Comment on egusphere-2022-774

Abstract. The vertical distribution of ozone plays an important role in atmospheric chemistry, climate change, air pollution, and human health. Over the 21st century, spaceborne remote-sensing methods and instrumentation have evolved to better determine this distribution. We quantify the ability of ozone retrievals to characterize this distribution through a sequential combination of thermal infrared (TIR) and ultraviolet (UV) spectral radiances, harnessing co-located TIR measurements from the Cross-track Infrared Sounder (CrIS) on board the Suomi National Polar-orbiting Partnership (NPP) and UV measurements from the TROPOspheric Monitoring Instrument (TROPOMI), which is on the Sentinel 5-Precursor (S5P) satellite. Using the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES) algorithm, the sequential combination of TIR and UV measurements, which follows retrievals from each instrument separately, moderately improves the ability of satellites to characterize global ozone profiles over the use of each instrument/band individually. The CrIS retrievals enhanced by TROPOMI radiances in the Huggins band (325–335 nm) show good agreement with independent datasets both in the troposphere and in the stratosphere in spite of calibration issues in the TROPOMI UV. Improved performance is characterized in the stratosphere from CrIS-TROPOMI, firstly through a modest increase in the degrees of freedom for signal (DFS; often between 0.1–0.2) and secondly through comparisons with the Microwave Limb Sounder (MLS), where a global multi-month-long comparison shows a mean difference ∼×10 lower than either CrIS or TROPOMI individually and R2 values 3 % higher. In the troposphere, CrIS-TROPOMI and CrIS show similar degrees of freedom for signal, with about 2 globally, but these are higher in the tropics partitioned equally between the lower and upper troposphere. CrIS-TROPOMI validation with ozonesondes shows improved performance over CrIS-only validation, with a difference in the tropospheric-column bias of between 30 % and 200 % depending on the season. Cross-comparisons with satellite instruments and reanalysis datasets show similar performances in terms of correlations and biases. These results demonstrate that CrIS and CrIS-TROPOMI retrievals have the potential to improve global satellite ozone retrievals, especially with future developments. If spectral accuracy is improved in future TROPOMI calibration, the degrees of freedom for signal in the stratosphere could double when using bands 1 and 2 of TROPOMI (270–330 nm), while tropospheric degrees of freedom for signal could increase by 25 %.

<strong class="journal-contentHeaderColor">Abstract.</strong> The Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel 5 Precursor (S5P) satellite, launched in October 2017, provides a wealth of atmospheric composition data, including total columns of carbon monoxide (TCCO) at high horizontal resolution (5.5 km <span class="inline-formula">×</span> 7 km). Near-real-time TROPOMI TCCO data have been monitored in the global data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS) since November 2018 to assess the quality of the data. The CAMS system already routinely assimilates TCCO data from the Measurement of Pollution in the Troposphere (MOPITT) instrument and the Infrared Atmospheric Sounding Interferometer (IASI) outside the polar regions. The assimilation of TROPOMI TCCO data in the CAMS system was tested for the period 6 July to 31 December 2021, i.e. after the TROPOMI algorithm update to version 02.02.00 in July 2021. By assimilating TROPOMI TCCO observations, the CAMS CO columns increase by on average 8 %, resulting in an improved fit to independent observations (IAGOS aircraft profiles and NDACC Fourier transform infrared (FTIR) tropospheric and total-column CO data) compared to a version of the CAMS system where only TCCO from MOPITT and IASI is assimilated. The largest absolute and relative changes from the assimilation of TROPOMI CO are found in the lower and middle troposphere, i.e. that part of the atmosphere that is not already well constrained by the assimilated TIR MOPITT and IASI data. The largest impact near the surface comes from clear-sky TROPOMI data over land, and additional vertical information comes from the retrievals of measurements in cloudy conditions. July and August 2021 saw record numbers of boreal wildfires over North America and Russia, leading to large amounts of CO being released into the atmosphere. The paper assesses the impact of TROPOMI CO assimilation on selected CO plumes more closely. While the CO column can be well constrained by the assimilation of TROPOMI CO data, and the fit to individual IAGOS CO profiles in the lower and middle troposphere is considerably improved, the TROPOMI CO columns do not provide further constraints on individual plumes that are transported across continents and oceans at altitudes above 500 hPa.

