EGU22-10604, updated on 28 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Novel methane emission estimation method for ground based remote sensing networks

Friedrich Klappenbach1, Jia Chen1, Adrian Wenzel1, Florian Dietrich1, Andreas Forstermeier1, Xinxu Zhao1, Taylor Jones2,3, Jonathan Franklin2, Steven Wofsy2, Matthias Frey4,5, Frank Hase5, Jacob Hedelius6, Paul Wennberg6, and Ronald Cohen7
Friedrich Klappenbach et al.
  • 1Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany (
  • 2School of Engineering and Applied Sciences, Harvard University, Boston, United States
  • 3Earth and Environment, Boston University, Boston, United States
  • 4National Institute for Environmental Studies, Tsukuba, Japan
  • 5Institue of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 6Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, United States of America
  • 7Department of Chemistry, University of California, Berkeley, California, United States of America

In order to infer greenhouse gas emissions from a source region, several top-down approaches can confirm or constrain the existing emission inventories.

Due to the long-term stability of methane, the air holds a non-zero background concentration before it enters the domain of interest. This background concentration typically cannot be neglected and poses a major challenge in emission estimates from observations.

Inspired by a Bayesian inversion framework [1] which inverts the background concentrations together with the emissions, we will present a novel (non-Bayesian) inversion framework that estimates the background from the observations and derives the emissions from these calculated enhancements.

Background concentrations are estimated using a combination of measurements at multiple upwind sites, similar to mass balance approaches, but in a more sophisticated manner: The observed total column concentrations at the downwind site are considered to be associated with the concentrations at an upwind site if the backward trajectories calculated by STILT pass close to the respective upwind site. In a second step, the derived enhancements are attributed to the surface fluxes using the STILT calculated footprint.

Methane emission estimates are derived from the total column concentrations measured with six EM27/SUN FTIR spectrometers using ground based direct sunlight spectroscopy. The measurement campaign was carried out in the San Francisco Bay Area in 2016. 

[1] Jones, T. S., Franklin, J. E., Chen, J., Dietrich, F., Hajny, K. D., Paetzold, J. C., Wenzel, A., Gately, C., Gottlieb, E., Parker, H., Dubey, M., Hase, F., Shepson, P. B., Mielke, L. H., and Wofsy, S. C.: Assessing Urban Methane Emissions using Column Observing Portable FTIR Spectrometers and a Novel Bayesian Inversion Framework, Atmos. Chem. Phys., 2021.

How to cite: Klappenbach, F., Chen, J., Wenzel, A., Dietrich, F., Forstermeier, A., Zhao, X., Jones, T., Franklin, J., Wofsy, S., Frey, M., Hase, F., Hedelius, J., Wennberg, P., and Cohen, R.: Novel methane emission estimation method for ground based remote sensing networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10604,, 2022.

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