The European Space Agency (ESA) Sentinel-5 Precursor (S5P) is a low Earth orbit polar satellite carrying the single payload instrument TROPOspheric Monitoring Instrument (TROPOMI). Since its launch on 13 October 2017, the S5P mission has been acquiring almost 8 years of nadir ozone profile data, retrieved from the UV bands 1–2 measurements in the spectral range 270–330 nm. The retrieval algorithm of the ozone profile is strongly affected by systematic effects in the measured radiance, therefore absolute calibration of the input spectra is necessary to obtain good quality retrievals. In this study, we characterize the radiometric bias of the TROPOMI bands 1–2 measurements in comparison with simulations obtained with the Determining Instrument Specifications and Analysing Methods for Atmospheric Retrieval (DISAMAR) radiative transfer model. This comparison is the basis of the so-called “soft” calibration correction, an empirical correction applied at level-2 (L2), before the retrieval. The soft calibration correction reduces the reflectance fit residuals of 20 %–30 %, which improves the precision of the integrated total and tropospheric ozone columns of 10 %–15 %, as well as reducing the along-track orbit artifacts. The soft calibration correction spectra provide useful insights into the instrument radiometric calibration and can be used together with the in-flight calibration measurements to investigate and enhance the radiometric calibration, especially in band 1 where it shows a large spectral, radiance, across-track position and temporal dependence. From the comparison between on-ground and in-flight calibration measurements, some inconsistencies were found in the L1 calibration of the bands 1–2 which were traced to the straylight and the residual signal correction algorithms and are the subject of this study. Bands 1–2 measurements have been reprocessed with improved L1 correction algorithms to address the remaining uncorrected additive effects. The soft calibration correction spectra, derived from this reprocessed L1 data, are significantly reduced in magnitude (around 15 %–20 %, especially in band 1), and show less across-track position and spectral/temporal biases. Even if the soft calibration is still an essential pre-processing step for the ozone profile retrieval algorithm, the retrieval obtained with the updated version shows decreased dependence on the correction and an overall enhancement of the global retrieval convergence. The updates to the L1 and soft calibration are included in the ESA's official upgrade of the L1b processsor version 3.0 and ozone profile algorithm processor version 2.9.0, which will be also used for the second TROPOMI mission reprocessing.

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 50ĝ€¯% 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.

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.

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-torow variation in the SCDs of TROPOMI, the "stripe amplitude", is 2.15 μmol m, 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.

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.