The Indianapolis Flux Experiment (INFLUX) aims to quantify and improve the effectiveness of inferring greenhouse gas (GHG) source strengths from downstream concentration measurements in urban environments. Mesoscale models such as the Weather Research and Forecasting (WRF) model can provide realistic depictions of planetary boundary layer (PBL) structure and flow fields at horizontal grid lengths (Δ x ) down to a few km. Nevertheless, a number of potential sources of error exist in the use of mesoscale models for urban inversions, including accurate representation of the dispersion of GHGs by turbulence close to a point source. Here we evaluate the predictive skill of a 1-km chemistry-adapted WRF (WRF-Chem) simulation of daytime CO 2 transport from an Indianapolis power plant for a single INFLUX case (28 September 2013). We compare the simulated plume release on domains at different resolutions, as well as on a domain run in large eddy simulation (LES) mode, enabling us to study the impact of both spatial resolution and parameterization of PBL turbulence on the transport of CO 2 . Sensitivity tests demonstrate that much of the difference between 1-km mesoscale and 111-m LES plumes, including substantially lower maximum concentrations in the mesoscale simulation, is due to the different horizontal resolutions. However, resolution is insufficient to account for the slower rate of ascent of the LES plume with downwind distance, which results in much higher surface concentrations for the LES plume in the near-field but a near absence of tracer aloft. Physics sensitivity experiments and theoretical analytical models demonstrate that this effect is an inherent problem with the parameterization of turbulent transport in the mesoscale PBL scheme. A simple transformation is proposed that may be applied to mesoscale model concentration footprints to correct for their near-field biases. Implications for longer-term source inversion are discussed.

We present a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF), and test the impact of assimilating meteorological observation on transport accuracy. A Four Dimensional Data Assimilation (FDDA) technique continuously assimilates meteorological observations from various observing systems into the transport modeling system, and is coupled to the high resolution CO 2 emission product Hestia to simulate the atmospheric mole fractions of CO 2 . For the Indianapolis Flux Experiment (INFLUX) project, we evaluated the impact of assimilating different meteorological observation systems on the linearized adjoint solutions and the CO 2 inverse fluxes estimated using observed CO 2 mole fractions from 11 out of 12 communications towers over Indianapolis for the Sep.-Nov. 2013 period. While assimilating WMO surface measurements improved the simulated wind speed and direction, their impact on the planetary boundary layer (PBL) was limited. Simulated PBL wind statistics improved significantly when assimilating upper-air observations from the commercial airline program Aircraft Communications Addressing and Reporting System (ACARS) and continuous ground-based Doppler lidar wind observations. Wind direction mean absolute error (MAE) decreased from 26 to 14 degrees and the wind speed MAE decreased from 2.0 to 1.2 m s –1 , while the bias remains small in all configurations (< 6 degrees and 0.2 m s –1 ). Wind speed MAE and ME are larger in daytime than in nighttime. PBL depth MAE is reduced by ~10%, with little bias reduction. The inverse results indicate that the spatial distribution of CO 2 inverse fluxes were affected by the model performance while the overall flux estimates changed little across WRF simulations when aggregated over the entire domain. Our results show that PBL wind observations are a potent tool for increasing the precision of urban meteorological reanalyses, but that the impact on inverse flux estimates is dependent on the specific urban environment.