Probabilities of detection of methane plumes by remote sensing and implications for inferred emissions distributions

Strategies for mitigating methane emissions rely on understanding the underlying drivers of methane emissions 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 LeakSurveyor™, 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 to analyze observations from systems with higher emission rate thresholds for detection, due to the heavy tailed emission rate distribution found in most North American oil and gas basins. Additionally, we find that the frequency of rare, high impact emission events drives high uncertainty in distributions of total emissions by emission rate.