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Venezuela has long been identified as an area with large methane emissions and intensive oil exploitation, especially in the Lake Maracaibo region, but production has strongly decreased in recent years. The area is notoriously difficult to observe from space due to its complex topography and persistent cloud cover. We use the unprecedented coverage of the TROPOspheric Monitoring Instrument (TROPOMI) methane observations in analytical inversions with the Integrated Methane Inversion (IMI) framework at the national scale and at the local scale with the Weather Research and Forecasting model with chemistry (WRF-Chem). In the IMI analysis, we find Venezuelan emissions of 7.5 (5.7–9.3) Tg a−1 in 2019, where about half of emissions can be informed by TROPOMI observations, and emissions from oil exploitation are a factor of ∼ 1.6 higher than in bottom-up inventories. Using WRF, we find emissions of 1.2 (1.0–1.5) Tg a−1 from the Lake Maracaibo area in 2019, close to bottom-up estimates. Our WRF estimate is ∼ 40 % lower than the result over the same region from the IMI due to differences in the meteorology used by the two models. We find only a small, non-significant trend in emissions between 2018 and 2020 around the lake, implying the area's methane emission intensity expressed against oil and gas production has doubled over the time period, to ∼ 20 %. This value is much higher than what has previously been found for other oil and gas production regions and indicates that there could be large emissions from abandoned infrastructure.

Abstract Various fire emission estimates for southern Africa during 2019, derived with multiple burned area data sets with resolutions ranging from 500 to 20 m, are evaluated using satellite carbon monoxide (CO) observations. Southern African emissions derived from burned area generated by 20 m Sentinel‐2 satellite imagery are up to 120% higher than other estimates because small fires are better detected with a higher‐resolution satellite instrument. A comprehensive comparison between simulated and observed atmospheric CO indicates that the Sentinel‐2 burned area data significantly improves emission estimates, with up to 15% reduction in CO concentration biases in comparison to emissions based on coarser resolution burned area data. We also found that the temporal lag between emissions and atmospheric CO concentrations during the peak fire month was related to atmospheric transport. These findings emphasize the importance of utilizing higher‐resolution satellite instruments in accurately estimating emissions and understanding the impact of small fires on global climate.

A reduction in anthropogenic methane emissions is vital to limit near-term global warming. A small number of so-called super-emitters is responsible for a disproportionally large fraction of total methane emissions. Since late 2017, the TROPOspheric Monitoring Instrument (TROPOMI) has been in orbit, providing daily global coverage of methane mixing ratios at a resolution of up to 7×5.5 km2, enabling the detection of these super-emitters. However, TROPOMI produces millions of observations each day, which together with the complexity of the methane data, makes manual inspection infeasible. We have therefore designed a two-step machine learning approach using a convolutional neural network to detect plume-like structures in the methane data and subsequently apply a support vector classifier to distinguish the emission plumes from retrieval artifacts. The models are trained on pre-2021 data and subsequently applied to all 2021 observations. We detect 2974 plumes in 2021, with a mean estimated source rate of 44 t h−1 and 5–95th percentile range of 8–122 t h−1. These emissions originate from 94 persistent emission clusters and hundreds of transient sources. Based on bottom-up emission inventories, we find that most detected plumes are related to urban areas and/or landfills (35 %), followed by plumes from gas infrastructure (24 %), oil infrastructure (21 %), and coal mines (20 %). For 12 (clusters of) TROPOMI detections, we tip and cue the targeted observations and analysis of high-resolution satellite instruments to identify the exact sources responsible for these plumes. Using high-resolution observations from GHGSat, PRISMA, and Sentinel-2, we detect and analyze both persistent and transient facility-level emissions underlying the TROPOMI detections. We find emissions from landfills and fossil fuel exploitation facilities, and for the latter, we find up to 10 facilities contributing to one TROPOMI detection. Our automated TROPOMI-based monitoring system in combination with high-resolution satellite data allows for the detection, precise identification, and monitoring of these methane super-emitters, which is essential for mitigating their emissions.

This paper presents the automated plume detection and emission estimation algorithm (APE), developed to detect CO plumes from isolated biomass burning events and to quantify the corresponding CO emission rate. APE uses the CO product of the Tropospheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor (S5P) satellite, launched in 2017, and collocated active fire data from the Visible Infrared Imaging Radiometer Suite (VIIRS), the latter flying 3 min ahead of S5P. After identifying appropriate fire events using VIIRS data, an automated plume detection algorithm based on traditional image processing algorithms selects plumes for further data interpretation. The approach is based on thresholds optimized for data over the United States in September 2020. Subsequently, the CO emission rate is estimated using the cross-sectional flux method, which requires horizontal wind fields at the plume height. Three different plume heights were considered, and the ECMWF Reanalysis v5 (ERA5) data were used to compute emissions. A varying plume height in the downwind direction based on three-dimensional Lagrangian simulation was considered appropriate. APE is verified for observations over Australia and Siberia. For all fire sources identified by VIIRS, only 16 % of the data corresponded to clear-sky TROPOMI CO data with plume signature. Furthermore, the quality filters of APE resulted in emission estimations for 26 % of the TROPOMI CO data with plume signatures. Visual filtering of the APE's output showed a true-positive confidence level of 97.7 %. Finally, we provide an estimate of the emission uncertainties. The greatest contribution of error comes from the uncertainty in Global Fire Assimilation System (GFAS) injection height that leads to emission errors %, followed by systematic errors in the ERA5 wind data. The assumption of constant emission during plume formation and spatial under-sampling of CO column concentration by TROPOMI yields an error of %. The randomized errors from the ensemble ERA5 wind data are found to be less than 20 % for 97 % of the cases.

