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

Abstract

Abstract. Methane, the second most important anthropogenic greenhouse gas, has a global warming potential more than 80 times that of carbon dioxide over a 20-year period. Given its decadal atmospheric lifetime, reducing anthropogenic methane emissions is critical for limiting near-term warming. The TROPOspheric Monitoring Instrument (TROPOMI) provides daily global methane satellite observations, enabling rapid detection of super-emitters. Here, we develop ML-SPERE, a machine-learning framework based on a convolutional neural network trained on simulated TROPOMI methane observations and meteorological data to estimate emission rates for super-emitters. ML-SPERE outperforms the Integrated Mass Enhancement (IME) method on simulated plumes that incorporate real TROPOMI backgrounds and missing spatial data, reducing the median absolute percentage error from 42.4 % to 24.3 % for well-observed methane plumes. At low wind speeds, IME estimates exhibit a negative bias and ML-SPERE estimates do not. Applied to TROPOMI observations of a 200-d well blowout in Kazakhstan, ML-SPERE typically shows agreement with TROPOMI inverse modeling results and TROPOMI IME estimates. Compared to TROPOMI IME estimates, ML-SPERE estimates show improved agreement with IME estimates derived from high-resolution point-source imagers. Global spatial patterns of methane emissions inferred from ML-SPERE and the IME method for all super-emitters found by TROPOMI in 2021 are broadly consistent, with notable regional differences in northern Russia (where transient pipeline emissions may not be well characterized by either method), the Congo Basin (where area source emissions may not be well characterized by either method), and southeastern Australia (where IME estimates are potentially negatively biased owing to predominantly low wind speeds). Mean estimated emission rates for this dataset aggregated by estimated source sector remain similar between both methods. Overall, improved performance on simulated plumes and consistency with independent estimates for real-world observations demonstrate the utility of ML-SPERE for quantifying TROPOMI methane super-emitters.