Preprints
https://doi.org/10.5194/gmd-2022-45
https://doi.org/10.5194/gmd-2022-45
Submitted as: model description paper
02 Mar 2022
Submitted as: model description paper | 02 Mar 2022
Status: this preprint is currently under review for the journal GMD.

Integrated Methane Inversion (IMI 1.0): A user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations

Daniel J. Varon1, Daniel J. Jacob1, Melissa Sulprizio1, Lucas Estrada1, William B. Downs1, Lu Shen2, Sarah E. Hancock1, Hannah Nesser1, Zhen Qu1, Elise Penn1, Zichong Chen1, Xiao Lu3, Alba Lorente4, Ashutosh Tewari5, and Cynthia A. Randles5 Daniel J. Varon et al.
  • 1Harvard University, Cambridge, Massachusetts, United States
  • 2Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
  • 3Sun Yat-Sen University, Guangzhou, China
  • 4SRON Netherlands Institute for Space Research
  • 5ExxonMobil Research and Engineering Company, Annandale, New Jersey, United States

Abstract. We present a user-friendly, cloud-based facility for quantifying methane emissions with 0.25° × 0.3125° (≈ 25 × 25 km2) resolution by inversion of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI). The facility is built on an Integrated Methane Inversion optimal estimation workflow (IMI 1.0) and supported for use on the Amazon Web Services (AWS) cloud. It exploits the GEOS-Chem chemical transport model and TROPOMI data already resident on AWS, thus avoiding cumbersome big-data download. Users select a region and period of interest, and the IMI returns an analytical solution for the Bayesian optimal estimate of emissions on the 0.25° × 0.3125° grid including error statistics, information content, and visualization code for inspection of results. An out-of-the-box inversion with rectilinear grid and default prior emission estimates can be conducted with no significant learning curve. Users can also configure their inversions to infer emissions for irregular regions of interest, swap in their own prior emission inventories, and modify inversion parameters. Inversion ensembles can be generated at minimal additional cost once the Jacobian matrix for the analytical inversion has been constructed. A preview feature allows users to determine the TROPOMI information content for their region and time period of interest before actually performing the inversion. The IMI is heavily documented and is intended to be accessible by researchers and stakeholders with no expertise in inverse modelling or high-performance computing. We demonstrate the IMI’s capabilities by applying it to estimate methane emissions from the US oil-producing Permian Basin in May 2018.

Daniel J. Varon et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-45', Anonymous Referee #1, 30 Mar 2022
  • RC2: 'Comment on gmd-2022-45', Christian Frankenberg, 02 May 2022

Daniel J. Varon et al.

Model code and software

Integrated Methane Inversion (IMI) beta release - Source code Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles https://doi.org/10.5281/zenodo.6081934

Daniel J. Varon et al.

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Short summary
Reducing atmospheric methane emissions is critical to abating near-term climate change. Global-surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to easily map methane emissions across user-selected regions of interest using TROPOMI satellite observations.