scholarly journals Quantifying methane point sources from fine-scale (GHGSat) satellite observations of atmospheric methane plumes

2018 ◽  
Author(s):  
Daniel J. Varon ◽  
Daniel J. Jacob ◽  
Jason McKeever ◽  
Dylan Jervis ◽  
Berke O. A. Durak ◽  
...  
Keyword(s):  
Wind Speed ◽  
Methane Emissions ◽  
Point Sources ◽  
Small Scale ◽  
Atmospheric Methane ◽  
Flux Method ◽  
Pixel Resolution ◽  
Cross Sectional ◽  
Source Rate ◽  
The Cross

Abstract. Anthropogenic methane emissions originate from a large number of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected ~ 10 × 10 km2 domains with ≤ 50 × 50 m2 pixel resolution and 1–5 % measurement precision. Here we develop algorithms for retrieving point source rates from such measurements. We simulate a large ensemble of instantaneous methane column plumes at 50 × 50 m2 pixel resolution for a range of atmospheric conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that standard methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10-m wind speed can infer source rates with error of 0.07–0.17 t h−1 + 5–12 % depending on instrument precision (1–5 %). The cross-sectional flux method has slightly larger errors (0.07–0.26 t h−1 + 8–12 %) but a simpler physical basis. For comparison, point sources larger than 0.5 t h−1 contribute more than 75 % of methane emissions reported to the U.S. Greenhouse Gas Reporting Program. Additional error applies if local wind speed measurements are not available, and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but not for source quantification.

scholarly journals Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes

2018 ◽  
Vol 11 (10) ◽  
pp. 5673-5686 ◽  
Author(s):  
Daniel J. Varon ◽  
Daniel J. Jacob ◽  
Jason McKeever ◽  
Dylan Jervis ◽  
Berke O. A. Durak ◽  
...  
Keyword(s):  
Wind Speed ◽  
Methane Emissions ◽  
Point Sources ◽  
Small Scale ◽  
Atmospheric Methane ◽  
Flux Method ◽  
Pixel Resolution ◽  
Cross Sectional ◽  
Source Rate ◽  
The Cross

Abstract. Anthropogenic methane emissions originate from a large number of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected ∼10×10 km2 domains with ≤50×50 m2 pixel resolution and 1 %–5 % measurement precision. Here we develop algorithms for retrieving point source rates from such measurements. We simulate a large ensemble of instantaneous methane column plumes at 50×50 m2 pixel resolution for a range of atmospheric conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that standard methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10 m wind speed can infer source rates with an error of 0.07–0.17 t h-1+5 %–12 % depending on instrument precision (1 %–5 %). The cross-sectional flux method has slightly larger errors (0.07–0.26 t h-1+8 %–12 %) but a simpler physical basis. For comparison, point sources larger than 0.3 t h−1 contribute more than 75 % of methane emissions reported to the US Greenhouse Gas Reporting Program. Additional error applies if local wind speed measurements are not available and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but detrimental for source quantification.

scholarly journals Determination of the emission rates of CO<sub>2</sub> point sources with airborne lidar

2021 ◽  
Vol 14 (4) ◽  
pp. 2717-2736
Author(s):  
Sebastian Wolff ◽  
Gerhard Ehret ◽  
Christoph Kiemle ◽  
Axel Amediek ◽  
Mathieu Quatrevalet ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Power Plants ◽  
Point Sources ◽  
Airborne Lidar ◽  
Observation System ◽  
Emission Rates ◽  
Flux Method ◽  
Cross Sectional ◽  
Mixing Ratios ◽  
The Cross

Abstract. Anthropogenic point sources, such as coal-fired power plants, produce a major share of global CO2 emissions. International climate agreements demand their independent monitoring. Due to the large number of point sources and their global spatial distribution, the implementation of a satellite-based observation system is convenient. Airborne active remote sensing measurements demonstrate that the deployment of lidar is promising in this respect. The integrated path differential absorption lidar CHARM-F is installed on board an aircraft in order to detect weighted column-integrated dry-air mixing ratios of CO2 below the aircraft along its flight track. During the Carbon Dioxide and Methane Mission (CoMet) in spring 2018, airborne greenhouse gas measurements were performed, focusing on the major European sources of anthropogenic CO2 emissions, i.e., large coal-fired power plants. The flights were designed to transect isolated exhaust plumes. From the resulting enhancement in the CO2 mixing ratios, emission rates can be derived via the cross-sectional flux method. On average, our results roughly correspond to reported annual emission rates, with wind speed uncertainties being the major source of error. We observe significant variations between individual overflights, ranging up to a factor of 2. We hypothesize that these variations are mostly driven by turbulence. This is confirmed by a high-resolution large eddy simulation that enables us to give a qualitative assessment of the influence of plume inhomogeneity on the cross-sectional flux method. Our findings suggest avoiding periods of strong turbulence, e.g., midday and afternoon. More favorable measurement conditions prevail during nighttime and morning. Since lidars are intrinsically independent of sunlight, they have a significant advantage in this regard.

