The retrieval of methane from satellite measurements is sensitive to the reflectance of the surface, and in many regions, especially those with agriculture, surface reflectance depends on the season. Existing corrections for this effect do not take into account a changing relationship between reflectance and the methane correction value over time. It is an important issue to consider, as agricultural emissions of methane are significant and other sources, like oil and gas production, are also often located in agricultural lands. In this work, we use a set of 12 monthly machine learning models to generate a seasonally resolved surface albedo correction for TROPOspheric Monitoring Instrument (TROPOMI) methane data across the Denver–Julesburg basin. We found that land cover is important in the correction, specifically the type of crops grown in an area, with drought-resistant-crop-covered areas requiring a correction of 5–6 ppb larger than areas covered in water-intensive crops in the summer. Additionally, the correction over different land covers changes significantly over the seasonally resolved timescale, with corrections over drought-resistant crops being up to 10 ppb larger in the summer than in the winter. This correction will allow for more accurate determination of methane emissions by removing the effect of agricultural and other seasonal effects on the albedo correction. The correction may also allow for the deconvolution of agricultural methane emissions, which are seasonally dependent, from oil and gas emissions, which are more constant in time.

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

Tropical tropospheric ozone (TTO) is important for the global radiation budget because the longwave radiative effect of tropospheric ozone is higher in the tropics than midlatitudes. In recent decades the TTO burden has increased, partly due to the ongoing shift of ozone precursor emissions from midlatitude regions toward the Equator. In this study, we assess the distribution and trends of TTO using ozone profiles measured by high-quality in situ instruments from the IAGOS (In-Service Aircraft for a Global Observing System) commercial aircraft, the SHADOZ (Southern Hemisphere ADditional OZonesondes) network, and the ATom (Atmospheric Tomographic Mission) aircraft campaign, as well as six satellite records reporting tropical tropospheric column ozone (TTCO): TROPOspheric Monitoring Instrument (TROPOMI), Ozone Monitoring Instrument (OMI), OMI/Microwave Limb Sounder (MLS), Ozone Mapping Profiler Suite (OMPS)/Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI)/Global Ozone Monitoring Experiment 2 (GOME2). With greater availability of ozone profiles across the tropics we can now demonstrate that tropical India is among the most polluted regions (e.g., western Africa, tropical South Atlantic, Southeast Asia, Malaysia and Indonesia), with present-day 95th percentile ozone values reaching 80 nmol mol−1 in the lower free troposphere, comparable to midlatitude regions such as northeastern China and Korea. In situ observations show that TTO increased between 1994 and 2019, with the largest mid- and upper-tropospheric increases above India, Southeast Asia, and Malaysia and Indonesia (from 3.4 ± 0.8 to 6.8 ± 1.8 nmol mol−1 decade−1), reaching 11 ± 2.4 and 8 ± 0.8 nmol mol−1 decade−1 close to the surface (India and Malaysia–Indonesia, respectively). The longest continuous satellite records only span 2004–2019 but also show increasing ozone across the tropics when their full sampling is considered, with maximum trends over Southeast Asia of 2.31 ± 1.34 nmol mol−1 decade−1 (OMI) and 1.69 ± 0.89 nmol mol−1 decade−1 (OMI/MLS). In general, the sparsely sampled aircraft and ozonesonde records do not detect the 2004–2019 ozone increase, which could be due to the genuine trends on this timescale being masked by the additional uncertainty resulting from sparse sampling. The fact that the sign of the trends detected with satellite records changes above three IAGOS regions, when their sampling frequency is limited to that of the in situ observations, demonstrates the limitations of sparse in situ sampling strategies. This study exposes the need to maintain and develop high-frequency continuous observations (in situ and remote sensing) above the tropical Pacific Ocean, the Indian Ocean, western Africa, and South Asia in order to estimate accurate and precise ozone trends for these regions. In contrast, Southeast Asia and Malaysia–Indonesia are regions with such strong increases in ozone that the current in situ sampling frequency is adequate to detect the trends on a relatively short 15-year timescale.@en

COVID-19 caused a historic collapse in fossil fuel demand, a general decline in economic activity, and hydrocarbon price volatility. This resulted in an unprecedented scenario to evaluate the contribution of the O&G (Oil and Gas) industry NO₂ (nitrogen dioxide) emissions in the Permian basin (United States), currently the second largest hydrocarbon-bearing area on Earth. TROPOMI (Tropospheric Monitoring Instrument), on board the Sentinel-5P satellite, has captured the impact of the oil and gas industry emissions during the COVID-19 lockdown. A generalized drop (∼30%) of NO₂ emissions derived using the divergence method in comparison with 2019 was observed following the decline in production and drilling (13% and 68% respectively) during the lockdown. NO₂ tropospheric columns were less impacted with a smaller decrease (∼4%) across the basins. This study demonstrates that the impact of the COVID-19 lockdown on NO₂ emissions was not only present in urban areas but also in vast O&G production regions, which shows the potential of TROPOMI to assess future pollution mitigation strategies for this industry.

