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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 (O 2) column retrieved from the O 2 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 O 2 absorption line strengths as suggested in the literature. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5-P) satellite provides methane (CH 4) measurements with high accuracy and exceptional temporal and spatial resolution and sampling. TROPOMI CH 4 measurements are highly valuable to constrain emissions inventories and for trend analysis, with strict requirements on the data quality. This study describes the improvements that we have implemented to retrieve CH 4 from TROPOMI using the RemoTeC full-physics algorithm. The updated retrieval algorithm features a constant regularization scheme of the inversion that stabilizes the retrieval and yields less scatter in the data and includes a higher resolution surface altitude database. We have tested the impact of three state-of-the-art molecular spectroscopic databases (HITRAN 2008, HITRAN 2016 and Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCH 4 data product is the implementation of an a posteriori correction fully independent of any reference data that is more accurate and corrects for the underestimation at low surface albedo scenes and the overestimation at high surface albedo scenes. After applying the correction, the albedo dependence is removed to a large extent in the TROPOMI versus satellite (Greenhouse gases Observing SATellite – GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network – TCCON) comparison, which is an independent verification of the correction scheme. We validate 2 years of TROPOMI CH 4 data that show the good agreement of the updated TROPOMI CH 4 with TCCON (- 3.4 ± 5.6 ppb) and GOSAT (- 10.3 ± 16.8 ppb) (mean bias and standard deviation). Low- and high-albedo scenes as well as snow-covered scenes are the most challenging for the CH 4 retrieval algorithm, and although the a posteriori correction accounts for most of the bias, there is a need to further investigate the underlying cause. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) aboard of the Sentinel 5 Precursor (S5-P) satellite provides methane (CH4) measurements with high accuracy and exceptional temporal and spatial resolution. TROPOMI CH4 measurements are highly valuable to constrain emissions inventories and for trend analysis, with strict requirements on the data quality. This study describes the improvements that we have implemented to retrieve CH4 from TROPOMI using the RemoTeC full-physics algorithm. The updated TROPOMI CH4 product features a constant regularization scheme of the inversion that stabilizes the retrieval and yields less scatter in the data, and includes a higher resolution surface altitude database. We have tested the impact of three state-of-the-art molecular spectroscopic databases (HITRAN 2008, HITRAN 2016 and Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCH4 data product is the implementation of a posteriori correction fully independent of any reference data that is more accurate and corrects for the underestimation at low surface albedo scenes and the overestimation at high surface albedo scenes. After applying the correction, the albedo dependence is removed to a large extent in the TROPOMI versus satellite (Greenhouse gases Observing SATellite - GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network - TCCON) comparison, which is an independent verification of the correction scheme. We validate two years of TROPOMI CH4 data that shows the good agreement of the updated TROPOMI CH4 with TCCON (-3.4 ± 5.6 ppb) and GOSAT (-10.3 ± 16.8 pbb) (mean bias and standard deviation). Low and high albedo scenes as well as snow covered scenes are the most challenging for the CH4 retrieval algorithm, and although the posteriori correction accounts for most of the bias, there is a need to further investigate the underlying cause. [ABSTRACT FROM AUTHOR]

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4 , respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5∘×5∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4 , corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1σ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1σ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are single-observation random error (1σ): 17.4 ppb (monthly: 8.7 ppb); global bias: -2.0 ppb (-2.9 ppb); and spatiotemporal bias (1σ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/ , last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/ , last access: 10 January 2020). [ABSTRACT FROM AUTHOR]

Methane emissions due to accidents in the oil and natural gas sector are very challenging tomonitor, and hence are seldomconsidered in emission inventories and reporting. One of the main reasons is the lack of measurements during such events. Here we report the detection of large methane emissions from a gas well blowout in Ohio during February to March 2018 in the total column methane measurements from the spaceborne Tropospheric Monitoring Instrument (TROPOMI). From these data, we derive a methane emission rate of 120 ± 32 metric tons per hour. This hourly emission rate is twice that of the widely reported Aliso Canyon event in California in 2015. Assuming the detected emission represents the average rate for the 20-d blowout period, we find the total methane emission from the well blowout is comparable to one-quarter of the entire state of Ohio's reported annual oil and natural gas methane emission, or, alternatively, a substantial fraction of the annual anthropogenic methane emissions from several European countries. Our work demonstrates the strength and effectiveness of routine satellite measurements in detecting and quantifying greenhouse gas emission from unpredictable events. In this specific case, the magnitude of a relatively unknown yet extremely large accidental leakage was revealed using measurements of TROPOMI in its routine global survey, providing quantitative assessment of associated methane emissions. [ABSTRACT FROM AUTHOR]