<strong class="journal-contentHeaderColor">Abstract.</strong> The Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel 5 Precursor (S5P) satellite, launched in October 2017, provides a wealth of atmospheric composition data, including total columns of carbon monoxide (TCCO) at high horizontal resolution (5.5 km <span class="inline-formula">×</span> 7 km). Near-real-time TROPOMI TCCO data have been monitored in the global data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS) since November 2018 to assess the quality of the data. The CAMS system already routinely assimilates TCCO data from the Measurement of Pollution in the Troposphere (MOPITT) instrument and the Infrared Atmospheric Sounding Interferometer (IASI) outside the polar regions. The assimilation of TROPOMI TCCO data in the CAMS system was tested for the period 6 July to 31 December 2021, i.e. after the TROPOMI algorithm update to version 02.02.00 in July 2021. By assimilating TROPOMI TCCO observations, the CAMS CO columns increase by on average 8 %, resulting in an improved fit to independent observations (IAGOS aircraft profiles and NDACC Fourier transform infrared (FTIR) tropospheric and total-column CO data) compared to a version of the CAMS system where only TCCO from MOPITT and IASI is assimilated. The largest absolute and relative changes from the assimilation of TROPOMI CO are found in the lower and middle troposphere, i.e. that part of the atmosphere that is not already well constrained by the assimilated TIR MOPITT and IASI data. The largest impact near the surface comes from clear-sky TROPOMI data over land, and additional vertical information comes from the retrievals of measurements in cloudy conditions. July and August 2021 saw record numbers of boreal wildfires over North America and Russia, leading to large amounts of CO being released into the atmosphere. The paper assesses the impact of TROPOMI CO assimilation on selected CO plumes more closely. While the CO column can be well constrained by the assimilation of TROPOMI CO data, and the fit to individual IAGOS CO profiles in the lower and middle troposphere is considerably improved, the TROPOMI CO columns do not provide further constraints on individual plumes that are transported across continents and oceans at altitudes above 500 hPa.

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017 on board the Sentinel-5 Precursor (S5P) satellite, monitors the composition of the Earth's atmosphere at an unprecedented horizontal resolution as fine as 3.5 × 5.5 km2. This paper assesses the performances of the TROPOMI formaldehyde (HCHO) operational product compared to its predecessor, the OMI (Ozone Monitoring Instrument) HCHO QA4ECV product, at different spatial and temporal scales. The parallel development of the two algorithms favoured the consistency of the products, which facilitates the production of long-term combined time series. The main difference between the two satellite products is related to the use of different cloud algorithms, leading to a positive bias of OMI compared to TROPOMI of up to 30 % in tropical regions. We show that after switching off the explicit correction for cloud effects, the two datasets come into an excellent agreement. For medium to large HCHO vertical columns (larger than 5 × 1015 molec. cm−2) the median bias between OMI and TROPOMI HCHO columns is not larger than 10 % (&lt; 0.4 × 1015 molec. cm−2). For lower columns, OMI observations present a remaining positive bias of about 20 % (&lt; 0.8 × 1015 molec. cm−2) compared to TROPOMI in midlatitude regions. Here, we also use a global network of 18 MAX-DOAS (multi-axis differential optical absorption spectroscopy) instruments to validate both satellite sensors for a large range of HCHO columns. This work complements the study by Vigouroux et al. (2020), where a global FTIR (Fourier transform infrared) network is used to validate the TROPOMI HCHO operational product. Consistent with the FTIR validation study, we find that for elevated HCHO columns, TROPOMI data are systematically low (−25 % for HCHO columns larger than 8 × 1015 molec. cm−2), while no significant bias is found for medium-range column values. We further show that OMI and TROPOMI data present equivalent biases for large HCHO levels. However, TROPOMI significantly improves the precision of the HCHO observations at short temporal scales and for low HCHO columns. We show that compared to OMI, the precision of the TROPOMI HCHO columns is improved by 25 % for individual pixels and by up to a factor of 3 when considering daily averages in 20 km radius circles. The validation precision obtained with daily TROPOMI observations is comparable to the one obtained with monthly OMI observations. To illustrate the improved performances of TROPOMI in capturing weak HCHO signals, we present clear detection of HCHO column enhancements related to shipping emissions in the Indian Ocean. This is achieved by averaging data over a much shorter period (3 months) than required with previous sensors (5 years) and opens new perspectives to study shipping emissions of VOCs (volatile organic compounds) and related atmospheric chemical interactions.

Abstract. 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.

