Probabilities of Detection of Methane Plumes by Remote Sensing and Implications for Inferred Emissions Distributions

Abstract. Strategies for mitigating methane emissions rely on understanding the underlying drivers of methane losses to the atmosphere. Observations of methane plumes emerging from point sources, combined with correct statistical interpretation, can provide key information. In this work, we examine a critical parameter, the probability of detection of a plume. For a given observing system, probability of detection is affected by the properties of the sensor, plume detection algorithm, observing conditions, and emission rate of the source. We parameterize relevant aspects of remotely sensed scenes containing plumes using a nondimensional observability parameter that predicts probability of detection. Our probability of detection model is trained using simulated plumes to capture natural variability in different meteorological conditions, and validated with data from controlled release experiments. We model probability of detection for two airborne imaging spectrometer systems, MethaneAIR and Insight M LeakSurveyorTM, and one high resolution satellite system, MethaneSAT. Monte Carlo simulations of emissions distributions implied by data from the extensive 2023 MAIRX campaign of MethaneAIR demonstrate the importance of an accurate probability of detection model, due to the heavy tailed emission distribution found in most oil and gas basins.