scholarly journals Determination of the Emission Rates of CO<sub>2</sub> Point Sources with Airborne Lidar

2020 ◽  
Author(s):  
Sebastian Wolff ◽  
Gerhard Ehret ◽  
Christoph Kiemle ◽  
Axel Amediek ◽  
Mathieu Quatrevalet ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Power Plants ◽  
Point Sources ◽  
Airborne Lidar ◽  
Emission Rates ◽  
Flux Method ◽  
Cross Sectional ◽  
Spatial Coverage ◽  
International Climate Agreements ◽  
The Cross

Abstract. Anthropogenic point sources, such as coal-fired power plants, produce a major share of global CO2 emissions. International climate agreements demand their independent monitoring. Due to the high amount of point sources and their global spatial distribution, a mobile measurement approach with fast spatial coverage is needed. Active remote sensing measurements by airborne lidar show much promise in this respect. The integrated-path differential-absorption lidar CHARM–F is installed onboard an aircraft, in order to detect weighted vertical columns of CO2 mixing ratios, below the aircraft along its flight track. During the Carbon Dioxide and Methane mission (CoMet) in spring 2018, airborne greenhouse gas measurements were performed, focusing on the major European sources of anthropogenic CO2 emissions, i.e. large coal–fired power plants. The flights were designed to transect isolated exhaust plumes. From the resulting enhancement in the CO2 mixings ratios, emission rates can be derived in terms of the cross–sectional flux method. On average, we find our results roughly corresponding to reported annual emission rates, but observe significant variations between individual overflights ranging up to a factor of 2. We suppose that these variations are mostly driven by turbulence. This hypothesis is supported by a high–resolution large eddy simulation that enables us to give a qualitative assessment of the influence of plume inhomogeneity on the cross–sectional flux method. Our findings suggest avoiding periods of strong turbulence, e.g. midday and afternoon. More favorable measurement conditions prevail during nighttime and morning. Since lidars are intrinsically independent of sunlight, they have a significant advantage in this regard.

scholarly journals Application of a fast and efficient algorithm to assess landslide-prone areas in sensitive clays in Sweden

2015 ◽  
Vol 15 (12) ◽  
pp. 2703-2713 ◽  
Author(s):  
C. Melchiorre ◽  
A. Tryggvason
Keyword(s):  
Optimal Algorithm ◽  
Model Performance ◽  
Small Scale ◽  
Depth Information ◽  
Computationally Efficient ◽  
Cross Sectional ◽  
Bedrock Depth ◽  
The Cross ◽  
Intelligent Filtering ◽  
Filtering Procedure

Abstract. We refine and test an algorithm for landslide susceptibility assessment in areas with sensitive clays. The algorithm uses soil data and digital elevation models to identify areas which may be prone to landslides and has been applied in Sweden for several years. The algorithm is very computationally efficient and includes an intelligent filtering procedure for identifying and removing small-scale artifacts in the hazard maps produced. Where information on bedrock depth is available, this can be included in the analysis, as can information on several soil-type-based cross-sectional angle thresholds for slip. We evaluate how processing choices such as of filtering parameters, local cross-sectional angle thresholds, and inclusion of bedrock depth information affect model performance. The specific cross-sectional angle thresholds used were derived by analyzing the relationship between landslide scarps and the quick-clay susceptibility index (QCSI). We tested the algorithm in the Göta River valley. Several different verification measures were used to compare results with observed landslides and thereby identify the optimal algorithm parameters. Our results show that even though a relationship between the cross-sectional angle threshold and the QCSI could be established, no significant improvement of the overall modeling performance could be achieved by using these geographically specific, soil-based thresholds. Our results indicate that lowering the cross-sectional angle threshold from 1 : 10 (the general value used in Sweden) to 1 : 13 improves results slightly. We also show that an application of the automatic filtering procedure that removes areas initially classified as prone to landslides not only removes artifacts and makes the maps visually more appealing, but it also improves the model performance.