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term Climate Data Records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003-2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT, TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory-2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the “ensemble median algorithm” (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5ox5o data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consists of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets): single observation random error (1-sigma): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm), spatio-temporal bias or “relative accuracy” (1-sigma): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are: single observation random error (1-sigma): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb), spatio-temporal bias (1-sigma): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from December 2019 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/). [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI), launched on 13 October 2017, aboard the Sentinel-5 Precursor satellite, measures reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral range. It enables daily global mapping of key atmospheric species for monitoring air quality and climate. We present the first methane observations from November and December 2017, using TROPOMI radiance measurements in the shortwave infrared band around 2.3 μm. We compare our results with the methane product obtained from the Greenhouse gases Observing SATellite (GOSAT). Although different spectral ranges and retrieval methods are used, we find excellent agreement between the methane products acquired from the two satellites with a mean difference of 13.6 ppb, standard deviation of 19.6 ppb, and Pearson’s correlation coefficient of 0.95. Our preliminary results capture the latitudinal gradient and show expected regional enhancements, for example, in the African Sudd wetlands, with much more detail than has been observed before. [ABSTRACT FROM AUTHOR]

Methane is an important atmospheric greenhouse gas and an adequate understanding of its emission sources is needed for climate change assessments, predictions, and the development and verification of emission mitigation strategies. Satellite retrievals of near-surface-sensitive column-averaged dry-air mole fractions of atmospheric methane, i.e. XCH4, can be used to quantify methane emissions. Maps of time-averaged satellite-derived XCH4 show regionally elevated methane over several methane source regions. In order to obtain methane emissions of these source regions we use a simple and fast data-driven method to estimate annual methane emissions and corresponding 1 uncertainties directly from maps of annually averaged satellite XCH4. From theoretical considerations we expect that our method tends to underestimate emissions. When applying our method to high-resolution atmospheric methane simulations, we typically find agreement within the uncertainty range of our method (often 100%) but also find that our method tends to underestimate emissions by typically about 40%. To what extent these findings are model dependent needs to be assessed. We apply our method to an ensemble of satellite XCH4 data products consisting of two products from SCIAMACHY/ENVISAT and two products from TANSOFTS/GOSAT covering the time period 2003-2014. We obtain annual emissions of four source areas: Four Corners in the south-western USA, the southern part of Central Valley, California, Azerbaijan, and Turkmenistan. We find that our estimated emissions are in good agreement with independently derived estimates for Four Corners and Azerbaijan. For the Central Valley and Turkmenistan our estimated annual emissions are higher compared to the EDGAR v4.2 anthropogenic emission inventory. For Turkmenistan we find on average about 50% higher emissions with our annual emission uncertainty estimates overlapping with the EDGAR emissions. For the region around Bakersfield in the Central Valley we find a factor of 5-8 higher emissions compared to EDGAR, albeit with large uncertainty. Major methane emission sources in this region are oil/gas and livestock. Our findings corroborate recently published studies based on aircraft and satellite measurements and new bottom-up estimates reporting significantly underestimated methane emissions of oil/gas and/or livestock in this area in EDGAR. [ABSTRACT FROM AUTHOR]

This work presents the operational methane retrieval algorithm for the Sentinel 5 Precursor (S5P) satellite and its performance tested on realistic ensembles of simulated measurements. The target product is the column-averaged dry air volume mixing ratio of methane (XCH4), which will be retrieved simultaneously with scattering properties of the atmosphere. The algorithm attempts to fit spectra observed by the shortwave and near-infrared channels of the TROPOspheric Monitoring Instrument (TROPOMI) spectrometer aboard S5P. The sensitivity of the retrieval performance to atmospheric scattering properties, atmospheric input data and instrument calibration errors is evaluated. In addition, we investigate the effect of inhomogeneous slit illumination on the instrument spectral response function. Finally, we discuss the cloud filters to be used operationally and as backup. We show that the required accuracy and precision of  < 1 % for the XCH4 product are met for clear-sky measurements over land surfaces and after appropriate filtering of difficult scenes. The algorithm is very stable, having a convergence rate of 99 %. The forward model error is less than 1 % for about 95 % of the valid retrievals. Model errors in the input profile of water do not influence the retrieval outcome noticeably. The methane product is expected to meet the requirements if errors in input profiles of pressure and temperature remain below 0.3 % and 2 K, respectively. We further find that, of all instrument calibration errors investigated here, our retrievals are the most sensitive to an error in the instrument spectral response function of the shortwave infrared channel. [ABSTRACT FROM AUTHOR]