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The purpose of this study is to investigate the ability of the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) to derive accurate geometrical features of lofted aerosol layers, selecting the Mediterranean Basin as the study area. Comparisons with ground-based correlative measurements constitute a key component in the validation of passive and active satellite aerosol products. For this purpose, we use ground-based observations from quality-controlled lidar stations reporting to the European Aerosol Research Lidar Network (EARLINET). An optimal methodology for validation purposes has been developed and applied using the EARLINET optical profiles and TROPOMI aerosol products, aiming at the in-depth evaluation of the TROPOMI aerosol layer height (ALH) product for the period 2018 to 2022 over the Mediterranean Basin. Seven EARLINET stations were chosen, taking into consideration their proximity to the sea, which provided 63 coincident aerosol cases for the satellite retrievals. In the following, we present the first validation results for the TROPOMI/S5P ALH using the optimized EARLINET lidar products employing the automated validation chain designed for this purpose. The quantitative validation at pixels over the selected EARLINET stations illustrates that the TROPOMI ALH product is consistent with the EARLINET lidar products, with a high correlation coefficient RCombining double low line0.82 (RCombining double low line0.51) and a mean bias of -0.51±0.77 km and -2.27±1.17 km over ocean and land, respectively. Overall, it appears that aerosol layer altitudes retrieved from TROPOMI are systematically lower than altitudes from the lidar retrievals. High-albedo scenes, as well as low-aerosol-load scenes, are the most challenging for the TROPOMI retrieval algorithm, and these results testify to the need to further investigate the underlying cause. This work provides a clear indication that the TROPOMI ALH product can under certain conditions achieve the required threshold accuracy and precision requirements of 1 km, especially when only ocean pixels are included in the comparison analysis. Furthermore, we describe and analyse three case studies in detail, one dust and two smoke episodes, in order to illustrate the strengths and limitations of the TROPOMI ALH product and demonstrate the presented validation methodology. The present analysis provides important additions to the existing validation studies that have been performed so far for the TROPOMI S5P ALH product, which were based only on satellite-to-satellite comparisons.

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We have analyzed Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) data over the Copperbelt mining region (Democratic Republic of Congo and Zambia). Despite high background values, annual 2019–2022 means of TROPOMI NO₂ (nitrogen dioxide) show local enhancements consistent with six point sources (four copper/cobalt mines, two cities) where high-emission industrial activities take place. We have quantified annual NO_x (nitrogen oxides) emissions from these point sources, identified temporal trends in emissions, and found strong correlations with production data from colocated mines and one oil refinery. The Copernicus Atmosphere Monitoring Service Global Anthropogenic (CAMS-GLOB-ANT) version 5 inventory underpredicts TROPOMI-derived emissions and lacks the temporal trends observed in TROPOMI and mine/refinery production. These results demonstrate the potential for satellite monitoring of mining and other industrial activities, often unreported or underestimated, which impact the air quality of local communities. This is particularly important for Africa, where mining is increasing aggressively.

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Wildfires have become larger and more frequent because of climate change, increasing their impact on air pollution. Air quality forecasts and climate models do not currently account for changes in the composition of wildfire emissions during the commonly observed progression from more flaming to smoldering combustion. Laboratory measurements have consistently shown decreased nitrogen dioxide (NO₂) relative to carbon monoxide (CO) over time, as they transitioned from more flaming to smoldering combustion, while formaldehyde (HCHO) relative to CO remained constant. Here, we show how daily ratios between column densities of NO₂ versus those of CO and HCHO versus CO from the Tropospheric Monitoring Instrument (TROPOMI) changed for large wildfires in the Western United States. TROPOMI-derived emission ratios were lower than those from the laboratory. We discuss reasons for the discrepancies, including how representative laboratory burns are of wildfires, the effect of aerosols on trace gas retrievals, and atmospheric chemistry in smoke plumes.