Carbon monoxide (CO) is an air pollutant that plays an important role in atmospheric chemistry and is mostly emitted by forest fires and incomplete combustion in, for example, road transport, residential heating, and industry. As CO is co-emitted with fossil fuel CO2 combustion emissions, it can be used as a proxy for CO2. Following the Paris Agreement, there is a need for independent verification of reported activity-based bottom-up CO2 emissions through atmospheric measurements. CO can be observed daily at a global scale with the TROPOspheric Monitoring Instrument (TROPOMI) satellite instrument with daily global coverage at a resolution down to 5.5 × 7 km2. To take advantage of this unique TROPOMI dataset, we develop a cross-sectional flux-based emission quantification method that can be applied to quantify emissions from a large number of cities, without relying on computationally expensive inversions. We focus on Africa as a region with quickly growing cities and large uncertainties in current emission estimates. We use a full year of high-resolution Weather Research and Forecasting (WRF) simulations over three cities to evaluate and optimize the performance of our cross-sectional flux emission quantification method and show its reliability down to emission rates of 0.1 Tg CO yr−1. Comparison of the TROPOMI-based emission estimates to the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) and Emissions Database for Global Atmospheric Research (EDGAR) bottom-up inventories shows that CO emission rates in northern Africa are underestimated in EDGAR, suggesting overestimated combustion efficiencies. We see the opposite when comparing TROPOMI to the DACCIWA inventory in South Africa and Côte d'Ivoire, where CO emission factors appear to be overestimated. Over Lagos and Kano (Nigeria) we find that potential errors in the spatial disaggregation of national emissions cause errors in DACCIWA and EDGAR respectively. Finally, we show that our computationally efficient quantification method combined with the daily TROPOMI observations can identify a weekend effect in the road-transport-dominated CO emissions from Cairo and Algiers.

We quantify weekly methane emissions at 0.25∘ × 0.3125∘ (≈25 × 25 km2) resolution from the Permian Basin, the largest oil production basin in the US, by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) from May 2018 to October 2020. The mean oil and gas emission from the region (± standard deviation of weekly estimates) was 3.7 ± 0.9 Tg a−1, higher than previous TROPOMI inversion estimates that may have used biased prior emissions or background assumptions. We find strong week-to-week variability in emissions superimposed on longer-term trends, and these are consistent with independent inferences of temporal emission variability from tower, aircraft, and multispectral satellite data. New well development and natural gas spot price were significant drivers of variability in emissions over our study period but the concurrent 50 % increase in oil and gas production was not. The methane intensity (methane emitted per unit of methane gas produced) averaged 4.6 % ± 1.3 % and steadily decreased from 5 %–6 % in 2018 to 3 %–4 % in 2020. While the decreasing trend suggests improvement in operator practices during the study period, methane emissions from the Permian Basin remained high, with methane intensity an order of magnitude above the industry target of <0.2 %. Our success in using TROPOMI satellite observations for weekly estimates of emissions from a major oil production basin shows promise for application to near-real-time monitoring in support of climate change mitigation efforts.

Since 2017 the Tropospheric Monitoring Instrument (TROPOMI) on board ESA's Copernicus Sentinel-5 satellite (S5-P) has provided the operational carbon monoxide (CO) data product with daily global coverage on a spatial resolution of 5.5×7 km2. The European Centre for Medium-Range Weather Forecasts (ECMWF) plans to assimilate the retrieved total columns and the corresponding vertical sensitivities in the Copernicus Atmosphere Monitoring Service Integrated Forecasting System (CAMS-IFS) to improve forecasts of the atmospheric chemical composition. The TROPOMI data will primarily constrain the vertical integrated CO field of CAMS-IFS but to a lesser extent also its vertical CO distribution. For clear-sky conditions, the vertical sensitivity of the TROPOMI CO data product is useful throughout the atmosphere, but for cloudy scenes it varies due to cloud shielding and light scattering. To assess the profile information, we deploy an a posteriori profile retrieval that combines an ensemble of TROPOMI CO column retrievals with different vertical sensitivities to obtain a vertical CO profile that is then a representative average for the chosen spatial and temporal domain. We demonstrate the approach on three CO pollution cases. For the so-called “Rabbit Foot Fire” in Idaho on 12 August 2018, we estimate a CO profile showing the pollution at an altitude of about 5 km in good agreement with airborne in situ measurements of the Biomass Burning Flux Measurements of Trace Gases and Aerosol (BB-FLUX) field campaign. The distinct CO enhancement in a plume aloft (length =212 km, width =34 km), decoupled from the ground, is sensed by TROPOMI but is not present in the CAMS-IFS model. For a large-scale event, we analyzed the CO pollution from Siberian wildfires that took place from 14 to 17 August 2018. The TROPOMI data estimate the height of the pollution plume over Canada at 7 km in agreement with CAMS-IFS. However, CAMS-IFS underestimates the enhanced CO vertical column densities sensed by TROPOMI within the plume by more than 100 ppb. Finally, we study the seasonal biomass burning in the Amazon. During the burning season the CO profile retrieved from the TROPOMI measurements (1–15 August 2019) agrees well with the one of CAMS-IFS with a similar vertical shape between ground and 14 km altitude. Hence, our results indicate that assimilating TROPOMI CO retrieval with different vertical sensitivities (e.g., under clear-sky and cloudy conditions) provides information about the vertical distribution of CO.