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) is the only payload of the Sentinel-5 Precursor (S5P), which is a polar-orbiting satellite mission of the European Space Agency (ESA). TROPOMI is a nadir-viewing spectrometer measuring in the ultraviolet, visible, near-infrared, and the shortwave infrared that provides near-global daily coverage. Among other things, TROPOMI measurements will be used for calculating the UV radiation reaching the Earth's surface. Thus, the TROPOMI surface UV product will contribute to the monitoring of UV radiation by providing daily information on the prevailing UV conditions over the globe. The TROPOMI UV algorithm builds on the heritage of the Ozone Monitoring Instrument (OMI) and the Satellite Application Facility for Atmospheric Composition and UV Radiation (AC SAF) algorithms. This paper provides a description of the algorithm that will be used for estimating surface UV radiation from TROPOMI observations. The TROPOMI surface UV product includes the following UV quantities: the UV irradiance at 305, 310, 324, and 380 nm; the erythemally weighted UV; and the vitamin-D weighted UV. Each of these are available as (i) daily dose or daily accumulated irradiance, (ii) overpass dose rate or irradiance, and (iii) local noon dose rate or irradiance. In addition, all quantities are available corresponding to actual cloud conditions and as clear-sky values, which otherwise correspond to the same conditions but assume a cloud-free atmosphere. This yields 36 UV parameters altogether. The TROPOMI UV algorithm has been tested using input based on OMI and the Global Ozone Monitoring Experiment-2 (GOME-2) satellite measurements. These preliminary results indicate that the algorithm is functioning according to expectations.

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 version v1.2-v1.3 data, however, have revealed that TROPOMI's tropospheric vertical columns (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: (1) The NO2-v2.2 data is based on version 2 level-1B (ir)radiance spectra with improved calibration, which results in a small and fairly homogeneous increase of the NO2 slant columns of 3 to 4 %, most of which ends up as a small increase of 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 lead 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 of 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 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 complicated, in particular for the tropospheric and total columns.

Satellite observations of total column methane atmospheric mixing ratios (XCH4) combined with atmospheric transport inverse modeling offer enhanced capabilities to monitor the methane (CH4) emissions at regional scale.The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite provides XCH4 with global daily coverage and a relatively high (5.5&amp;#215;7 km&amp;#178;) horizontal resolution since 2017. Widely used for the hotspot detection and quantification, TROPOMI-CH4 data is also exploited in regional and global CH4 flux inversions.Three retrieval products of XCH4are produced&amp;#160;and routinely updated from TROPOMI: the SRON official product, the WFMD product by the University of Bremen and the BLENDED product by the University of Harvard. The official dataset (v2.04) relies on the RemoTeC full-physics algorithm, which retrieves atmospheric methane concentration alongside atmospheric scattering properties. The WFMD scientific product is based on the University of Bremen&amp;#8217;s Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) and a machine learning classifier for quality filtering. The BLENDED product combines S5P-TROPOMI and GOSAT-TANSO retrievals. It is a post-processed of the official TROPOMI product, correcting biases using a machine learning model trained on collocated observations from both instruments. Despite recent advances in retrieval techniques, inter-product comparisons reveal notable differences in quality filtering, observed XCH&amp;#8324; values, and associated uncertainties. It leads to discrepancies in flux estimates derived from inversions, particularly at local and country scales.We assimilate these three TROPOMI XCH4products into regional atmospheric inversions of CH&amp;#8324; emissions over Europe at a 0.5&amp;#176;&amp;#215;0.5&amp;#176; resolution, for the year 2019. The inversions are conducted using the CHIMERE transport model within&amp;#160;the inverse modeling platform Community Inversion Framework (CIF). In situ surface measurements are used for&amp;#160;validation. We investigate the primary factors contributing to the inter-product differences, including albedo, aerosols and striping patterns. We also perform Observing System Simulation Experiments (OSSE) with synthetic pseudo-observations and perturbed prior fluxes to assess the sensitivity of the system to observations and isolate the causes of the differences in inversion results. We inquire into the impact of observation density, retrieval errors and inter-product biases on the posterior fluxes. The resulting methane emissions budgets are compared at pixel, country and regional scales, providing insights into the consistency of TROPOMI-based regional inversions.