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We analyzed observational and model data to study the sources of formaldehyde over oil and gas production regions and to investigate how these observations may be used to constrain oil and gas volatile organic compound (VOC) emissions. The analysis of aircraft and satellite data consistently found that formaldehyde over oil and gas production regions during spring and summer is mostly formed by the photooxidation of precursor VOCs. Formaldehyde columns over the Permian Basin, one of the largest oil- and gas-producing regions in the United States, are correlated with the production locations. Formaldehyde simulations by the atmospheric chemistry and transport model WRF-Chem, which included oil and gas NO_x and VOC emissions from the fuel-based oil and gas inventory, were in very good agreement with TROPOMI satellite measurements. Sensitivity studies illustrated that VOCs released from oil and gas activities are important precursors to formaldehyde, but other sources of VOCs contribute as well and that the formation of secondary formaldehyde is highly sensitive to NO_x. We also investigated the ability of the chemical mechanism used in WRF-Chem to represent formaldehyde formation from oil and gas hydrocarbons by comparing against the Master Chemical Mechanism. Further, our work provides estimates of primary formaldehyde emissions from oil and gas production activities, with per basin averages ranging from 0.07 to 2.2 kg h^-1 in 2018. A separate estimate for natural gas flaring found that flaring emissions could contribute 5 to 12% to the total primary formaldehyde emissions for the Permian Basin in 2018.

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Emissions of methane (CH₄) in the Permian basin (USA) have been derived for 2019 and 2020 from satellite observations of the Tropospheric Monitoring Instrument (TROPOMI) using the divergence method, in combination with a data driven method to estimate the background column densities. The resulting CH₄ emission data, which have been verified using model data with known emissions, have a spatial resolution of approximately 10 km. The CH₄ emissions show moderate spatial correlation with the locations of oil and gas production and drilling activities in the Permian basin, as well as with emissions of nitrogen oxides (NO_x). Analysis of the emission maps and time series indicates that a significant fraction of methane emissions in the Permian basin is from frequent widespread emissions sources, rather than from a few infrequent very large unplanned releases, which is important considering possible CH₄ emission mitigation strategies. In addition to providing spatially resolved emissions, the divergence method also provides the total emissions of the Permian basin and its main sub-basins. The total CH₄ emission of the Permian is estimated as 3.0 ± 0.7 Tg yr⁻¹ for 2019, which agrees with other independent estimates based on TROPOMI data. For the Delaware sub-basin, it is estimated as 1.4 ± 0.3 Tg yr⁻¹ for 2019, and for the Midland sub-basin 1.2 ± 0.3 Tg yr⁻¹. In 2020 the emissions are 9% lower compared to 2019 in the entire Permian basin, and respectively 19% and 27% for the Delaware and Midland sub-basins.

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Nitrogen dioxide (NO₂) is an important contributor to air pollution and can adversely affect human health^1–9. A decrease in NO₂ concentrations has been reported as a result of lockdown measures to reduce the spread of COVID-19^10–20. Questions remain, however, regarding the relationship of satellite-derived atmospheric column NO₂ data with health-relevant ambient ground-level concentrations, and the representativeness of limited ground-based monitoring data for global assessment. Here we derive spatially resolved, global ground-level NO₂ concentrations from NO₂ column densities observed by the TROPOMI satellite instrument at sufficiently fine resolution (approximately one kilometre) to allow assessment of individual cities during COVID-19 lockdowns in 2020 compared to 2019. We apply these estimates to quantify NO₂ changes in more than 200 cities, including 65 cities without available ground monitoring, largely in lower-income regions. Mean country-level population-weighted NO₂ concentrations are 29% ± 3% lower in countries with strict lockdown conditions than in those without. Relative to long-term trends, NO₂ decreases during COVID-19 lockdowns exceed recent Ozone Monitoring Instrument (OMI)-derived year-to-year decreases from emission controls, comparable to 15 ± 4 years of reductions globally. Our case studies indicate that the sensitivity of NO₂ to lockdowns varies by country and emissions sector, demonstrating the critical need for spatially resolved observational information provided by these satellite-derived surface concentration estimates.

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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 14g % and 63g %. 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.

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

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The production of crude oil and natural gas is associated with emissions of air pollutants, such as nitrogen oxides (NOx = NO + NO2) and volatile organic compounds, which are precursors for the formation of ground-level ozone. Knowledge of these emissions is critical to the understanding and mitigation of local air quality. NOx emissions from oil and gas production activities are not well described in commonly used emission inventories, and discrepancies of several factors have been found in the past. Here we present an easy and computationally efficient method to quantify NOx emissions from satellite NO2 observations that can be applied to evaluate common emission inventories and provide timely input for chemistry transport models. Using NO2 columns from the TROPOspheric Monitoring Instrument (TROPOMI), we calculated annually averaged NOx emissions from the divergence of NO2 column fluxes for six oil and gas production regions in the United States. Derived NOx emissions for the years 2018 to 2020 range between 4.8 and 81.1 t/day, and observed trends over time are consistent with changes in industrial activity. To evaluate the method, we compared our results with the fuel-based oil and gas NOx inventory (FOG) and performed sensitivity studies using model output from the Weather Research Forecasting model with Chemistry (WRF-Chem). We found that annually averaged NOx emissions from oil and gas production activities can in most cases be calculated within an uncertainty of 50%, while simultaneously derived emission maps show the spatial distribution of NOx emissions with a high level of detail. For future use, this method can easily be applied globally.

