Reducing the impact of transport model error in urban greenhouse gas inversion models using the Aliso Canyon natural gas leak as a natural tracer experiment

Published: March 22, 2018

Author(s)

Sharon M. Gourdji, Vineet Yadav, Anna Karion, Kimberly L. Mueller, T. Conley, Stephen Conley, Tom Ryerson, Thomas Nehrkorn, Eric Kort

Abstract

Urban greenhouse gas (GHG) flux estimation with atmospheric measurements and modeling, i.e., the “top-down” approach, can potentially support emission reduction policies by providing continuous data streams for trend and anomaly detection in conjunction with bottom-up inventories. In particular, aircraft-collected GHG observations have the potential to help quantify point-source emissions that may not be adequately sampled by fixed tower-based observing systems. Here, we estimate CH4 emissions from a known point source, the Aliso Canyon natural gas leak in Los Angeles, CA from October 2015 to February 2016, using atmospheric inverse models with CH4 observations from twelve flights ≈4 km downwind of the leak and surface sensitivities from a mesoscale atmospheric transport model. This leak event has been well- quantified using various methods by the California Air Resources Board, thereby providing high confidence in the mass-balance leak rate estimates of (Conley et al. 2016), which are used as reference for comparison to inversion results. The inversions are shown to provide reasonable estimates of the leak magnitude with realistic confidence intervals, although inversion results are still affected by modeled wind speed errors of up to 10 m/s, quantified by comparing airborne meteorological observations with modeled values along the flight track. An additional setup is tested using scaled wind speed errors along the flight track in the model-data mismatch covariance matrix, which is shown to significantly reduce the influence of these errors on spatial patterns and magnitudes of leak estimates from the inversions. In sum, this study takes advantage of a natural tracer release experiment (i.e., the Aliso Canyon leak) to identify effective approaches for reducing the influence of transport model error on inversions, while suggesting the potential for integrating surface tower and aircraft-collected GHG observations in future top-down urban GHG emission monitoring
Citation: Environmental Research Letters
Pub Type: Journals

Keywords

greenhouse gas emissions, methane, atmospheric inverse models, urban emission monitoring
Created March 22, 2018, Updated November 10, 2018