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

Abstract

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 10 selected ~10×10 km 2 domains with ≤ 50×50 m 2 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 m 2 pixel resolution for a range of atmospheric conditions using the Weather Research andForecasting 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 15 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 20 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.

Methane Mitigation: Methods to Reduce Emissions, on the Path to the Paris Agreement

“…MethaneSAT, with high spatial resolution and very high precision, is designed to monitor emissions from about 50 oil and gas production regions accounting for about 80% of global production. It will measure methane and carbon dioxide over a ~200km wide swathe (Wofsy & Hamburg, ).The high‐resolution GHGSat microsatellites designed for greenhouse gas detection, observing methane columns over selected 10 × 10‐km locations, give high effective pixel resolution of 50 × 50 m and 1–5% precision (Jacob et al, ; Varon et al, ). Next‐generation versions with excellent spatial resolution and improvement in point source detection better than ~10 kt/yr (0.01 Tg/yr) should be valuable where likely emission loci are already known, for example, in identifying superemitters in gasfields, and in targeting locations of major leaks on gas industry sites.…”

Detecting high-emitting methane sources in oil/gas fields using satellite observations

“…An important consideration in the interpretation of satellite observations is that methane column enhancements from point sources are typically small relative to instrument precision, even in the high-emitting mode. This reflects the general problem of methane emissions originating from a large number of relatively small point sources (Jacob et al, 2016;Varon et al, 2018). Figure 4 shows the pixel-resolved distribution of atmospheric methane column enhancements above the background for a single pass of the different satellite instruments sampling the emission field of Figure 2.…”

“…OSSEs have shown the potential for TROPOMI and GeoCARB to effectively constrain emissions at the 25-100 km scale without the multiyear averaging required by SCIAMACHY and GOSAT (Wecht et al, 2014b;Sheng et al, 2018a). Other OSSEs have examined the potential for satellites to quantify large point sources from plume observations (Buchwitz et al, 2013;Rayner et al, 2014;Varon et al, 2018). A recent study by Turner et al (2018) evaluated the capability of TROPOMI and GeoCARB to quantify emissions in the Barnett Shale down to the kilometer scale for a 1-week observing period.…”

A methane monitoring station siting method based on WRF-STILT and genetic algorithm

“…Varon et al used methane observation data from GHGSAT and TROPOMI to identify super-emitters of methane in the Central Asian oil fields. These smaller emission sources, though in smaller quantities, contribute to over 60% of the total emissions (Varon et al, 2018;Varon et al, 2019). Although high-resolution methane concentration anomalies have made significant progress in identifying and quantifying superemitters of methane (Pei et al, 2023a), emissions from oil and gas sources exhibit notable temporality (Shi et al, 2022;Shi et al, 2023a).…”