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Southeast Australia experienced intensive and geographically extensive wildfires during the 2019–2020 summer season^1,2. The fires released substantial amounts of carbon dioxide into the atmosphere³. However, existing emission estimates based on fire inventories are uncertain⁴, and vary by up to a factor of four for this event. Here we constrain emission estimates with the help of satellite observations of carbon monoxide⁵, an analytical Bayesian inversion⁶ and observed ratios between emitted carbon dioxide and carbon monoxide⁷. We estimate emissions of carbon dioxide to be 715 teragrams (range 517–867) from November 2019 to January 2020. This is more than twice the estimate derived by five different fire inventories^8–12, and broadly consistent with estimates based on a bottom-up bootstrap analysis of this fire episode¹³. Although fires occur regularly in the savannas in northern Australia, the recent episodes were extremely large in scale and intensity, burning unusually large areas of eucalyptus forest in the southeast¹³. The fires were driven partly by climate change^14,15, making better-constrained emission estimates particularly important. This is because the build-up of atmospheric carbon dioxide may become increasingly dependent on fire-driven climate–carbon feedbacks, as highlighted by this event¹⁶.

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The global fire emission inventories depend on ground and airborne measurements of species-specific emission factors (EFs), which translate dry matter losses due to fires to actual trace gas and aerosol emissions. The EFs of nitrogen oxides (NOx) and carbon monoxide (CO) can function as a proxy for combustion efficiency to distinguish flaming from smoldering combustion. The uncertainties in these EFs remain large as they are limited by the spatial and temporal representativeness of the measurements. The global coverage of satellite observations has the advantage of filling this gap, making these measurements highly complementary to ground-based or airborne data. We present a new analysis of biomass burning pollutants using space-borne data to investigate the spatiotemporal efficiency of fire combustion. Column measurements of nitrogen dioxide and carbon monoxide from the TROPOspheric Monitoring Instrument (TROPOMI) are used to quantify the relative atmospheric enhancements of these species over different fire-prone regions around the world. We find spatial and temporal patterns in the classCombining double low linespan classCombining double low lineratio that point to distinct differences in biomass burning behavior. Such differences are induced by the burning phase of the fire (e.g., high-temperature flaming vs. low-temperature smoldering combustion) and burning practice (e.g., the combustion of logs, coarse woody debris and soil organic matter vs. the combustion of fine fuels such as savanna grasses). The sampling techniques and the signal-to-noise ratio of the retrieved <span classCombining double low signals were quantified with WRF-Chem experiments and showed similar distinct differences in combustion types. The TROPOMI measurements show that the fraction of surface smoldering combustion is much larger for the boreal forest fires in the upper Northern Hemisphere and peatland fires in Indonesia. These types of fires cause a much larger increase (3 to 6 times) in <span classCombining double low lineinline-formula relative to span classCombining double low lineinline-formula than elsewhere in the world. The high spatial and temporal resolution of TROPOMI also enables the detection of spatial gradients in combustion efficiency at smaller regional scales. For instance, in the Amazon, we found higher combustion efficiency (up to 3-fold) for savanna fires than for the nearby tropical deforestation fires. Out of two investigated fire emission products, the TROPOMI measurements support the broad spatial pattern of combustion efficiency rooted in GFED4s. Meanwhile, TROPOMI data also add new insights into regional variability in combustion characteristics that are not well represented in the different emission inventories, which can help the fire modeling community to improve their representation of the spatiotemporal variability in EFs.

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We present a new divergence method to estimated methane (CH₄) emissions from satellite observed mean mixing ratio of methane (XCH₄) by deriving the regional enhancement of XCH₄ in the Planetary Boundary Layer (PBL). The applicability is proven by comparing the estimated emissions with its known emission inventory from a 3-month GEOS-Chem simulation. When applied to TROPOspheric Monitoring Instrument observations, sources from well-known oil/gas production areas, livestock farms and wetlands in Texas become clearly visible in the emission maps. The calculated yearly averaged total CH₄ emission over the Permian Basin is 3.06 (2.82, 3.78) Tg a⁻¹ for 2019, which is consistent with previous studies and double that of EDGAR v4.3.2 for 2012. Sensitivity tests on PBL heights, on the derived regional background and on wind speeds suggest our divergence method is quite robust. It is also a fast and simple method to estimate the CH₄ emissions globally.

