Comparative analysis of low-Earth orbit (TROPOMI) and geostationary (GeoCARB, GEO-CAPE) satellite instruments for constraining methane emissions on fine regional scales: application to the Southeast US

We conduct Observing System Simulation Experiments (OSSEs) to compare the ability of future satellite measurements of atmospheric methane columns (TROPOMI, GeoCARB, GEO-CAPE) for constraining methane emissions down to the 25&thinsp;km scale through inverse analyses. The OSSE uses the GEOS-Chem chemical transport model (0.25° × 0.3125° grid resolution) in a 1-week simulation for the Southeast US with 216 emission elements to be optimized through inversion of synthetic satellite observations. Clouds contaminate 73&thinsp;%–91&thinsp;% of the viewing scenes depending on pixel size. Comparison of GEOS-Chem to Total Carbon Column Observing Network (TCCON) surface-based methane column observations indicates a model transport error standard deviation of 12&thinsp;ppb, larger than the instrument errors when aggregated on the 25&thinsp;km model grid scale, and with a temporal error correlation of 6&thinsp;h. We find that TROPOMI (7×7&thinsp;km2 pixels, daily return time) can provide a coarse regional optimization of methane emissions, comparable to results from an aircraft campaign (SEAC4RS), and is highly sensitive to cloud cover. The geostationary instruments can do much better and are less sensitive to cloud cover, reflecting both their finer pixel resolution and more frequent observations. The information content from GeoCARB toward constraining methane emissions increases by 20&thinsp;%–25&thinsp;% for each doubling of the GeoCARB measurement frequency. Temporal error correlation in the transport model moderates but does not cancel the benefit of more frequent measurements for geostationary instruments. We find that GeoCARB observing twice a day would provide 70&thinsp;% of the information from the nominal GEO-CAPE mission preformulated by NASA in response to the Decadal Survey of the US National Research Council.</p

Emissions of methane in Europe inferred by total column measurements

Using five long-running ground-based atmospheric observatories in Europe, we demonstrate the utility of long-term, stationary, ground-based measurements of atmospheric total columns for verifying annual methane emission inventories. Our results indicate that the methane emissions for the region in Europe between Orléans, Bremen, Białystok, and Garmisch-Partenkirchen are overestimated by the state-of-the-art inventories of the Emissions Database for Global Atmospheric Research (EDGAR) v4.2 FT2010 and the high-resolution emissions database developed by the Netherlands Organisation for Applied Scientific Research (TNO) as part of the Monitoring Atmospheric Composition and Climate project (TNO-MACC_III), possibly due to the disaggregation of emissions onto a spatial grid. Uncertainties in the carbon monoxide inventories used to compute the methane emissions contribute to the discrepancy between our inferred emissions and those from the inventories

Quantification of carbon monoxide emissions from African cities using TROPOMI

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

A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases

Satellite observations of dry-column methane mixing ratios (XCH4) from shortwave infrared (SWIR) solar backscatter radiation provide a powerful resource to quantify methane emissions in service of climate action. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017, provides global daily coverage at a 5.5 × 7 km2 (nadir) pixel resolution, but its methane retrievals can suffer from biases associated with SWIR surface albedo, scattering from aerosols and cirrus clouds, and across-track variability (striping). The Greenhouse gases Observing SATellite (GOSAT) instrument, launched in 2009, has better spectral characteristics and its methane retrieval is much less subject to biases, but its data density is 250 times sparser than TROPOMI. Here, we present a blended TROPOMI+GOSAT methane product obtained by training a machine learning (ML) model to predict the difference between TROPOMI and GOSAT co-located measurements, using only predictor variables included in the TROPOMI retrieval, and then applying the correction to the complete TROPOMI record from April 2018 to present. We find that the largest corrections are associated with coarse aerosol particles, high SWIR surface albedo, and across-track pixel index. Our blended product corrects a systematic difference between TROPOMI and GOSAT over water, and it features corrections exceeding 10 ppb over arid land, persistently cloudy regions, and high northern latitudes. It reduces the TROPOMI spatially variable bias over land (referenced to GOSAT data) from 14.3 to 10.4 ppb at a 0.25∘ × 0.3125∘ resolution. Validation with Total Carbon Column Observing Network (TCCON) ground-based column measurements shows reductions in variable bias compared with the original TROPOMI data from 4.7 to 4.4 ppb and in single-retrieval precision from 14.5 to 11.9 ppb. TCCON data are all in locations with a SWIR surface albedo below 0.4 (where TROPOMI biases tend to be relatively low), but they confirm the dependence of TROPOMI biases on SWIR surface albedo and coarse aerosol particles, as well as the reduction of these biases in the blended product. Fine-scale inspection of the Arabian Peninsula shows that a number of hotspots in the original TROPOMI data are removed as artifacts in the blended product. The blended product also corrects striping and aerosol/cloud biases in single-orbit TROPOMI data, enabling better detection and quantification of ultra-emitters. Residual coastal biases can be removed by applying additional filters. The ML method presented here can be applied more generally to validate and correct data from any new satellite instrument by reference to a more established instrument.</p