The Copernicus Atmosphere Monitoring Service (CAMS) has recently produced a greenhouse gas reanalysis (version egg4) that covers almost 2 decades from 2003 to 2020 and which will be extended in the future. This reanalysis dataset includes carbon dioxide (CO2) and methane (CH4). The reanalysis procedure combines model data with satellite data into a globally complete and consistent dataset using the European Centre for Medium-Range Weather Forecasts' Integrated Forecasting System (IFS). This dataset has been carefully evaluated against independent observations to ensure validity and to point out deficiencies to the user. The greenhouse gas reanalysis can be used to examine the impact of atmospheric greenhouse gas concentrations on climate change (such as global and regional climate radiative forcing), assess intercontinental transport, and serve as boundary conditions for regional simulations, among other applications and scientific uses. The caveats associated with changes in assimilated observations and fixed underlying emissions are highlighted, as is their impact on the estimation of trends and annual growth rates of these long-lived greenhouse gases.

Satellite remote sensing of methane (CH4) using the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Copernicus Sentinel-5 Precursor (S5-P) satellite is key to monitor and quantify emissions globally. Overall, the S5-P methane data are of satisfying quality, demonstrated by the product validation with ground-based measurements from the Total Carbon Column Observing Network (TCCON). However, analysis of TROPOMI dry-air column mixing ratio (XCH4) data has pointed to a few false methane anomalies up to 20–40 ppb that can potentially be misinterpreted as enhancements due to strong emission sources. These artefacts are caused by spectral features of the underlying surfaces of specific materials (e.g. carbonate rocks), which are not well represented in the forward model of the retrieval algorithm. In this study we show that the observed anomalies are due to the surface model which describes the spectral dependence of the Lambertian albedo by a second-order polynomial in wavelength. By analysing the ECOSTRESS library that contains laboratory spectra for different types of surfaces, we find that a quadratic function might not be the most optimal representation of the surface reflectance spectral dependencies in the short-wave infrared (SWIR) range. Already the use of a third-order polynomial improves the methane data such that the anomalies disappear at several locations (e.g. Siberia, Australia and Algeria) without affecting the data quality elsewhere, and the quality of the fit significantly improves. We also found that the known bias in retrieved methane for low-albedo scenes slightly improves, but still, a posterior correction needs to be applied, leaving open the question about the root cause of the albedo bias. After applying the adjusted surface model globally, we perform the routine validation with TCCON and Greenhouse gases Observing SATellite (GOSAT) data. GOSAT comparison does not significantly improve, while TCCON validation results show a small improvement in some stations of 2–4 ppb, up to a factor of 10 smaller than the artificial XCH4 enhancements. This reflects that TCCON stations are not close to any of the corrected artefacts, hinting at a limitation of the current validation approach of the S5-P XCH4 data product.

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 × 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.

The TROPOspheric Monitoring Instrument (TROPOMI), due to its wide swath, performs observations over the ocean in every orbit, enhancing the monitoring capabilities of methane from space. In the short-wave–infrared (SWIR) spectral band ocean surfaces are dark except for the specific sun glint geometry, for which the specular reflectance detected by the satellite provides a signal that is high enough to retrieve methane with high accuracy and precision. In this study, we build upon the RemoTeC full-physics retrieval algorithm for land measurements, and we retrieve 4 years of methane concentrations over the ocean from TROPOMI. We fully assess the quality of the dataset by performing a validation using ground-based measurements of the Total Carbon Column Observing Network (TCCON) from near-ocean sites. The validation results in an agreement of -0.5±0.3 % (-8.4±6.3 ppb) for the mean bias and station-to-station variability, which show that glint measurements comply with the mission requirement of precision and accuracy below 1 %. Comparison to ocean measurements from the Greenhouse gases Observing SATellite (GOSAT) results in a bias of -0.2±0.9 % (-4.4±15.7 ppb), equivalent to the comparison of measurements over land. The full-physics algorithm simultaneously retrieves the amount of atmospheric methane and the physical scattering properties of the atmosphere from measurements in the near-infrared (NIR) and SWIR spectral bands. Based on the scattering properties of the atmosphere and ocean surface reflection we further validate retrievals over the ocean. Using the “upper-edge” method, we identify a set of ocean glint observations where scattering by aerosols and clouds can be ignored in the measurement simulation to investigate other possible error sources such as instrumental errors, radiometric inaccuracies or uncertainties related to spectroscopic absorption cross-sections. With this ensemble we evaluate the RemoTeC forward model via the validation of the total atmospheric oxygen (O2) column retrieved from the O2 A-band, as well as the consistency of XCH4 retrievals using sub-bands from the SWIR band, which show a consistency within 1 %. We discard any instrumental and radiometric errors by a calibration of the O2 absorption line strengths as suggested in the literature.

