EGU General Assembly 2021
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methane emission estimate using ground based remote sensing in complex terrain

Friedrich Klappenbach1, Jia Chen1, Adri Wenzel1, Andreas Forstmaier1, Florian Dietrich1, Xinxu Zhao1, Taylor Jones2,3, Jonathan Franklin2, Steven Wofsy2, Matthias Frey4,5, Frank Hase5, Jacob Hedelius6, Paul Wennberg6, Ronald Cohen7, and Marc Fischer8
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 of America
  • 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
  • 8Air Quality Research Center, University of California, Davis, 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. In this work an adopted version of a Bayesian inversion framework [1] will be presented. Methane emissions are derived from the 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.  

The framework uses the STILT generated footprints, which represent the surface-interaction of an air-parcel on its trajectory to the measurement site and thus describe the sensitivity of the measured concentration at a certain location to its surrounding source emissions. The dot product of the footprint matrix with a gridded emission inventory matrix results in expected concentration enhancements at the measurement site as a prior estimate. Here, we use the 1km-gridded local methane inventory by the Bay Area Air Quality Management District (BAAQMD).

Due to the long-term stability of methane, the air parcel holds a non-zero background concentration, which is not negligible. This poses a major challenge in the inversion. The existing Bayesian framework constrains a background concentration as well as a scaling factor for the inventory from the measurements. Within the existing framework, the assumption is made that all instruments eventually experience the same, time dependent background concentration. This assumption holds well for flat terrain with undisturbed wind-fields.

However, in the presence of complex topography, such as San Francisco Bay Area, the background source regions may differ significantly for the individual measurement sites. Here, we present an approach to account for differing background concentrations seen by multiple measurement sites:

The adopted inversion allows to have individual background concentrations for each measurement site. This is strongly constrained by background covariances, which represent the background in common with the remaining measurement sites. These covariances are calculated from the STILT trajectories.

[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. Discuss. [preprint],, in review, 2021.

How to cite: Klappenbach, F., Chen, J., Wenzel, A., Forstmaier, A., Dietrich, F., Zhao, X., Jones, T., Franklin, J., Wofsy, S., Frey, M., Hase, F., Hedelius, J., Wennberg, P., Cohen, R., and Fischer, M.: Methane emission estimate using ground based remote sensing in complex terrain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15406,, 2021.

Corresponding presentation materials formerly uploaded have been withdrawn.