Robust probabilities of detection and quantification uncertainty for aerial methane detection: Examples for three airborne technologies

Thorough characterization of probabilities detection (POD) and quantification uncertainties is fundamentally important to understand the place aerial measurement technologies in alternative means emission limitation (AMEL) or alternate fugitive emissions management programs (Alt-FEMP); monitoring, reporting, verification (MRV) efforts; surveys designed support measurement-based inventories mitigation tracking. This paper presents a robust framework for deriving continuous probability functions uncertainty models example techniques based on controlled release data. Using extensive fully- semi-blinded experiments test Bridger Photonics Inc.'s Gas Mapping LiDAR (GML)™, as well available semi- non-blinded data Kairos LeakSurveyor™ NASA/JPL AVIRIS-NG technologies, POD are derived that enable calculation any given source rate, wind speed, flight altitude. Uncertainty separately developed independently address bias, bias variability, precision, allowing distribution true rate be directly calculated from estimated by technology. Derived results demonstrate potential all three methane mitigation, methodology can readily applied characterize other update following future experiments. Finally, analyzed also importance using range sites times avoid underestimating uncertainties.