AirCore in situ vertical profiles sample the atmosphere from near the surface to the lower stratosphere, making them ideal for the validation of satellite tropospheric trace gas data. Here we present intercomparison results of AirCore carbon monoxide (CO) measurements with respect to retrievals from MOPITT (Measurements of Pollution In The Troposphere; version 8) and TROPOMI (TROPOspheric Monitoring Instrument), on board the NASA Terra and ESA Sentinel 5-Precursor satellites, respectively. Mean MOPITT/AirCore total column bias values and their standard deviation (0.4 ± 5.5, 1.7 ± 5.6, and 0.7 ± 6.0 for MOPITT thermal-infrared, near-infrared, and multispectral retrievals, respectively; all in %) are similar to results obtained in MOPITT/NOAA aircraft flask data comparisons from this study and from previous validation efforts. MOPITT CO retrievals are systematically validated using in situ vertical profiles from a variety of aircraft campaigns. Because most aircraft vertical profiles do not sample the troposphere's entire vertical extent, they must be extended upwards in order to be usable in validation. Here we quantify, for the first time, the error introduced in MOPITT CO validation by the use of shorter aircraft vertical profiles extended upwards by analyzing validation results of MOPITT with respect to full and truncated AirCore CO vertical profiles. Our results indicate that the error is small, affects mostly upper tropospheric retrievals (at 300 hPa: ∼ 2.6, 0.8, and 3.2 percent points for MOPITT thermal-infrared, near-infrared, and multispectral, respectively), and may have resulted in the overestimation of MOPITT retrieval biases in that region. TROPOMI can retrieve CO under both clear and cloudy conditions. The latter is achieved by quantifying interfering trace gases and parameters describing the cloud contamination of the measurements together with the CO column; then, the reference CO profiles used in the retrieval are scaled based on estimated above-cloud CO rather than on estimated total CO. We use AirCore measurements as the reference to evaluate the error introduced by this approach in cloudy TROPOMI retrievals over land after accounting for TROPOMI's vertical sensitivity to CO (relative bias and its standard deviation = 2.02 % ± 11.13 %). We also quantify the null-space error, which accounts for differences between the shape of TROPOMI reference profiles and that of AirCore measured profiles (for TROPOMI cloudy enull=0.98 % ± 2.32 %).

The aim of this paper is to highlight how TROPOspheric Monitoring Instrument (TROPOMI) trace gas data can best be used and interpreted to understand event-based impacts on air quality from regional to city scales around the globe. For this study, we present the observed changes in the atmospheric column amounts of five trace gases (NO2, SO2, CO, HCHO, and CHOCHO) detected by the Sentinel-5P TROPOMI instrument and driven by reductions in anthropogenic emissions due to COVID-19 lockdown measures in 2020. We report clear COVID-19-related decreases in TROPOMI NO2 column amounts on all continents. For megacities, reductions in column amounts of tropospheric NO2 range between 14 % and 63 %. For China and India, supported by NO2 observations, where the primary source of anthropogenic SO2 is coal-fired power generation, we were able to detect sector-specific emission changes using the SO2 data. For HCHO and CHOCHO, we consistently observe anthropogenic changes in 2-week-averaged column amounts over China and India during the early phases of the lockdown periods. That these variations over such a short timescale are detectable from space is due to the high resolution and improved sensitivity of the TROPOMI instrument. For CO, we observe a small reduction over China, which is in concert with the other trace gas reductions observed during lockdown; however, large interannual differences prevent firm conclusions from being drawn. The joint analysis of COVID-19-lockdown-driven reductions in satellite-observed trace gas column amounts using the latest operational and scientific retrieval techniques for five species concomitantly is unprecedented. However, the meteorologically and seasonally driven variability of the five trace gases does not allow for drawing fully quantitative conclusions on the reduction in anthropogenic emissions based on TROPOMI observations alone. We anticipate that in future the combined use of inverse modeling techniques with the high spatial resolution data from S5P/TROPOMI for all observed trace gases presented here will yield a significantly improved sector-specific, space-based analysis of the impact of COVID-19 lockdown measures as compared to other existing satellite observations. Such analyses will further enhance the scientific impact and societal relevance of the TROPOMI mission.

