Comment on gmd-2022-45

We present a user-friendly, cloud-based facility for quantifying methane emissions with 0.25° × 0.3125° (≈ 25 × 25 km2) resolution by inversion of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI). The facility is built on an Integrated Methane Inversion optimal estimation workflow (IMI 1.0) and supported for use on the Amazon Web Services (AWS) cloud. It exploits the GEOS-Chem chemical transport model and TROPOMI data already resident on AWS, thus avoiding cumbersome big-data download. Users select a region and period of interest, and the IMI returns an analytical solution for the Bayesian optimal estimate of emissions on the 0.25° × 0.3125° grid including error statistics, information content, and visualization code for inspection of results. An out-of-the-box inversion with rectilinear grid and default prior emission estimates can be conducted with no significant learning curve. Users can also configure their inversions to infer emissions for irregular regions of interest, swap in their own prior emission inventories, and modify inversion parameters. Inversion ensembles can be generated at minimal additional cost once the Jacobian matrix for the analytical inversion has been constructed. A preview feature allows users to determine the TROPOMI information content for their region and time period of interest before actually performing the inversion. The IMI is heavily documented and is intended to be accessible by researchers and stakeholders with no expertise in inverse modelling or high-performance computing. We demonstrate the IMI’s capabilities by applying it to estimate methane emissions from the US oil-producing Permian Basin in May 2018.

<strong class="journal-contentHeaderColor">Abstract.</strong> Since 2017 the Tropospheric Monitoring Instrument (TROPOMI) onboard ESA&rsquo;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 &times; 7 km² (7&times; 7 km² before August 2019). 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 (VCD) of CAMS-IFS but to a lesser extent also its vertical CO distribution as well. 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 a posteriori profile retrieval that combines individual 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 &ldquo;Rabbit Foot Fire&rdquo; in Idaho on the 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 fluxes of trace gases and aerosols (BB-FLUX) field campaign. The distinct CO enhancement in a plume aloft, 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 18 August 2018. The TROPOMI data is estimating 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 (1&ndash;15 August 2019) the CO profile retrieved from the TROPOMI measurements 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 provide information about the vertical distribution of CO.

TROPOspheric Monitoring Instrument observations of total column water vapour: Algorithm and validation

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. 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. 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.

Methane (CH&amp;#8324;) is a potent greenhouse gas that plays a significant role in global warming, with over 60% of CH&amp;#8324; emissions attributed to human activities. A substantial portion of these anthropogenic emissions originates from a small number of super-emitters, making their monitoring crucial for understanding emission patterns and implementing targeted mitigation strategies. The TROPOspheric Monitoring Instrument (TROPOMI), onboard the ESA Sentinel-5P satellite, provides daily global observations of methane mixing ratios at high spatial resolution, enabling the detection and analysis of plumes associated with these super-emitters.To detect methane plumes globally in TROPOMI data, a two-step machine learning pipeline is employed, with weekly results now officially part of the Copernicus Atmosphere Monitoring Service (CAMS) products. The first step utilizes a convolutional neural network to identify plume-like structures in the methane data. A support vector classifier is then applied to filter out retrieval artifacts, ensuring the accurate identification of true methane emissions. Detected plumes are categorized by emission rate, and human experts conduct final verifications. In this study, we present the results for the full year 2024, during which a total of 2,311 methane plumes were identified, providing a comprehensive overview of global super-emitter activity. Preliminary analysis based on bottom-up emission inventories shows that the majority of plumes are associated with oil and gas production, landfills, and coal mining activities.This study provides a detailed analysis of methane plume detections for 2024, highlighting temporal variations and regional hotspots. By focusing on the detected plumes rather than the detection methodology, this work delivers valuable insights into the spatial and temporal dynamics of methane emissions. The findings contribute to the growing body of knowledge required to address super-emitter mitigation and support informed policymaking for reducing global methane emissions.

Introduction: Monitoring the volcanic activity of a potentially hazardous volcano is essential for eruption warning and hazard mitigation. The SO2 flux from the volcano is one of the most important measures to understand its activity. Mount Tokachi, in Japan, is an active volcano that experienced three magmatic explosive eruptions in the 20th century (in 1926, 1962, and 1988–1989). Since 2006, geodetic observations have captured ground deformation, suggesting an inflation beneath the main crater. Moreover, since 2020 daily visual observations have detected the increase in plume heights and the occurrence of volcanic glow at the main crater. The high-time-resolution estimation of SO2 flux will help monitor the activity of Mount Tokachi and clarify the associating mechanisms. Furthermore, satellite remote sensing can estimate the vertical column density (VCD) of sulfur dioxide (SO2), enabling the daily determination of SO2 flux without the need to visit the site. Due to the improved spatial resolution, the TROPOspheric Monitoring Instrument (TROPOMI) has advanced satellite-based volcanic gas flux measurements.Methods: We have analyzed the available TROPOMI data and conducted ground-based observations to estimate the SO2 flux from Mount Tokachi at 100–2,000 tons/day.Result and Discussion: The average annual SO2 flux has been increasing since 2021, on par with the increase in plume height and volcanic glow. TROPOMI data enabled quantification of the annual SO2 flux, with a sufficient temporal resolution to monitor the volcanic activity at Mount Tokachi. However, a high flux, such as in excess of 2,000 tons/day, was observed in the winter season. The flux from the satellite data was similar to that from ground-based observations during the summer. However, a seasonal change in flux from the satellite data was observed in winter, with the flux being larger than that in summer, possibly because the flux increases during winter. Another possible reason is the influence of snow cover on satellite observations due to its high surface reflectance. We reanalyzed some TROPOMI data during the winter, in which the ground snow cover was misidentified as clouds at low altitudes. This procedure suppresses the error caused by the high surface reflectance due to winter snow coverage and improves the quality of the annual SO2 flux from satellite observations. This methodology is applicable to other high-latitude or high-altitude volcanoes during specific seasons characterized by frequent fluctuations in snow cover conditions.

