Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 17
Articles | Volume 19, issue 17
https://doi.org/10.5194/amt-19-5659-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-5659-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 17
Research article
 | 
08 Sep 2026
Research article |  | 08 Sep 2026

Machine learning-based emission rate estimates of global methane super-emissions

Clayton Roberts, Joannes D. Maasakkers, Tobias A. de Jong, Berend J. Schuit, Shubham Sharma, Theo Huegens, Anne-Wil van den Berg, Sander Houweling, and Ilse Aben

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Short summary
Methane is a powerful greenhouse gas, and cutting its emissions can slow climate change in the near term. We have created ML-SPERE, a new machine-learning based method that uses TROPOMI observations and weather data to better estimate emission rates for large methane plumes. It has proved more accurate than the Integrated Mass Enhancement (IME) method when tested on simulated satellite observations. When applied to real TROPOMI plumes, ML-SPERE produces estimates that agree with other methods.
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