The thermal infrared nadir spectra of IASI (Infrared Atmospheric Sounding Interferometer) are successfully used for retrievals of different atmospheric trace gas profiles. However, these retrievals offer generally reduced information about the lowermost tropospheric layer due to the lack of thermal contrast close to the surface. Spectra of scattered solar radiation observed in the near-infrared and/or shortwave infrared, for instance by TROPOMI (TROPOspheric Monitoring Instrument), offer higher sensitivity near the ground and are used for the retrieval of total-column-averaged mixing ratios of a variety of atmospheric trace gases. Here we present a method for the synergetic use of IASI profile and TROPOMI total-column level 2 retrieval products. Our method uses the output of the individual retrievals and consists of linear algebra a posteriori calculations (i.e. calculation after the individual retrievals). We show that this approach has strong theoretical similarities to applying the spectra of the different sensors together in a single retrieval procedure but with the substantial advantage of being applicable to data generated with different individual retrieval processors, of being very time efficient, and of directly benefiting from the high quality and most recent improvements of the individual retrieval processors. We demonstrate the method exemplarily for atmospheric methane (CH4). We perform a theoretical evaluation and show that the a posteriori combination method yields a total-column-averaged CH4 product (XCH4) that conserves the good sensitivity of the corresponding TROPOMI product while merging it with the high-quality upper troposphere–lower stratosphere (UTLS) CH4 partial-column information of the corresponding IASI product. As a consequence, the combined product offers additional sensitivity for the tropospheric CH4 partial column, which is not provided by the individual TROPOMI nor the individual IASI product. The theoretically predicted synergetic effect is verified by comparisons to CH4 reference data obtained from collocated XCH4 measurements at 14 globally distributed TCCON (Total Carbon Column Observing Network) stations, CH4 profile measurements made by 36 individual AirCore soundings, and tropospheric CH4 data derived from continuous ground-based in situ observations made at two nearby Global Atmospheric Watch (GAW) mountain stations. The comparisons clearly demonstrate that the combined product can reliably detect the actual variations of atmospheric XCH4, CH4 in the UTLS, and CH4 in the troposphere. A similar good reliability for the latter is not achievable by the individual TROPOMI and IASI products.

The Sentinel-5 Precursor (S5P) mission was launched on October 2017 and has since provided data with high spatio-temporal resolution using its remote sensing instrument, the TROPOspheric Monitoring Instrument (TROPOMI). The latter is a nadir viewing passive grating imaging spectrometer. The mathematical inversion of the TROPOMI data yields retrievals of different trace gas and aerosol data products. The column-averaged dry air mole fraction of methane (XCH4) is the product of interest to this study. The daily global coverage of the atmospheric methane mole fraction data enables the analysis of the methane distribution and variation on large scales and also to estimate surface emissions. The spatio-temporal high-resolution satellite data are potentially particularly valuable in remote regions, such as the Arctic, where few ground stations and in-situ measurements are available. In addition to the operational Copernicus S5P total-column averaged dry air mole fraction methane data product developed by SRON, the scientific TROPOMI/WFMD algorithm data product v1.5 (WFMD product) was generated at the Institute of Environmental Physics at the University of Bremen. In this study we focus on the assessment of both S5P XCH4 data products over Greenland and find that spatial maps of both products show distinct features along the coast lines. Anomalies up to and exceeding 100 ppb are observed and stand out in comparison to the otherwise smooth changes in the methane distribution. These features are more pronounced for the operational product compared to the WFMD product. The spatial patterns correlate with the difference between the GMTED2010 digital elevation model (DEM) used in the retrievals and a more recent topography data set indicating that inaccuracies in the assumed surface elevation are the origin of the observed features. These correlations are stronger for the WFMD product. In order to evaluate the impact of the topography dataset on the retrieval we reprocess the WFMD product with updated elevation data. We find a significant reduction of the localized features when GMTED2010 is replaced by recent topography data over Greenland based on ICESat-2 data. This study shows the importance of the chosen topography data on retrieved dry air mole fractions. The use of an accurate and as up-to-date as possible DEM is advised for all S5P data products as well as for future missions which rely on DEM as input data. A modification based on this study is planned to be introduced in the next version of the WFMD data product.