&amp;lt;p&amp;gt;Along with monitoring of seismic activity and ground deformation, the measurement of volcanic gas emissions and composition plays a key role in the surveillance of active volcanoes and the mitigation of volcanic hazards. Volcanic gas emissions also potentially impact the environment, human health and climate, providing further motivation for study. Currently, volcano observatories typically employ ground-based or airborne techniques to monitor volcanic gas emissions, mainly sulfur dioxide (SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;) fluxes and its ratios over other species (e.g., CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, H&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;S). However, in recent years there have been significant breakthroughs in satellite observations of passive volcanic SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; emissions, including high-resolution ultraviolet (UV) measurements from the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, and the development of long-term records of volcanic SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; degassing from the Ozone Monitoring Instrument (OMI) on NASA&amp;amp;#8217;s Aura satellite. Satellite measurements offer some advantages over traditional gas monitoring techniques, e.g., synoptic coverage of large regions, relative immunity to variations in wind direction, and ability to map the spatial extent and dispersion of volcanic SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; plumes with applications for health hazard mitigation. Although these satellite datasets are potentially valuable for active volcano monitoring and as a supplement to other gas monitoring techniques, significant barriers remain to their use at many volcano observatories, particularly in low-income countries. Notably, the increasing volume of satellite datasets (NASA&amp;amp;#8217;s database is bigger than 3 petabytes) and the demands of data processing represent challenges to their operational use at observatories with limited internet connectivity or computational capacity. Here, we present an ongoing effort to develop open-source Python software to access and process SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; data directly through NASA&amp;amp;#8217;s Earthdata portal Application Processing Interface (API), in order to streamline the satellite SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; data processing workflow for a volcano observatory. By allowing server-side satellite data subsetting around the volcano of interest, this API greatly reduces the processing burden and only requires an internet connection to the NASA server hosting the required datasets (including S5P/TROPOMI, Aura/OMI and many others). We present some examples of software output and potential applications. Our current goal is to deploy and test the software for operational use in a volcano observatory.&amp;amp;#160;&amp;amp;#160;&amp;lt;/p&amp;gt;

Abstract. Shipping is an important source of nitrogen oxide (NOx) emissions worldwide, contributing to air pollution and negatively affecting marine environments, ecosystems, and biodiversity. TROPOMI (TROPOspheric Monitoring Instrument) on board the Sentinel-5 Precursor (S5P) has significantly enhanced the ability to detect ship emissions from space due to its low measurement noise levels and high spatial resolution of 5.5×3.5 km2 at nadir. This study uses the TROPOMI tropospheric NO2 slant column density (tSCD) to identify global shipping routes qualitatively. Preprocessing techniques, including iterative high-pass and Fourier filtering, markedly improve the detection of shipping lanes, revealing previously undetectable routes. Our analysis examines the impact of high-pass-filter box sizes, demonstrating that smaller sizes enhance the visibility of narrow shipping features, whereas larger box sizes increase overall NO2 signals. Additionally, we investigate various flagging criteria that affect NO2 signal distribution, highlighting the critical importance of careful selection for accurate emission monitoring. Filtered TROPOMI NO2 tSCDs over oceans show a strong correlation with shipping activities, as confirmed by comparison with the CAMS-GLOB-SHIP (Copernicus Atmospheric Monitoring Service for Global Shipping) inventory, and also reveal unknown shipping routes in regions such as the Bering Sea. Furthermore, TROPOMI effectively captures NO2 emissions from offshore oil and gas platforms, with NO2 hotspots in the TROPOMI data aligning well with locations of offshore installations listed in the OSPAR (Oslo and Paris Commission) and BOEM (Bureau of Ocean Energy Management) inventories. Lastly, the filtered TROPOMI NO2 tropospheric vertical column densities (tVCDs) are compared with the high-pass-filtered NO2 tVCDs from the CAMS (Copernicus Atmospheric Monitoring Service) model, which has a coarse spatial resolution of 0.4°. While both datasets effectively identify global shipping lanes, the high-pass-filtered CAMS NO2 tVCDs are significantly higher than the filtered TROPOMI NO2 tVCDs in the North Atlantic and strongly depend on the masking threshold in the high-pass filtering method in the South Atlantic Ocean.