Methane ( CH 4 ) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane ( XCH 4 ). To understand and quantify the spatial differences of the seasonal cycle and trend of XCH 4 in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average XCH 4 seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC CH 4 Proxy algorithm version 2.3.8 and RemoTeC CH 4 Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43°S and 53.13°N. At latitude bands, we also compared the trend of GOSAT XCH 4 retrievals to the NOAA's Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of XCH 4 agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of XCH 4 . These differences are linked to the regional CH 4 sources and sinks, and call for further research. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) aboard of the Sentinel 5 Precursor (S5P) is operational since about one year and measurements of the total column concentration of methane is one of the primary targets of the mission. The CH4 retrieval algorithm used for the operational processing of TROPOMI data is developed by SRON Netherlands Institute for Space Research. It retrieves column averaged dry air volume mixing ratio of CH4 from backscatter radiances measured by TROPOMI in the near-infrared (NIR, 675-775 nm) and shortwave-infrared (SWIR, 2305-2385 nm) spectral range while accounting for atmospheric scattering with the RemoTeC full-physics retrieval algorithm. In this contribution, we present results from the first year of TROPOMI methane measurements. We have set-up an improved retrieval scheme and tested the impact of several molecular absorption cross sections databases on the retrieval. With the new retrieval approach, we reach a very good agreement with thirteen different TCCON sites located in North America, Asia, Europe and Oceania. The mean bias is lower than 5 ppb and the station-to-station variability lower than 6 ppb. Furthermore, the albedo dependent bias is reduced significantly compared to the proxy methane product from the Japanese GOSAT satellite. To illustrate the usage of the dataset, we show first case studies where we analyse in detail the temporal and spatial variability of methane over several regions in the United States. [ABSTRACT FROM AUTHOR]

There are strong indications from on-ground and airborne measurement campaigns thatmethane (CH4) leakage due to activities by the oil &#x0026; gas sector may have been significantlyunderestimated. Especially, accidental leakages of CH4 from the oil &#x0026; gas sector can releaselarge amounts of the gas to the atmosphere within a short period of time. These accidentalemissions are difficult to monitor due to their erratic nature, and administrative and logisticallimitations. A promising means to regularly monitor CH4 emission due to leakages is via aspace-based platform regularly scanning the entire globe. In this study, we use CH4 totalcolumn estimates derived from Tropospheric Monitoring Instrument (TROPOMI)measurements, with a 7×7 km2 spatial resolution at nadir and daily global coverage. Wedemonstrate that single sounding accuracy of the TROPOMI data is sufficient to detect andquantify large accidental emissions by reporting on atmospheric measurements of CH4leakage from a gas well blowout in Ohio that took place in February 2018. UsingTROPOMI CH4 measurements and WRF atmospheric tracer transport simulation,we quantify the CH4 emission rate and total release from the blowout accident. [ABSTRACT FROM AUTHOR]

The Sentinel-5 Precursor (S-5P) satellite that was launched on 13 October 2017 is the first ESA Sentinel mission for atmospheric composition monitoring. It is a contribution to the European Commission Copernicus Programme to improve our understanding of the earth and its environment. The main objective of the S-5P mission is to perform atmospheric measurements with high spatio-temporal resolution to monitor air quality, climate forcing, ozone and UV radiation. The TROPOspheric Monitoring Instrument (TROPOMI) is the single payload onboard S-5P and measures the earth's reflected radiances from the ultraviolet to the shortwave infrared spectral range with a daily global coverage at a high spatial resolution of 7x7 km2. In this presentation we will show the first validation results of the soon-to-be released S-5P methane product and the validation results of the carbon monoxide product from the start of the measurements in 2017 to the most recent available data. The validation study is performed as part of the ESA-AO project TCCON4S5P which uses data from all ground-based FTIR stations of the Total Carbon Column Observing Network (TCCON) and of the S-5P Mission Performance Centre (MPC) which uses publicly available data from the Network for the Detection of Atmospheric Composition Change – Infrared Working Group (NDACC-IRWG) and TCCON stations. The site-dependent biases and correlations will be discussed in detail with focus on different geophysical and meteorological conditions. [ABSTRACT FROM AUTHOR]