This paper presents an extended scientific HDO/H2O total column data product from short-wave infrared (SWIR) measurements by the Tropospheric Monitoring Instrument (TROPOMI) including clear-sky and cloudy scenes. The retrieval employs a forward model which accounts for scattering, and the algorithm infers the trace gas column information, surface properties, and effective cloud parameters from the observations. Compared to the previous clear-sky-only data product, coverage is greatly enhanced by including scenes over low clouds, particularly enabling data over oceans as the albedo of water in the SWIR spectral range is too low to retrieve under cloud-free conditions. The new dataset is validated against co-located ground-based Fourier transform infrared (FTIR) observations by the Total Carbon Column Observing Network (TCCON). The median bias for clear-sky scenes is 1.4×1021 molec cm−2 (2.9 %) in H2O columns and 1.1×1017 molec cm−2 (−0.3 %) in HDO columns, which corresponds to −17 ‰ (9.9 %) in a posteriori δD. The bias for cloudy scenes is 4.9×1021 molec cm−2 (11 %) in H2O, 1.1×1018 molec cm−2 (7.9 %) in HDO, and −20 ‰ (9.7 %) in a posteriori δD. At low-altitude stations, the bias is small at low and middle latitudes and has a larger value at high latitudes. At high-altitude stations, an altitude correction is required to compensate for different partial columns seen by the station and the satellite. The bias in a posteriori δD after altitude correction depends on sensitivity due to shielding by clouds and on realistic a priori profile shapes for both isotopologues. Cloudy scenes generally involve low sensitivity below the clouds, and since the information is filled up by the prior, a realistic shape of the prior is important for realistic total column estimation in these cases. Over oceans, aircraft measurements with the Water Isotope System for Precipitation and Entrainment Research (WISPER) instrument from a field campaign in 2018 are used for validation, yielding biases of −3.9 % in H2O and −3 ‰ in δD over clouds. To demonstrate the added value of the new dataset, a short case study of a cold air outbreak over the Atlantic Ocean in January 2020 is presented, showing the daily evolution of the event with single-overpass results.

The objective of this study is to derive methane (CH4) emissions from three landfills, which are found to be the most significant CH4 sources in the metropolitan area of Madrid in Spain. We derive CH4 emissions from the CH4 enhancements observed by spaceborne and ground-based instruments. We apply satellite-based measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) together with measurements from the ground-based COllaborative Carbon Column Observing Network (COCCON) instruments. In 2018, a 2-week field campaign for measuring the atmospheric concentrations of greenhouse gases was performed in Madrid in the framework of Monitoring of the Greenhouse Gases Concentrations in Madrid (MEGEI-MAD) project. Five COCCON instruments were deployed at different locations around the Madrid city center, enabling the observation of total column-averaged CH4 mixing ratios (XCH4). Considering the prevalent wind regimes, we calculate the wind-assigned XCH4 anomalies for two opposite wind directions. Pronounced bipolar plumes are found when applying the method to NO2, which implies that our method of wind-assigned anomaly is suitable to estimate enhancements of trace gases at the urban level from satellite-based measurements. For quantifying the CH4 emissions, the wind-assigned plume method is applied to the TROPOMI XCH4 and to the lower tropospheric CH4 / dry-air column ratio (TXCH4) of the combined TROPOMI+IASI product. As CH4 emission strength we estimate 7.4 × 1025 ± 6.4 × 1024 molec. s−1 from the TROPOMI XCH4 data and 7.1 × 1025 ± 1.0 × 1025 molec. s−1 from the TROPOMI+IASI merged TXCH4 data. We use COCCON observations to estimate the local source strength as an independent method. COCCON observations indicate a weaker CH4 emission strength of 3.7 × 1025 molec. s−1 from a local source (the Valdemingómez waste plant) based on observations from a single day. This strength is lower than the one derived from the satellite observations, and it is a plausible result. This is because the analysis of the satellite data refers to a larger area, covering further emission sources in the study region, whereas the signal observed by COCCON is generated by a nearby local source. All emission rates estimated from the different observations are significantly larger than the emission rates provided via the official Spanish Register of Emissions and Pollutant Sources.

Water vapour is the most abundant natural greenhouse gas in the Earth's atmosphere, and global data sets are required for meteorological applications and climate research. The Tropospheric Monitoring Instrument (TROPOMI) on board Sentinel-5 Precursor (S5P) launched on 13 October 2017 has a high spatial resolution of around 5 km and a daily global coverage. Currently, there is no operational total water vapour product for S5P measurements. Here, we present first results of a new scientific total column water vapour (TCWV) product for S5P using the so-called air-mass-corrected differential optical absorption spectroscopy (AMC-DOAS) scheme. This method analyses spectral data between 688 and 700 nm and has already been successfully applied to measurements from the Global Monitoring Experiment (GOME) on ERS-2, the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on Envisat and GOME-2 on MetOp. The adaptation of the AMC-DOAS method to S5P data requires an additional post-processing procedure to correct the influences of surface albedo, cloud height and cloud fraction. The quality of the new AMC-DOAS S5P water vapour product is assessed by comparisons with data from GOME-2 on MetOp-B retrieved also with the AMC-DOAS algorithm and with four independent data sets, namely reanalysis data from the European Centre for Medium range Weather Forecast (ECMWF ERA5), data obtained by the Special Sensor Microwave Imager and Sounder (SSMIS) flown on the Defense Meteorological Satellite Program (DMSP) platform 16 and two scientific S5P TCWV products derived from TROPOMI measurements. Both are recently published TCWV products for S5P provided by the Max Planck Institute for Chemistry (MPIC) in Mainz and the Netherlands Institute for Space Research (SRON), Utrecht. The SRON TCWV is limited to clear-sky scenes over land. These comparisons reveal a good agreement between the various data sets but also some systematic differences between all of them. On average, the daily derived offset between AMC-DOAS S5P TCWV and AMC-DOAS GOME-2B TCWV is negative (around −1.5 kg m−2) over land and positive over ocean surfaces (more than 1.5 kg m−2). In contrast, SSMIS TCWV is on average lower than AMC-DOAS S5P TCWV by about 3 kg m−2. Monthly averaged ERA5 TCWV and AMC-DOAS S5P TCWV comparison shows spatial features over both land and water surface. Over land, there are systematic spatial structures. There are larger differences between AMC-DOAS S5P TCWV and ERA5 TCWV in tropical regions. Over sea, AMC-DOAS S5P TCWV is slightly lower than ERA5 TCWV by around 2 kg m−2. The AMC-DOAS S5P TCWV and S5P TCWV from MPIC agree on average within 1 kg m−2 over both land and ocean. TCWV from SRON shows daily global averaged differences to AMC-DOAS S5P TCWV of around 1.2 kg m−2. All of these differences are in line with the accuracy of these products and with the typical range of differences of 5 kg m−2 obtained when comparing different TCWV data sets. The AMC-DOAS TCWV product for S5P provides therefore a valuable new and independent data set for atmospheric applications which also has a higher spatial coverage than the other S5P TCWV products.