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.

<strong class="journal-contentHeaderColor">Abstract.</strong> We quantify weekly methane emissions at 0.25&deg;&times;0.3125&deg; (&asymp;25&times;25 km²) resolution from the Permian Basin, the largest oil production basin in the United States, 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 (&plusmn; standard deviation of weekly estimates) was 3.7 &plusmn; 0.9 Tg a^-1, higher than previous TROPOMI inversion estimates that may have used too-low prior emissions or biased 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 local 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 % &plusmn; 1.3 % and steadily decreased over the period from 5&ndash;6 % in 2018 to 3&ndash;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 recent industry targets of &lt;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.

<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.

<strong class="journal-contentHeaderColor">Abstract.</strong> Methane is the second most important greenhouse gas after carbon dioxide, and accounts for around 10 % of total European Union greenhouse gases emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modeling techniques appear as a powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modeling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modeling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modeling component will be based on the WRF model coupled to a lagrangian particle dispersion module. A description of the modeling tools, input data sets and one-year forward modeling evaluation from April 01, 2018 to March 31, 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modeling for Denmark, will be provided in a second paper. A good general agreement is found between the modeling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model-observation discrepancies for summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from study ranging from 1 to 2 %. Domain-wide correlation coefficients and root-mean-square-errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. For winter months, otherwise, model-observation discrepancies show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 to 3 %. Domain-wide correlation coefficients and root-mean-square-errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modeling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe, and demonstrate a huge and under explored potential for methane inverse modeling using improved TROPOMI products in large-scale applications.

<strong class="journal-contentHeaderColor">Abstract.</strong> Methane is the second most important greenhouse gas after carbon dioxide, and accounts for around 10 % of total European Union greenhouse gases emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modeling techniques appear as a powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modeling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modeling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modeling component will be based on the WRF model coupled to a lagrangian particle dispersion module. A description of the modeling tools, input data sets and one-year forward modeling evaluation from April 01, 2018 to March 31, 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modeling for Denmark, will be provided in a second paper. A good general agreement is found between the modeling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model-observation discrepancies for summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from study ranging from 1 to 2 %. Domain-wide correlation coefficients and root-mean-square-errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. For winter months, otherwise, model-observation discrepancies show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 to 3 %. Domain-wide correlation coefficients and root-mean-square-errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modeling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe, and demonstrate a huge and under explored potential for methane inverse modeling using improved TROPOMI products in large-scale applications.

Abstract. The Arctic is experiencing unprecedented environmental changes with rapidly rising temperatures. Emissions of methane (CH4) – a potent greenhouse gas – may be increasing from the region, making accurate monitoring essential. The TROPOspheric Monitoring Instrument (TROPOMI) instrument offers high spatial and temporal coverage of CH4 column mole fractions. However, its data in the Arctic has historically exhibited seasonal and latitudinal biases and low-quality retrievals. A major challenge is the lack of ground-based validation data in high-latitude regions, which are used to improve satellite retrievals. This study evaluates inverse modelling to estimate CH4 emissions using TROPOMI measurements over the North Slope of Alaska. Using two retrieval products – the operational SRON product and the scientific WFMD product from the University of Bremen – we assess the alignment of derived emissions with surface measurement-derived inversions over 2018–2020 and test their robustness through sensitivity analyses. Our results show that tundra emissions from SRON inversions align more closely with surface measurement-derived emissions than WFMD inversions. Both TROPOMI-product derived emissions have anomalously low emissions in August 2018 compared to surface measurement-derived emissions, likely due to low data density resulting from high cloud cover. TROPOMI inversions provided stronger constraints on fugitive anthropogenic emissions compared to surface inversions. However, each retrieval produced different emission estimates, highlighting retrieval-dependent differences. Sensitivity tests revealed a strong prior dependence in both retrievals, raising concerns about robustness in northern high latitudes. This study highlights the importance of using multiple retrievals and rigorous sensitivity testing in high-latitude satellite inversions.