• Observational sensor and wind data are available at a high temporal resolution. The Gaussian puff model is able to exploit these high resolution data, resulting in more accurate concentration predictions. For use with the Gaussian plume model, the high-resolution data would need to be averaged over longer time periods to meet the steady state assumption, which eradicates the information contained in the higher frequency component of the signal. • Near real-time applications such as rapid methane emission detection, localization, quantification and mitigation are desired. In this case, using an extended averaging window for the Gaussian plume model may introduce too long of a lag between observations and detection, localization, and quantification results. • Emission rates are not constant. This scenario violates the steady state assumption in the Gaussian plume model but is easily accommodated by the Gaussian puff model. Note that this study focused solely on constant emission rates due to the nature of the available test data, but our implementation is fully equipped to handle variable emission rates as well.