Abstract. We present a new tropospheric ozone dataset based on TROPOspheric Monitoring Instrument (TROPOMI)/Sentinel-5 Precursor (S5P) total ozone measurements combined with stratospheric ozone data from the Belgian Assimilation System for Chemical ObsErvations (BASCOE) constrained by assimilating ozone observations from the Microwave Limb Sounder (MLS). The BASCOE stratospheric data are interpolated to the S5P observations and subtracted from the TROPOMI total ozone data. The difference is equal to the tropospheric ozone residual column from the surface up to the tropopause. The tropospheric ozone columns are retrieved at the full spatial resolution of the TROPOMI sensor (5.5×3.5 km2) with daily global coverage. Compared to the Ozone Mapping and Profiler Suite Modern-Era Retrospective analysis for Research and Applications 2 (OMPS-MERRA-2) data, a global mean positive bias of 3.3 DU is found for the analysed period April 2018 to June 2020. A small negative bias of about −0.91 DU is observed in the tropics relative to the operational TROPOMI tropical tropospheric data based on the convective cloud differential (CCD) algorithm throughout the same period. The new tropospheric ozone data (S5P-BASCOE) are compared to a set of globally distributed ozonesonde data integrated up to the tropopause level. We found 2254 comparisons with cloud-free TROPOMI observations within 25 km of the stations. In the global mean, S5P-BASCOE deviates by 2.6 DU from the integrated ozonesondes. Depending on the latitude the S5P-BASCOE deviate from the sondes and between −4.8 and 7.9 DU, indicating a good agreement. However, some exceptional larger positive deviations up to 12 DU are found, especially in the northern polar regions (north of 70∘). The monthly mean tropospheric column and time series for selected areas showed the expected spatial and temporal pattern, such as the wave one structure in the tropics or the seasonal cycle, including a summer maximum, in the mid-latitudes.

Abstract. The Airborne ROmanian Measurements of Aerosols and Trace gases (AROMAT) campaigns took place in Romania in September 2014 and August 2015. They focused on two sites: the Bucharest urban area and large power plants in the Jiu Valley. The main objectives of the campaigns were to test recently developed airborne observation systems dedicated to air quality studies and to verify their applicability for the validation of space-borne atmospheric missions such as the TROPOspheric Monitoring Instrument (TROPOMI)/Sentinel-5 Precursor (S5P). We present the AROMAT campaigns from the perspective of findings related to the validation of tropospheric NO2, SO2, and H2CO. We also quantify the emissions of NOx and SO2 at both measurement sites. We show that tropospheric NO2 vertical column density (VCD) measurements using airborne mapping instruments are well suited for satellite validation in principle. The signal-to-noise ratio of the airborne NO2 measurements is an order of magnitude higher than its space-borne counterpart when the airborne measurements are averaged at the TROPOMI pixel scale. However, we show that the temporal variation of the NO2 VCDs during a flight might be a significant source of comparison error. Considering the random error of the TROPOMI tropospheric NO2 VCD (σ), the dynamic range of the NO2 VCDs field extends from detection limit up to 37 σ (2.6×1016 molec. cm−2) and 29 σ (2×1016 molec. cm−2) for Bucharest and the Jiu Valley, respectively. For both areas, we simulate validation exercises applied to the TROPOMI tropospheric NO2 product. These simulations indicate that a comparison error budget closely matching the TROPOMI optimal target accuracy of 25 % can be obtained by adding NO2 and aerosol profile information to the airborne mapping observations, which constrains the investigated accuracy to within 28 %. In addition to NO2, our study also addresses the measurements of SO2 emissions from power plants in the Jiu Valley and an urban hotspot of H2CO in the centre of Bucharest. For these two species, we conclude that the best validation strategy would consist of deploying ground-based measurement systems at well-identified locations.

Abstract. Nitrous acid (HONO) is a key atmospheric component, acting as a major source of the hydroxyl radical (OH), the primary oxidant in the Earth's atmosphere. However, understanding its spatial and temporal variability remains a significant challenge. Recent TROPOspheric Monitoring Instrument (TROPOMI)/Sentinel-5 Precursor (S5P) ultraviolet–visible (UV–Vis) measurements of fresh fire plumes shed light on the impact of global pyrogenic HONO emissions. Here, we leverage Infrared Atmospheric Sounding Interferometer (IASI)/MetOp's global infrared satellite measurements, complementing midday TROPOMI observations with morning and evening overpasses, to detect and retrieve pyrogenic HONO in 2007–2023. Employing a sensitive detection method, we identify HONO enhancements within concentrated fire plumes worldwide. Most detections are in the Northern Hemisphere (NH) mid- and high latitudes, where intense wildfires and high injection heights favour HONO detection. IASI's nighttime measurements yield 10-fold more HONO detections than daytime measurements, emphasizing HONO's extended lifetime in the absence of photolysis during the night. The annual detection count increases by at least 3–4 times throughout the IASI time series, mirroring the recent surge in intense wildfires at these latitudes. Additionally, we employ a neural-network-based algorithm for retrieving pyrogenic HONO total columns from IASI and compare them with TROPOMI in the same fire plumes. The results demonstrate TROPOMI's efficacy in capturing HONO enhancements in smaller fire plumes and in proximity to fire sources, while IASI's morning and evening overpasses enable HONO measurements further downwind, highlighting the survival of HONO or its secondary formation along long-range transport in smoke plumes.