Satellite observations of atmospheric methane and their value for quantifying methane emissions

Methane is a greenhouse gas emitted by a range of natural and anthropogenic sources. Atmospheric methane has been measured continuously from space since 2003, and new instruments are planned for launch in the near future that will greatly expand the capabilities of space-based observations. We review the value of current, future, and proposed satellite observations to better quantify and understand methane emissions through inverse analyses, from the global scale down to the scale of point sources and in combination with suborbital (surface and aircraft) data. Current global observations from Greenhouse Gases Observing Satellite (GOSAT) are of high quality but have sparse spatial coverage. They can quantify methane emissions on a regional scale (100–1000 km) through multiyear averaging. The Tropospheric Monitoring Instrument (TROPOMI), to be launched in 2017, is expected to quantify daily emissions on the regional scale and will also effectively detect large point sources. A different observing strategy by GHGSat (launched in June 2016) is to target limited viewing domains with very fine pixel resolution in order to detect a wide range of methane point sources. Geostationary observation of methane, still in the proposal stage, will have the unique capability of mapping source regions with high resolution, detecting transient "super-emitter" point sources and resolving diurnal variation of emissions from sources such as wetlands and manure. Exploiting these rapidly expanding satellite measurement capabilities to quantify methane emissions requires a parallel effort to construct high-quality spatially and sectorally resolved emission inventories. Partnership between top-down inverse analyses of atmospheric data and bottom-up construction of emission inventories is crucial to better understanding methane emission processes and subsequently informing climate policy

2010–2016 methane trends over Canada, the United States, and Mexico observed by the GOSAT satellite: contributions from different source sectors

We use 7 years (2010–2016) of methane column observations from the Greenhouse Gases Observing Satellite (GOSAT) to examine trends in atmospheric methane concentrations over North America and infer trends in emissions. Local methane enhancements above background are diagnosed in the GOSAT data on a 0.5° × 0.5° grid by estimating the local background as the low (10th–25th) percentiles of the deseasonalized frequency distributions of the data for individual years. Trends in methane enhancements on the 0.5° × 0.5° grid are then aggregated nationally and for individual source sectors, using information from state-of-science bottom-up inventories. We find that US methane emissions increased by 2.5±1.4  %&thinsp;a−1 (mean&thinsp;±&thinsp;1 standard deviation) over the 7-year period, with contributions from both oil–gas systems (possibly unconventional oil–gas production) and from livestock in the Midwest (possibly swine manure management). Mexican emissions show a decrease that can be attributed to a decreasing cattle population. Canadian emissions show year-to-year variability driven by wetland emissions and correlated with wetland areal extent. The US emission trends inferred from the GOSAT data account for about 20&thinsp;% of the observed increase in global methane over the 2010–2016 period.</p

Sustained methane emissions from China after 2012 despite declining coal production and rice-cultivated area

China’s anthropogenic methane emissions are the largest of any country in the world. A recent study using atmospheric observations suggested that recent policies aimed at reducing emissions of methane due to coal production in China after 2010 had been largely ineffective. Here, based on a longer observational record and an updated modelling approach, we find a statistically significant positive linear trend (0.36 ± 0.04 ( $\pm1\sigma$ ) Tg CH _4 yr ^−2 ) in China’s methane emissions for 2010–2017. This trend was slowing down at a statistically significant rate of -0.1 ± 0.04 Tg CH _4 yr ^−3 . We find that this decrease in growth rate can in part be attributed to a decline in China’s coal production. However, coal mine methane emissions have not declined as rapidly as production, implying that there may be substantial fugitive emissions from abandoned coal mines that have previously been overlooked. We also find that emissions over rice-growing and aquaculture-farming regions show a positive trend (0.13 ± 0.05 Tg CH _4 yr ^−2 for 2010–2017) despite reports of shrinking rice paddy areas, implying potentially significant emissions from new aquaculture activities, which are thought to be primarily located on converted rice paddies