Detecting high-emitting methane sources in oil/gas fields using satellite observations. Cusworth, D. H., Jacob, D. J., Sheng, J., Benmergui, J., Turner, A. J., Brandman, J., White, L., & Randles, C. A. Atmospheric Chemistry and Physics, 18(23):16885–16896, November, 2018.
Detecting high-emitting methane sources in oil/gas fields using satellite observations [link]Paper  doi  abstract   bibtex   
Abstract. Methane emissions from oil/gas fields originate from a large number of relatively small and densely clustered point sources. A small fraction of high-mode emitters can make a large contribution to the total methane emission. Here we conduct observation system simulation experiments (OSSEs) to examine the potential of recently launched or planned satellites to detect and locate these high-mode emitters through measurements of atmospheric methane columns. We simulate atmospheric methane over a generic oil/gas field (20–500 production sites of different size categories in a 50×50 km2 domain) for a 1-week period using the WRF-STILT meteorological model with 1.3×1.3 km2 horizontal resolution. The simulations consider many random realizations for the occurrence and distribution of high-mode emitters in the field by sampling bimodal probability density functions (PDFs) of emissions from individual sites. The atmospheric methane fields for each realization are observed virtually with different satellite and surface observing configurations. Column methane enhancements observed from satellites are small relative to instrument precision, even for high-mode emitters, so an inverse analysis is necessary. We compare L1 and L2 regularizations and show that L1 regularization effectively provides sparse solutions for a bimodally distributed variable and enables the retrieval of high-mode emitters. We find that the recently launched TROPOMI instrument (low Earth orbit, 7×7 km2 nadir pixels, daily return time) and the planned GeoCARB instrument (geostationary orbit, 2.7×3.0 km2 pixels, 2 times or 4 times per day return times) are successful (\textgreater 80 % detection rate, \textless 20 % false alarm rate) at locating high-emitting sources for fields of 20–50 emitters within the 50×50 km2 domain as long as skies are clear. They are unsuccessful for denser fields. GeoCARB does not benefit significantly from more frequent observations (4 times per day vs. 2 times per day) because of a temporal error correlation in the inversion, unless under partly cloudy conditions where more frequent observation increases the probability of clear sky. It becomes marginally successful when allowing a 5 km error tolerance for localization. A next-generation geostationary satellite instrument with 1.3×1.3 km2 pixels, hourly return time, and 1 ppb precision can successfully detect and locate the high-mode emitters for a dense field with up to 500 sites in the 50×50 km2 domain. The capabilities of TROPOMI and GeoCARB can be usefully augmented with a surface air observation network of 5–20 sites, and in turn the satellite instruments increase the detection capability that can be achieved from the surface sites alone.
@article{cusworth_detecting_2018,
	title = {Detecting high-emitting methane sources in oil/gas fields using satellite observations},
	volume = {18},
	issn = {1680-7324},
	url = {https://acp.copernicus.org/articles/18/16885/2018/},
	doi = {10.5194/acp-18-16885-2018},
	abstract = {Abstract. Methane emissions from oil/gas fields originate from a large
number of relatively small and densely clustered point sources. A small fraction of
high-mode emitters can make a large contribution to the total methane emission. Here we
conduct observation system simulation experiments (OSSEs) to examine the potential of
recently launched or planned satellites to detect and locate these high-mode emitters
through measurements of atmospheric methane columns. We simulate atmospheric methane over
a generic oil/gas field (20–500 production sites of different size categories in a 50×50 km2 domain) for a 1-week period using the WRF-STILT meteorological model
with 1.3×1.3 km2 horizontal resolution. The
simulations consider many random realizations for the occurrence and distribution of
high-mode emitters in the field by sampling bimodal probability density functions (PDFs)
of emissions from individual sites. The atmospheric methane fields for each realization
are observed virtually with different satellite and surface observing configurations.
Column methane enhancements observed from satellites are small relative to instrument
precision, even for high-mode emitters, so an inverse analysis is necessary. We compare
L1 and L2 regularizations and show that L1 regularization effectively
provides sparse solutions for a bimodally distributed variable and enables the retrieval
of high-mode emitters. We find that the recently launched TROPOMI instrument (low Earth
orbit, 7×7 km2 nadir pixels, daily return time) and the planned GeoCARB
instrument (geostationary orbit, 2.7×3.0 km2 pixels, 2 times or 4 times
per day return times) are successful ({\textgreater} 80 \% detection rate,
{\textless} 20 \% false alarm rate) at locating high-emitting sources for fields of
20–50 emitters within the 50×50 km2 domain as long as skies are clear.
They are unsuccessful for denser fields. GeoCARB does not benefit significantly from more
frequent observations (4 times per day vs. 2 times per day) because of a temporal error
correlation in the inversion, unless under partly cloudy conditions where more frequent
observation increases the probability of clear sky. It becomes marginally successful when
allowing a 5 km error tolerance for localization. A next-generation geostationary
satellite instrument with 1.3×1.3 km2 pixels, hourly return time, and
1 ppb precision can successfully detect and locate the high-mode emitters for a dense
field with up to 500 sites in the 50×50 km2 domain. The capabilities of
TROPOMI and GeoCARB can be usefully augmented with a surface air observation network of
5–20 sites, and in turn the satellite instruments increase the detection capability that
can be achieved from the surface sites alone.},
	language = {en},
	number = {23},
	urldate = {2020-09-28},
	journal = {Atmospheric Chemistry and Physics},
	author = {Cusworth, Daniel H. and Jacob, Daniel J. and Sheng, Jian-Xiong and Benmergui, Joshua and Turner, Alexander J. and Brandman, Jeremy and White, Laurent and Randles, Cynthia A.},
	month = nov,
	year = {2018},
	pages = {16885--16896},
}
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