Abstract. Greenhouse gas emissions mitigation requires understanding
the dominant processes controlling fluxes of these trace gases at increasingly
finer spatial and temporal scales. Trace gas fluxes can be estimated using a
variety of approaches that translate observed atmospheric species mole
fractions into fluxes or emission rates, often identifying the spatial and
temporal characteristics of the emission sources as well. Meteorological
models are commonly combined with tracer dispersion models to estimate fluxes
using an inverse approach that optimizes emissions to best fit the trace gas
mole fraction observations. One way to evaluate the accuracy of atmospheric
flux estimation methods is to compare results from independent methods,
including approaches in which different meteorological and tracer dispersion
models are used. In this work, we use a rich data set of atmospheric methane
observations collected during an intensive airborne campaign to compare
different methane emissions estimates from the Barnett Shale oil and natural
gas production basin in Texas, USA. We estimate emissions based on a variety
of different meteorological and dispersion models. Previous estimates of
methane emissions from this region relied on a simple model (a mass balance
analysis) as well as on ground-based measurements and statistical data
analysis (an inventory). We find that in addition to meteorological model
choice, the choice of tracer dispersion model also has a significant impact
on the predicted downwind methane concentrations given the same emissions
field. The dispersion models tested often underpredicted the observed
methane enhancements with significant variability (up to a factor of 3)
between different models and between different days. We examine possible
causes for this result and find that the models differ in their simulation of
vertical dispersion, indicating that additional work is needed to evaluate
and improve vertical mixing in the tracer dispersion models commonly used in
regional trace gas flux inversions.
Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection