Satellite observations of nitrogen dioxide (NO₂) provide valuable information on the location and strength of NO_x emissions, but spatial resolution is limited by horizontal transport and smearing of temporal averages due to changing wind fields. In this study, we map NO_x emissions on high spatial resolution from TROPOMI observations of NO₂ combined with wind fields based on the continuity equation. The divergence of horizontal fluxes proves to be highly sensitive for point sources like exhaust stacks. Thus, NO_x emissions from individual power plants can be resolved and quantified even on top of considerably high urban pollution from the Saudi Arabian capital city Riyadh. This allows us to catalog NO_x emissions from large point sources globally, as demonstrated for South Africa and Germany, with a detection limit of about 0.11 kg/s down to 0.03 kg/s for ideal conditions.

We present version 1.0 of a global catalog of NO_x emissions from point sources, derived from TROPOspheric Monitoring Instrument (TROPOMI) measurements of tropospheric NO₂ for 2018–2019. The identification of sources and quantification of emissions are based on the divergence (spatial derivative) of the mean horizontal flux, which is highly sensitive for point sources like power plant exhaust stacks. The catalog lists 451 locations which could be clearly identified as NO_x point sources by a fully automated algorithm, while ambiguous cases as well as area sources such as megacities are skipped. A total of 242 of these point sources could be automatically matched to power plants. Other NO_x point sources listed in the catalog are metal smelters, cement plants, or industrial areas. The four largest localized NO_x emitters are all coal combustion plants in South Africa. About $\mathrm{1}/\mathrm{4}$ of all detected point sources are located in the Indian subcontinent and are mostly associated with power plants. The catalog is incomplete, mainly due to persisting gaps in the TROPOMI NO₂ product at some coastlines, inaccurate or complex wind fields in coastal and mountainous regions, and high noise in the divergence maps for high background pollution. The derived emissions are generally too low, lacking a factor of about 2 up to 8 for extreme cases. This strong low bias results from combination of different effects, most of all a strong underestimation of near-surface NO₂ in TROPOMI NO₂ columns. Still, the catalog has high potential for checking and improving emission inventories, as it provides accurate and independent up-to-date information on the location of sources of NO_x and thus also CO₂. The catalog of NO_x emissions from point sources is freely available at https://doi.org/10.26050/WDCC/Quant_NOx_TROPOMI (Beirle et al., 2020).

Airborne imaging differential optical absorption spectroscopy (DOAS), ground-based stationary DOAS, and car DOAS measurements were conducted during the S5P-VAL-DE-Ruhr campaign in September 2020. The campaign area is located in the Rhine-Ruhr region of North Rhine-Westphalia, western Germany, which is a pollution hotspot in Europe comprising urban and large industrial sources. The DOAS measurements are used to validate spaceborne NO₂ tropospheric vertical column density (VCD) data products from the Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI). Seven flights were performed with the airborne imaging DOAS instrument for measurements of atmospheric pollution (AirMAP), providing measurements that were used to create continuous maps of NO₂ in the layer below the aircraft. These flights cover many S5P ground pixels within an area of 30 km × 35 km and were accompanied by ground-based stationary measurements and three mobile car DOAS instruments. Stationary measurements were conducted by two Pandora, two Zenith-DOAS, and two MAX-DOAS instruments. Ground-based stationary and car DOAS measurements are used to evaluate the AirMAP tropospheric NO₂ VCDs and show high Pearson correlation coefficients of 0.88 and 0.89 and slopes of 0.90 ± 0.09 and 0.89 ± 0.02 for the stationary and car DOAS, respectively. Having a spatial resolution of about 100 m × 30 m, the AirMAP tropospheric NO₂ VCD data create a link between the ground-based and the TROPOMI measurements with a nadir resolution of 3.5 km × 5.5 km and are therefore well suited to validate the TROPOMI tropospheric NO₂ VCD. The observations on the 7 flight days show strong NO₂ variability, which is dependent on the three target areas, the day of the week, and the meteorological conditions. The AirMAP campaign data set is compared to the TROPOMI NO₂ operational offline (OFFL) V01.03.02 data product, the reprocessed NO₂ data using the V02.03.01 of the official level-2 processor provided by the Product Algorithm Laboratory (PAL), and several scientific TROPOMI NO₂ data products. The AirMAP and TROPOMI OFFL V01.03.02 data are highly correlated (r=0.87) but show an underestimation of the TROPOMI data with a slope of 0.38 ± 0.02 and a median relative difference of −9 %. With the modifications in the NO₂ retrieval implemented in the PAL V02.03.01 product, the slope and median relative difference increased to 0.83 ± 0.06 and +20 %. However, the modifications resulted in larger scatter and the correlation decreased significantly to r=0.72. The results can be improved by not applying a cloud correction for the TROPOMI data in conditions with high aerosol load and when cloud pressures are retrieved close to the surface. The influence of spatially more highly resolved a priori NO₂ vertical profiles and surface reflectivity are investigated using scientific TROPOMI tropospheric NO₂ VCD data products. The comparison of the AirMAP campaign data set to the scientific data products shows that the choice of surface reflectivity database has a minor impact on the tropospheric NO₂ VCD retrieval in the campaign region and season. In comparison, the replacement of the a priori NO₂ profile in combination with the improvements in the retrieval of the PAL V02.03.01 product regarding cloud heights can further increase the tropospheric NO₂ VCDs. This study demonstrates that the underestimation of the TROPOMI tropospheric NO₂ VCD product with respect to the validation data set has been and can be further significantly improved.

We present an updated (v2) catalog of NO_x emissions from point sources as derived from TROPOspheric Monitoring Instrument (TROPOMI) measurements of NO₂ (Products Algorithm Laboratory (PAL) product) combined with wind fields from ERA5. Compared to version 1 of the catalog (Beirle et al., 2021), several improvements have been introduced to the algorithm. Most importantly, several corrections are applied, accounting for the effects of plume height on satellite sensitivity, 3D topographic effects, and the chemical loss of NO_x, resulting in considerably higher and more accurate NO_x emissions. In addition, error estimates are provided for each point source, taking into account the uncertainties of the individual retrieval steps. The v2 catalog is based on a fully automated iterative detection algorithm of point sources worldwide. It lists 1139 locations that have been found to be significant NO_x sources. The majority of these locations match power plants listed in the Global Power Plant Database (GPPD). Other NO_x point sources correspond to cement plants, metal smelters, industrial areas, or medium-sized cities. The emissions listed in v2 of the catalog show good agreement (within 20 % on average) to emissions reported by the German Environment Agency (Umweltbundesamt, UBA) as well as the United States Environmental Protection Agency (EPA). The data are publicly available at https://doi.org/10.26050/WDCC/No_xPointEmissionsV2 (Beirle et al., 2023).

Total column water vapour has been retrieved from TROPOMI measurements in the visible blue spectral range and compared to a variety of different reference data sets for clear-sky conditions during boreal summer and winter. The retrieval consists of the common two-step DOAS approach: first the spectral analysis is performed within a linearized scheme and then the retrieved slant column densities are converted to vertical columns using an iterative scheme for the water vapour a priori profile shape, which is based on an empirical parameterization of the water vapour scale height. Moreover, a modified albedo map was used combining the OMI LER albedo and scaled MODIS albedo map. The use of the alternative albedo is especially important over regions with very low albedo and high probability of clouds like the Amazon region. The errors of the total column water vapour (TCWV) retrieval have been theoretically estimated considering the contribution of a variety of different uncertainty sources. For observations during clear-sky conditions, over ocean surface, and at low solar zenith angles the error typically is around values of 10 %–20 %, and during cloudy-sky conditions, over land surface, and at high solar zenith angles it reaches values around 20 %–50 %. In the framework of a validation study the retrieval demonstrates that it can well capture the global water vapour distribution: the retrieved H₂O vertical column densities (VCDs) show very good agreement with the reference data sets over ocean for boreal summer and winter whereby the modified albedo map substantially improves the retrieval's consistency to the reference data sets, in particular over tropical land masses. However, over land the retrieval underestimates the VCD by about 10 %, particularly during summertime. Our investigations show that this underestimation is likely caused by uncertainties within the surface albedo and the cloud input data: low-level clouds cause an underestimation, but for mid- to high-level clouds good agreement is found. In addition, our investigations indicate that these biases can probably be further reduced by the use of improved cloud input data. For the general purpose we recommend only using VCDs with cloud fraction <20 % and AMF >0.1, which represents a good compromise between spatial coverage and retrieval accuracy. The TCWV retrieval can be easily applied to further satellite sensors (e.g. GOME-2 or OMI) for creating uniform, long-term measurement data sets, which is particularly interesting for climate and trend studies of water vapour.

In this study, we investigate trends in total column water vapour (TCWV) retrieved from measurements of the Ozone Monitoring Instrument (OMI) for the time range between January 2005 to December 2020. The trend analysis reveals, on global average, an annual increase in the TCWV amount of approximately +0.054 kg m⁻² yr⁻¹ or +0.21 % yr⁻¹. After the application of a Z test (to the significance level of 5 %) and a false discovery rate (FDR) test to the results of the trend analysis, mainly positive trends remain, in particular over the northern subtropics in the eastern Pacific. Combining the relative TCWV trends with trends in air temperature, we also analyse trends in relative humidity (RH) on the local scale. This analysis reveals that the assumption of temporally invariant RH is not always fulfilled, as we obtain increasing and decreasing RH trends over large areas of the ocean and land surface and also observe that these trends are not limited to arid and humid regions, respectively. For instance, we find decreasing RH trends over the (humid) tropical Pacific Ocean in the region of the Intertropical Convergence Zone. Interestingly, these decreasing RH trends in the tropical Pacific Ocean coincide well with decreasing trends in precipitation. Moreover, by combining the trends of TCWV, surface temperature, and precipitation, we derive trends for the global water vapour turnover time (TUT) of approximately +0.02 d yr⁻¹. Also, we obtain a TUT rate of change of around 8.4 % K⁻¹, which is 2 to 3 times higher than the values obtained in previous studies.