. Our air mass factor calculations are further updated by the implementation of a high-resolution terrain height and a high-resolution surface albedo climatology based on OMI measurements. Together with a correction for across-track stripes, the overall impact of the improved terrain height and albedo descriptions is modest (2 simulated aloft, where sensitivity to NO2 is higher, and amount to reductions in tropospheric NO2 columns of up to 20% in winter, and 10% in summer over extended polluted areas. We investigate the impact of aerosols on the NO2 retrieval, and based on a comparison of concurrent retrievals of clouds from OMI and aerosols from MODIS Aqua, we find empirical evidence that OMI cloud retrievals are sensitive to the presence of scattering aerosols. It follows that an implicit correction for the effects of aerosols occurs through the aerosol-induced cloud parameters in DOMINO, and we show that such an empirical correction amounts to a 20 %AMF reduction in summer and ±10% changes in winter over the eastern United States.