The TROPOspheric Monitoring Instrument (TROPOMI) provides a daily, spatially resolved (initially 7×7 km2, upgraded to 7×5.6 km2 in August 2019) global dataset of CO columns; however, due to the relative sparseness of reliable ground-based data sources, it can be challenging to characterize the validity and accuracy of satellite data products in remote regions such as the high Arctic. In these regions, satellite intercomparisons can supplement model- and ground-based validation efforts and serve to verify previously observed differences. In this paper, we compare the CO products from TROPOMI, the Atmospheric Chemistry Experiment (ACE) Fourier transform spectrometer (FTS), and a high-Arctic ground-based FTS located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut (80.05∘ N, 86.42∘ W). A global comparison of TROPOMI reference profiles scaled by the retrieved total column with ACE-FTS CO partial columns for the period from 28 November 2017 to 31 May 2020 displays excellent agreement between the two datasets (R=0.93) and a small relative bias of -0.83±0.26% (bias ± standard error of the mean). Additional comparisons were performed within five latitude bands: the north polar region (60 to 90∘ N), northern mid-latitudes (20 to 60∘ N), the equatorial region (20∘ S to 20∘ N), southern mid-latitudes (60 to 20∘ S), and the south polar region (90 to 60∘ S). Latitudinal comparisons of the TROPOMI and ACE-FTS CO datasets show strong correlations ranging from R=0.93 (southern mid-latitudes) to R=0.86 (equatorial region) between the CO products but display a dependence of the mean differences on latitude. Positive mean biases of 7.93±0.61 % and 7.21±0.52 % were found in the northern and southern polar regions, respectively, while a negative bias of -9.41±0.55% was observed in the equatorial region. To investigate whether these differences are introduced by cloud contamination, which is reflected in the TROPOMI averaging kernel shape, the latitudinal comparisons were repeated for cloud-covered pixels and clear-sky pixels only, as well as for the unsmoothed and smoothed cases. Clear-sky pixels were found to be biased higher with poorer correlations on average than clear+cloudy scenes and cloud-covered scenes only. Furthermore, the latitudinal dependence on the biases was observed in both the smoothed and unsmoothed cases. To provide additional context to the global comparisons of TROPOMI with ACE-FTS in the Arctic, both satellite datasets were compared against measurements from the ground-based PEARL-FTS. Comparisons of TROPOMI with smoothed PEARL-FTS total columns in the period of 3 March 2018 to 27 March 2020 display a strong correlation (R=0.88); however, a positive mean bias of 14.7±0.16 % was also found. A partial column comparison of ACE-FTS with the PEARL-FTS in the period from 25 February 2007 to 18 March 2020 shows good agreement (R=0.79) and a mean positive bias of 7.89±0.21 % in the ACE-FTS product relative to the ground-based FTS. The magnitude and sign of the mean relative differences are consistent across all intercomparisons in this work, as well as with recent ground-based validation efforts, suggesting that the current TROPOMI CO product exhibits a positive bias in the high-Arctic region. However, the observed bias is within the TROPOMI mission accuracy requirement of ±15 %, providing further confirmation that the data quality in these remote high-latitude regions meets this specification.