Solar-Induced chlorophyll Fluorescence (SIF) retrieved from spaceborne hyperspectral measurements can provide a synoptic perspective on the functioning of photosynthetic active vegetation and algae. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 precursor mission provides data for the retrieval of SIF with an unprecedented resolution in the spatial and temporal dimensions. In this study, we propose an approach for the retrieval of the red SIF peak value (at around 685 nm) over both land and ocean from TROPOMI measurements. This approach exploits the solar Fraunhofer lines located in the spectral window of 669–686 nm to disentangle SIF from the solar radiation reflected by the surface–atmosphere system. First, through simulated TROPOMI-like datasets, we determine the proper parameter settings and demonstrate the feasibility of the approach for the red SIF retrieval from TROPOMI data. Then, the approach is applied to real TROPOMI measurements. The red SIF retrieved with the proposed approach for two months’ TROPOMI data is compared with another TROPOMI red SIF dataset and the marine normalized fluorescence line height (nFLH) product of the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS). The comparisons display similar spatial distributions over both land and ocean for the weekly global composites. Especially, the comparison of the two red SIF datasets demonstrates good agreement overall, indicating consistency of the two retrieval approaches. The retrieval uncertainty for the weekly global composite is about 13% and 10% of the peak SIF value over land/ocean, respectively, which can be considered as satisfactory error thresholds for global SIF observations.

Abstract. On 13 October 2017, the Tropospheric Monitoring Instrument (TROPOMI) was launched on the Copernicus Sentinel-5 Precursor satellite in a sun-synchronous orbit. One of the mission's operational data products is the total column concentration of carbon monoxide (CO), which was released to the public in July 2018. The current TROPOMI CO processing uses the HITRAN 2008 spectroscopic data with updated water vapor spectroscopy and produces a CO data product compliant with the mission requirement of 10 % precision and 15 % accuracy for single soundings. Comparison with ground-based CO observations of the Total Carbon Column Observing Network (TCCON) show systematic differences of about 6.2 ppb and single-orbit observations are superimposed by a significant striping pattern along the flight path exceeding 5 ppb. In this study, we discuss possible improvements of the CO data product. We found that the molecular spectroscopic data used in the retrieval plays a key role for the data quality where the use of the Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases (SEOM-IAS) and the HITRAN 2012 and 2016 releases reduce the bias between TROPOMI and TCCON due to improved CH4 spectroscopy. SEOM-IAS achieves the best spectral fit quality (root-mean-square, rms, differences between the simulated and measured spectrum) of 1.5×10-10 mol s−1 m−2 nm−1 sr−1 and reduces the bias between TROPOMI and TCCON to 3.4 ppb, while HITRAN 2012 and HITRAN 2016 decrease the bias even further below 1 ppb. HITRAN 2012 shows the worst fit quality (rms = 2.5×10-10 mol s−1 m−2 nm−1 sr−1) of the tested cross sections and furthermore introduces an artificial bias of about -1.5×1017 molec cm−2 between TROPOMI CO and the CAMS-IFS model in the Tropics caused by the H2O spectroscopic data. Moreover, analyzing 1 year of TROPOMI CO observations, we identified increased striping patterns by about 16 % percent from November 2017 to November 2018. For that, we defined a measure γ, quantifying the relative pixel-to-pixel variation in CO in the cross-track and along-track directions. To mitigate this effect, we discuss two destriping methods applied to the CO data a posteriori. A destriping mask calculated per orbit by median filtering of the data in the cross-track direction significantly reduced the stripe pattern from γ=2.1 to γ=1.6. However, the destriping can be further improved, achieving γ=1.2 by deploying a Fourier analysis and filtering of the data, which not only corrects for stripe patterns in the cross-track direction but also accounts for the variability of stripes along the flight path.