scholarly journals Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space

2019 ◽  
Author(s):  
Daniel H. Cusworth ◽  
Daniel J. Jacob ◽  
Daniel J. Varon ◽  
Christopher Chan Miller ◽  
Xiong Liu ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
Signal To Noise Ratio ◽  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Global Scale ◽  
Point Sources ◽  
Emission Rates ◽  
Atmospheric Methane ◽  
Pixel Resolution ◽  
Gas Fields

Abstract. We examine the potential for global detection of methane plumes from individual point sources with the new generation of spaceborne imaging spectrometers (EnMAP, PRISMA, EMIT, SBG) scheduled for launch in 2019–2025. These instruments are designed to map the Earth's surface with a sampling distance as fine as 30 × 30 m2 but they have spectral resolution of 7–10 nm in the 2200–2400 nm band that should also allow useful detection of atmospheric methane. We simulate scenes viewed by EnMAP (10 nm spectral resolution, 180 signal-to-noise ratio) using the EnMAP End-to-End Simulation Tool with superimposed methane plumes generated by large-eddy simulations. We retrieve atmospheric methane and surface reflectivity for these scenes using the IMAP-DOAS optimal estimation algorithm. We find an EnMAP precision of 4–13 % for atmospheric methane depending on surface type, allowing effective single-pass detection of 100+ kg h−1 methane point sources depending on surface brightness, surface homogeneity, and wind speed. Successful retrievals over very heterogeneous surfaces such as an urban mosaic require finer spectral resolution. We simulated the EnMAP capability with actual plume observations over oil/gas fields in California from the airborne AVIRIS-NG sensor (3 × 3 m2 pixel resolution, 5 nm spectral resolution, SNR 200–400). We spectrally and spatially downsampled AVIRIS-NG images to match EnMAP instrument specifications and found that we could successfully detect point sources of ~ 100 kg h−1 over bright surfaces. Estimated emission rates inferred with a generic Integrated Mass Enhancement (IME) method agreed within a factor of 2 between EnMAP and AVIRIS-NG. Better agreement may be achieved with a more customized IME method. Our results suggest that imaging spectrometers in space could play a transformative role in the future for quantifying methane emissions from point sources on a global scale.

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

2016 ◽  
Vol 16 (22) ◽  
pp. 14371-14396 ◽  
Author(s):  
Daniel J. Jacob ◽  
Alexander J. Turner ◽  
Joannes D. Maasakkers ◽  
Jianxiong Sheng ◽  
Kang Sun ◽  
...  
Keyword(s):  
Regional Scale ◽  
Methane Emissions ◽  
Point Sources ◽  
Satellite Observations ◽  
Anthropogenic Sources ◽  
Emission Inventories ◽  
Atmospheric Methane ◽  
High Quality ◽  
Spatial Coverage ◽  
Wide Range

Abstract. 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.

scholarly journals Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data

2015 ◽  
Vol 15 (12) ◽  
pp. 7049-7069 ◽  
Author(s):  
A. J. Turner ◽  
D. J. Jacob ◽  
K. J. Wecht ◽  
J. D. Maasakkers ◽  
E. Lundgren ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
North American ◽  
Transport Model ◽  
Central Valley ◽  
Methane Emissions ◽  
Point Sources ◽  
Atmospheric Methane ◽  
Chemical Transport Model ◽  
Error Characterization ◽  
Methane Source

Abstract. We use 2009–2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to estimate global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resolution, respectively. GEOS-Chem and GOSAT data are first evaluated with atmospheric methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chemical transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a−1 with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an analytical inversion of North American methane emissions using radial basis functions to achieve high resolution of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2–42.7 Tg a−1, as compared to 24.9–27.0 Tg a−1 in the EDGAR and EPA bottom-up inventories, and 30.0–44.5 Tg a−1 in recent inverse studies. Our estimate is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern–central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29–44 % of US anthropogenic methane emissions to livestock, 22–31 % to oil/gas, 20 % to landfills/wastewater, and 11–15 % to coal. Wetlands contribute an additional 9.0–10.1 Tg a−1.

