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

Development of a high-resolution prior for inverse modelling of New York City methane emissions

Joseph Pitt1, Israel Lopez-Coto2, Kris Hajny1,3, Jay Tomlin3, Robert Kaeser3, Thilina Jayarathne3, Brian Stirm4, Cody Floerchinger5, Chris Loughner6, Róisín Commane7, Conor Gately8, Lucy Hutyra9, Kevin Gurney10, Geoffrey Roest10, Jianming Liang11, Anna Karion2, James Whetstone2, and Paul Shepson1,3
Joseph Pitt et al.
  • 1School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, US (
  • 2National Institute of Standards and Technology, Gaithersburg, MD, US
  • 3Department of Chemistry, Purdue University, West Lafayette, IN, US
  • 4School of Aviation and Transportation Technology, Purdue University, West Lafayette, IN, US
  • 5Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, US
  • 6Air Resources Laboratory, NOAA, College Park, MD, US
  • 7Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, US
  • 8Metropolitan Area Planning Council, Boston, MA, US
  • 9Department of Earth and Environment, Boston University, Boston, MA, US
  • 10School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ, US
  • 11Environmental Systems Research Institute, Redlands, CA, US

Recent studies have shown that methane emissions are underestimated by inventories in many US urban areas. This has important implications for climate change mitigation policy at the city, state and national level. Uncertainty in both the spatial distribution and sectoral allocation of urban emissions can limit the ability of policy makers to develop well-targeted emission reductions strategies. Top-down emission estimates based on atmospheric greenhouse gas measurements can help to improve inventories and better inform policy decisions.

This presentation builds on previous work estimating methane emissions from New York City and the wider urban area based on measurements taken during nine research flights. We used an ensemble of dispersion model runs in a Bayesian inverse modelling framework to derive posterior emission estimates. Prior emissions were taken from three coarse-resolution inventories based on spatially disaggregated national totals. The most recent version of EDGAR (v5) and the gridded EPA inventory both required upscaling by more than a factor of two to be consistent with our measurements.

Here, we construct a high-resolution methane emission prior using a combination of spatial proxies and reported emissions for various sectors. We present preliminary results evaluating the ability of this new prior to represent the magnitude and spatial distribution of emissions, through comparison with both the measured data and results obtained using coarser resolution inventories.

How to cite: Pitt, J., Lopez-Coto, I., Hajny, K., Tomlin, J., Kaeser, R., Jayarathne, T., Stirm, B., Floerchinger, C., Loughner, C., Commane, R., Gately, C., Hutyra, L., Gurney, K., Roest, G., Liang, J., Karion, A., Whetstone, J., and Shepson, P.: Development of a high-resolution prior for inverse modelling of New York City methane emissions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13828,, 2021.

Corresponding presentation materials formerly uploaded have been withdrawn.