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This paper reports on consolidated ground-based validation results of the atmospheric NO2 data produced operationally since April 2018 by the TROPOspheric Monitoring Instrument (TROPOMI) on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite. Tropospheric, stratospheric, and total NO2 column data from S5P are compared to correlative measurements collected from, respectively, 19 Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), 26 Network for the Detection of Atmospheric Composition Change (NDACC) Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 Pandonia Global Network (PGN)/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatiotemporal co-location of the satellite and correlative data, e.g. by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5P data show, on average, (i) a negative bias for the tropospheric column data, of typically-23 % to-37 % in clean to slightly polluted conditions but reaching values as high as-51 % over highly polluted areas; (ii) a slight negative median difference for the stratospheric column data, of about-0:2 Pmolec cm-2, i.e. approx.-2 % in summer to-15 % in winter; and (iii) a bias ranging from zero to-50 % for the total column data, found to depend on the amplitude of the total NO2 column, with small to slightly positive bias values for columns below 6 Pmolec cm-2 and negative values above. The dispersion between S5P and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec cm-2) but exceed those for the tropospheric column data (0.7 Pmolec cm-2). While a part of the biases and dispersion may be due to representativeness differences such as different area averaging and measurement times, it is known that errors in the S5P tropospheric columns exist due to shortcomings in the (horizontally coarse) a priori profile representation in the TM5-MP chemical transport model used in the S5P retrieval and, to a lesser extent, to the treatment of cloud effects and aerosols. Although considerable differences (up to 2 Pmolec cm-2 and more) are observed at single ground-pixel level, the near-real-time (NRTI) and offline (OFFL) versions of the S5P NO2 operational data processor provide similar NO2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values. @en

Quantitative measurements of aerosol absorptive properties, e.g., the absorbing aerosol optical depth (AAOD) and the single scattering albedo (SSA), are important to reduce uncertainties of aerosol climate radiative forcing assessments. Currently, global retrievals of AAOD and SSA are mainly provided by the ground-based aerosol robotic network (AERONET), whereas it is still challenging to retrieve them from space. However, we found the AERONET AAOD has a relatively strong correlation with the satellite retrieved ultra-violet aerosol index (UVAI). Based on this, a numerical relation is built by a deep neural network (DNN) to predict global AAOD and SSA over land from the long-term UVAI record (2006-2019) provided by the ozone monitoring instrument (OMI) onboard Aura. The DNN predicted aerosol absorption is satisfying for samples with AOD at 550 nm larger than 0.1, and the DNN model performance is better for smaller absorbing aerosols (e.g., smoke) than larger ones (e.g., mineral dust). The comparison of the DNN predictions with AERONET shows a high correlation coefficient of 0.90 and a root mean square of 0.005 for the AAOD, and over 80% of samples are within the expected uncertainty of AERONET SSA (pm0.03).

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Societal dependence on, and commercial and scientific exploitation of Earth-Oriented remote sensing from satellites is growing at an exponential rate. The comprehensive EU Copernicus programme provides a major contribution to the global effort, but even so, to achieve the necessary global and temporal coverage requires synergistic cooperation and associated interoperability of the Worlds sensors. For a user to exploit Earth Observation (EO) data there must exist confidence in data characteristics, quality and reliable delivery. Although long-term data records for climate may be the most demanding in nature, generation of analysis-ready operational data sets for applications, as diverse as food security to pollution monitoring, all require the user to have some quantitative level of confidence in the data and derived information. A long-term Calibration/Validation (Cal/Val) vision necessitates clear ownership and long-term funding. Delineating the roles of the European Commission (EC), space agencies and member states in long-term Cal/Val would provide clarity. It is clear that the space agencies have the responsibility to meet the mission requirement of their spaceborne instruments but long-term validation is often entrusted to interested parties bringing their own resources to the task. Furthermore, there is a critical need for Fiducial Reference Measurements (FRMs), acquired in operational mode, and comprehensive in coverage both spatially and temporally, to assure that the satellite product accuracies are met. This paper discusses the current status, gaps and challenges regarding long-term Cal/Val of EO satellites and recommends the creation of a European coordinating entity for satellite product calibration and validation. The proposed entity would be an integrative organization coordinating the European Cal/Val activities in partnership with the member states and the space agencies and working together with existing data providers to secure access to satellite and in-situ data of traceable FRM standards.

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

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