In September 2020, the Western United States experienced anomalously severe wildfires that resulted in carbon monoxide (CO) emissions almost three times the 2001-2019 average. In this study, we investigate the influence of wildfires on atmospheric carbon monoxide (CO) variability through a comparative analysis of observations from the Measurements of Pollution in the Troposphere (MOPITT), the Infrared Atmospheric Sounding Interferometer (IASI), and the Tropospheric Monitoring Instrument (TROPOMI). Our focus is on the North American domain, aiming to understand the differences among these products. In general, all instruments show excellent agreement under typical atmospheric CO conditions. However, notable discrepancies were observed in the CO data from the three sensors, particularly in regions with elevated CO total column (TC) values. IASI and TROPOMI consistently showed higher CO values over the western U.S. compared to MOPITT. During the fire episodes, we found that the IASI retrievals suggested higher CO abundances near the surface than the MOPITT thermal infrared retrievals that are probably the result of the differences in the covariance matrices used in IASI and MOPITT retrievals. We also found that the high IASI and TROPOMI CO observations over the western U.S. coincided with high values of the TROPOMI aerosol index (AI), suggesting the presence of absorbing aerosols. The analysis exhibited better agreement between TROPOMI and MOPITT CO TC when the AI values were low. Our results suggest that appropriate quality filtering should be employed when analyzing pollution events with these data. In particular, utilizing the AI for quality filtering may be useful when analyzing extreme pollution events with these satellite products., (© 2024. The Author(s).)

Numerous studies have reported in situ monitoring and source analysis in the Tibetan Plateau (TP), a region crucial for climate systems. However, a gap remains in understanding the comprehensive distribution of atmospheric pollutants in the TP and their transboundary pollution transport. Here, we analyzed the high-resolution satellite TROPOMI observations from 2018 to 2023 in Tibet and its surrounding areas. Our result reveals that, contrary to the results from in situ surface CO monitoring, Tibet exhibits a distinct seasonality in atmospheric carbon monoxide total column average mixing ratio (XCO), with higher levels in summer and lower levels in winter. This distinctive seasonal pattern may be related to the TP's 'air pump' effect and the Asia summer monsoon. Before 2022, the annual growth rate of XCO in Tibet was 1.63 %·year -1 ; however, it declined by 6.88 % in 2022. Source analysis and satellite observations suggest that CO from South Asia may enter Tibet either by crossing the Himalayas or through the Yarlung Zangbo Grand Canyon. We discovered that spring outbreaks of open biomass burning (OBB) in South and Southeast Asia led to an 11.57-27.98 % increase in XCO over Tibet. Favorable wind pattern and unique topography of the canyon promote the high concentrations CO transport to Tibet. Our greater concern is whether the TP will experience more severe transboundary pollution in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Methane emissions from oil and gas production provide an important contribution to global warming. We investigate 2020 emissions from the largest gas field in Algeria, Hassi R'Mel, and the oil-production-dominated area Hassi Messaoud. We use methane data from the high-resolution (20 m) Sentinel-2 instruments to identify and estimate emission time series for 11 superemitters (including 10 unlit flares). We integrate this information in a transport model inversion that uses methane data from the coarser (7 km × 5.5 km) but higher-precision TROPOMI instrument to estimate emissions from both the 11 superemitters (>1 t/h individually) and the remaining diffuse area source (not detected as point sources with Sentinel-2). Compared to a bottom-up inventory for 2019 that is aligned with UNFCCC-reported emissions, we find that 2020 emissions in Hassi R'Mel (0.16 [0.11-0.22] Tg/yr) are lower by 53 [24-73]%, and emissions in Hassi Messaoud (0.22 [0.13-0.28] Tg/yr) are higher by 79 [4-188]%. Our analysis indicates that a larger fraction of Algeria's methane emissions (∼75%) come from oil production than national reporting suggests (5%). Although in both regions the diffuse area source constitutes the majority of emissions, relatively few satellite-detected superemitters provide a significant contribution (24 [12-40]% in Hassi R'Mel; 49 [27-71]% in Hassi Messaoud), indicating that mitigation efforts should address both. Our synergistic use of Sentinel-2 and TROPOMI can produce a unique and detailed emission characterization of oil and gas production areas.

As atmospheric methane concentrations increase at record pace, it is critical to identify individual emission sources with high potential for mitigation. Here, we leverage the synergy between satellite instruments with different spatiotemporal coverage and resolution to detect and quantify emissions from individual landfills. We use the global surveying Tropospheric Monitoring Instrument (TROPOMI) to identify large emission hot spots and then zoom in with high-resolution target-mode observations from the GHGSat instrument suite to identify the responsible facilities and characterize their emissions. Using this approach, we detect and analyze strongly emitting landfills (3 to 29 t hour -1 ) in Buenos Aires, Delhi, Lahore, and Mumbai. Using TROPOMI data in an inversion, we find that city-level emissions are 1.4 to 2.6 times larger than reported in commonly used emission inventories and that the landfills contribute 6 to 50% of those emissions. Our work demonstrates how complementary satellites enable global detection, identification, and monitoring of methane superemitters at the facility level.

Two years of satellite observations were used to quantify methane emissions from coal mines in Queensland, the largest coal-producing state in Australia. The six analyzed surface and underground coal mines are estimated to emit 570 ± 98 Gg a -1 in 2018-2019. Together, they account for 7% of the national coal production while emitting 55 ± 10% of the reported methane emission from coal mining in Australia. Our results indicate that for two of the three locations, our satellite-based estimates are significantly higher than reported to the Australian government. Most remarkably, 40% of the quantified emission came from a single surface mine (Hail Creek) located in a methane-rich coal basin. Our findings call for increased monitoring and investment in methane recovery technologies for both surface and underground mines.