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

Jacob, Daniel; Turner, Alexander J.; Maasakkers, Joannes D.; Sheng, Jianxiong; Sun, Kang; Liu, Xiong; Chance, K.; Aben, I.; McKeever, J.; Frankenberg, Christian

doi:10.5194/acp-2016-555

Cited by 38 publications

(57 citation statements)

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“…Alexe et al, 2015;Bergamaschi et al, 2007Bergamaschi et al, , 2009Bergamaschi et al, , 2013Bloom et al, 2010;Turner et al, 2015Turner et al, , 2016Fraser et al, 2013;Monteil et al, 2013;Cressot et al, 2014;Wecht et al, 2014a, b;Kort et al, 2014;Jacob et al, 2016;Houweling et al, 2017). Nevertheless, there are still many important aspects which need further investigation.…”

Section: Introductionmentioning

confidence: 99%

“…However, our current knowledge about the various natural and anthropogenic methane sources and sinks is inadequate (e.g. Rigby et al, 2008;Dlugokencky et al, 2009;IPCC, 2013;Kirschke et al, 2013;Houweling et al, , 2017Nisbet et al, 2014;Jeong et al, 2014;Alexe et al, 2015;Jacob et al, 2016;Schaefer et al, 2016;Miller and Michalak et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Satellite-derived methane hotspot emission estimates using a fast data-driven method

et al. 2017

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Abstract. 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 columnaveraged dry-air mole fractions of atmospheric methane, i.e. XCH 4 , can be used to quantify methane emissions. Maps of time-averaged satellite-derived XCH 4 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 1σ uncertainties directly from maps of annually averaged satellite XCH 4 . 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 XCH 4 data products consisting of two products from SCIAMACHY/ENVISAT and two products from TANSO-FTS/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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Satellite-derived methane hotspot emission estimates using a fast data-driven method

et al. 2017

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“…Inverse analyses have used observations from these satellitebased instruments to estimate methane emissions at ∼ 100-1000 km spatial resolution (e.g., Bergamaschi et al, 2009Bergamaschi et al, , 2013Fraser et al, 2013;Monteil et al, 2013;Wecht et al, 2014a;Cressot et al, 2014;Kort et al, 2014;Turner et al, 2016a;Alexe et al, 2015;Tan et al, 2016;Buchwitz et al, 2017;Sheng et al, 2018a, b). But such coarse resolution makes it difficult to resolve individual source types because of spatial overlap (Maasakkers et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales

et al. 2018

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Abstract. Anthropogenic methane emissions originate from a large number of fine-scale and often transient point sources. Satellite observations of atmospheric methane columns are an attractive approach for monitoring these emissions but have limitations from instrument precision, pixel resolution, and measurement frequency. Dense observations will soon be available in both low-Earth and geostationary orbits, but the extent to which they can provide fine-scale information on methane sources has yet to be explored. Here we present an observation system simulation experiment (OSSE) to assess the capabilities of different satellite observing system configurations. We conduct a 1-week WRF-STILT simulation to generate methane column footprints at 1.3 × 1.3 km 2 spatial resolution and hourly temporal resolution over a 290 × 235 km 2 domain in the Barnett Shale, a major oil and gas field in Texas with a large number of point sources. We sub-sample these footprints to match the observing characteristics of the recently launched TROPOMI instrument (7 × 7 km 2 pixels, 11 ppb precision, daily frequency), the planned GeoCARB instrument (2.7 × 3.0 km 2 pixels, 4 ppb precision, nominal twice-daily frequency), and other proposed observing configurations. The information content of the various observing systems is evaluated using the Fisher information matrix and its eigenvalues. We find that a week of TROPOMI observations should provide information on temporally invariant emissions at ∼ 30 km spatial resolution. GeoCARB should provide information available on temporally invariant emissions ∼ 2-7 km spatial resolution depending on sampling frequency (hourly to daily). Improvements to the instrument precision yield greater increases in information content than improved sampling frequency. A precision better than 6 ppb is critical for GeoCARB to achieve fine resolution of emissions. Transient emissions would be missed with either TROPOMI or GeoCARB. An aspirational highresolution geostationary instrument with 1.3 × 1.3 km 2 pixel resolution, hourly return time, and 1 ppb precision would effectively constrain the temporally invariant emissions in the Barnett Shale at the kilometer scale and provide some information on hourly variability of sources.

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“…Despite their importance, point sources are sparsely sampled by the ground-based network which calls for more measurements, whereby satellite remote sensors offer global cover. Several comprehensive studies assess emissions from localized CO 2 and CH 4 sources using passive remote sensors [11][12][13][14]. Recently, the Carbon Monitoring Satellite (CarbonSat) was proposed to the European Space Agency (ESA) to retrieve city emissions globally [15].…”

Section: Introductionmentioning

confidence: 99%

Potential of Spaceborne Lidar Measurements of Carbon Dioxide and Methane Emissions from Strong Point Sources

et al. 2017

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Abstract:Emissions from strong point sources, primarily large power plants, are a major portion of the total CO 2 emissions. International climate agreements will increasingly require their independent monitoring. A satellite-based, double-pulse, direct detection Integrated Path Differential Absorption (IPDA) Lidar with the capability to actively target point sources has the potential to usefully complement the current and future GHG observing system. This initial study uses simple approaches to determine the required Lidar characteristics and the expected skill of spaceborne Lidar plume detection and emission quantification. A Gaussian plume model simulates the CO 2 or CH 4 distribution downstream of the sources. A Lidar simulator provides the instrument characteristics and dimensions required to retrieve the emission rates, assuming an ideal detector configuration. The Lidar sampling frequency, the footprint distance to the emitting source and the error of an individual measurement are of great importance. If wind speed and direction are known and environmental conditions are ideal, an IPDA Lidar on a 500-km orbit with 2 W average power in the 1.6 µm CO 2 absorption band, 500 Hz pulse repetition frequency, 50 m footprint at sea level and 0.7 m telescope diameter can be expected to measure CO 2 emission rates of 20 Mt/a with an average accuracy better than 3% up to a distance of 3 km away from the source. CH 4 point source emission rates can be quantified with comparable skill if they are larger than 10 kt/a, or if the Lidar pulse repetition frequency is augmented.

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

Cited by 38 publications

References 0 publications

Satellite-derived methane hotspot emission estimates using a fast data-driven method

Satellite-derived methane hotspot emission estimates using a fast data-driven method

Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales

Potential of Spaceborne Lidar Measurements of Carbon Dioxide and Methane Emissions from Strong Point Sources

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