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

2016 ◽  
Author(s):  
Daniel J. Jacob ◽  
Alexander J. Turner ◽  
Joannes D. Maasakkers ◽  
Jianxiong Sheng ◽  
Kang Sun ◽  
...  
Keyword(s):  
Methane Emissions ◽  
Point Sources ◽  
Satellite Observations ◽  
Anthropogenic Sources ◽  
Satellite Measurement ◽  
Emission Inventories ◽  
Atmospheric Methane ◽  
High Quality ◽  
Spatial Coverage ◽  
Wide Range

Abstract. 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, down to the scale of point sources and in combination with suborbital (surface and aircraft) data. Current observations from GOSAT are of high quality but have sparse spatial coverage. They provide limited information to quantify methane emissions on a regional (100–1000 km) scale. TROPOMI to be launched in late 2016 is expected to quantify daily emissions on the regional scale and will also effectively detect large point sources. Future satellite instruments with much higher spatial resolution, such as the recently launched GHGSat with 50 × 50 m2 resolution over targeted viewing domains, have the potential to detect a wide range of methane point sources. Geostationary observation of methane, still in the proposal stage, will have unique capability for mapping source regions with high resolution while also detecting transient "super-emitter" point sources. Exploiting the 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 understand methane emission processes and from there to inform climate policy.

scholarly journals A global gridded (0.1° × 0.1°) inventory of methane emissions from oil, gas, and coal exploitation based on national reports to the United Nations Framework Convention on Climate Change

2020 ◽  
Vol 12 (1) ◽  
pp. 563-575 ◽  
Author(s):  
Tia R. Scarpelli ◽  
Daniel J. Jacob ◽  
Joannes D. Maasakkers ◽  
Melissa P. Sulprizio ◽  
Jian-Xiong Sheng ◽  
...  
Keyword(s):  
Climate Change ◽  
United Nations ◽  
Oil And Gas ◽  
Methane Emissions ◽  
Point Sources ◽  
Atmospheric Methane ◽  
United Nations Framework Convention ◽  
Oil Refineries ◽  
The United Nations ◽  
Oil Gas

Abstract. Individual countries report national emissions of methane, a potent greenhouse gas, in accordance with the United Nations Framework Convention on Climate Change (UNFCCC). We present a global inventory of methane emissions from oil, gas, and coal exploitation that spatially allocates the national emissions reported to the UNFCCC (Scarpelli et al., 2019). Our inventory is at 0.1∘×0.1∘ resolution and resolves the subsectors of oil and gas exploitation, from upstream to downstream, and the different emission processes (leakage, venting, flaring). Global emissions for 2016 are 41.5 Tg a−1 for oil, 24.4 Tg a−1 for gas, and 31.3 Tg a−1 for coal. An array of databases is used to spatially allocate national emissions to infrastructure, including wells, pipelines, oil refineries, gas processing plants, gas compressor stations, gas storage facilities, and coal mines. Gridded error estimates are provided in normal and lognormal forms based on emission factor uncertainties from the IPCC. Our inventory shows large differences with the EDGAR v4.3.2 global gridded inventory both at the national scale and in finer-scale spatial allocation. It shows good agreement with the gridded version of the United Kingdom's National Atmospheric Emissions Inventory (NAEI). There are significant errors on the 0.1∘×0.1∘ grid associated with the location and magnitude of large point sources, but these are smoothed out when averaging the inventory over a coarser grid. Use of our inventory as prior estimate in inverse analyses of atmospheric methane observations allows investigation of individual subsector contributions and can serve policy needs by evaluating the national emissions totals reported to the UNFCCC. Gridded data sets can be accessed at https://doi.org/10.7910/DVN/HH4EUM (Scarpelli et al., 2019).

scholarly journals 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

2018 ◽  
Vol 11 (12) ◽  
pp. 6379-6388 ◽  
Author(s):  
Jian-Xiong Sheng ◽  
Daniel J. Jacob ◽  
Joannes D. Maasakkers ◽  
Yuzhong Zhang ◽  
Melissa P. Sulprizio
Keyword(s):  
Cloud Cover ◽  
Transport Model ◽  
National Research Council ◽  
Methane Emissions ◽  
Low Earth Orbit ◽  
Total Carbon ◽  
Atmospheric Methane ◽  
Pixel Resolution ◽  
Error Correlation ◽  
Southeast Us

Abstract. 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 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 %–91 % 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 ppb, larger than the instrument errors when aggregated on the 25 km model grid scale, and with a temporal error correlation of 6 h. We find that TROPOMI (7×7 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 %–25 % 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 % of the information from the nominal GEO-CAPE mission preformulated by NASA in response to the Decadal Survey of the US National Research Council.

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