The Indianapolis Flux Experiment aims to utilize a variety of atmospheric measurements and a high-resolution inversion system to estimate the temporal and spatial variation of anthropogenic greenhouse gas emissions from an urban environment. We present a Bayesian inversion system solving for fossil fuel and biogenic CO 2 fluxes over the city of Indianapolis, IN. Both components were described at 1 km resolution to represent point sources and fine-scale structures such as highways in the a priori fluxes. With a series of Observing System Simulation Experiments, we evaluate the sensitivity of inverse flux estimates to various measurement deployment strategies and errors. We also test the impacts of flux error structures, biogenic CO 2 fluxes and atmospheric transport errors on estimating fossil fuel CO 2 emissions and their uncertainties. The results indicate that high-accuracy and high-precision measurements produce significant improvement in fossil fuel CO 2 flux estimates. Systematic measurement errors of 1 ppm produce significantly biased inverse solutions, degrading the accuracy of retrieved emissions by about 1 µ mol m –2 s –1 compared to the spatially averaged anthropogenic CO 2 emissions of 5 µ mol m –2 s –1 . The presence of biogenic CO 2 fluxes (similar magnitude to the anthropogenic fluxes) limits our ability to correct for random and systematic emission errors. However, assimilating continuous fossil fuel CO 2 measurements with 1 ppm random error in addition to total CO 2 measurements can partially compensate for the interference from biogenic CO 2 fluxes. Moreover, systematic and random flux errors can be further reduced by reducing model-data mismatch errors caused by atmospheric transport uncertainty. Finally, the precision of the inverse flux estimate is highly sensitive to the correlation length scale in the prior emission errors. This work suggests that improved fossil fuel CO 2 measurement technology, and better understanding of both prior flux and atmospheric transport errors are essential to improve the accuracy and precision of high-resolution urban CO 2 flux estimates.

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 assess the detectability of city emissions via a tower-based greenhouse gas (GHG) network, as part of the Indianapolis Flux (INFLUX) experiment. By examining afternoon-averaged results from a network of carbon dioxide (CO 2 ), methane (CH 4 ), and carbon monoxide (CO) mole fraction measurements in Indianapolis, Indiana for 2011–2013, we quantify spatial and temporal patterns in urban atmospheric GHG dry mole fractions. The platform for these measurements is twelve communications towers spread across the metropolitan region, ranging in height from 39 to 136 m above ground level, and instrumented with cavity ring-down spectrometers. Nine of the sites were deployed as of January 2013 and data from these sites are the focus of this paper. A background site, chosen such that it is on the predominantly upwind side of the city, is utilized to quantify enhancements caused by urban emissions. Afternoon averaged mole fractions are studied because this is the time of day during which the height of the boundary layer is most steady in time and the area that influences the tower measurements is likely to be largest. Additionally, atmospheric transport models have better performance in simulating the daytime convective boundary layer compared to the nighttime boundary layer. Averaged from January through April of 2013, the mean urban dormant-season enhancements range from 0.3 ppm CO 2 at the site 24 km typically downwind of the edge of the city (Site 09) to 1.4 ppm at the site at the downwind edge of the city (Site 02) to 2.9 ppm at the downtown site (Site 03). When the wind is aligned such that the sites are downwind of the urban area, the enhancements are increased, to 1.6 ppm at Site 09, and 3.3 ppm at Site 02. Differences in sampling height affect the reported urban enhancement by up to 50%, but the overall spatial pattern remains similar. The time interval over which the afternoon data are averaged alters the calculated urban enhancement by an average of 0.4 ppm. The CO 2 observations are compared to CO 2 mole fractions simulated using a mesoscale atmospheric model and an emissions inventory for Indianapolis. The observed and modeled CO 2 enhancements are highly correlated (r 2 = 0.94), but the modeled enhancements prior to inversion average 53% of those measured at the towers. Following the inversion, the enhancements follow the observations closely, as expected. The CH 4 urban enhancement ranges from 5 ppb at the site 10 km predominantly downwind of the city (Site 13) to 21 ppb at the site near the landfill (Site 10), and for CO ranges from 6 ppb at the site 24 km downwind of the edge of the city (Site 09) to 29 ppb at the downtown site (Site 03). Overall, these observations show that a dense network of urban GHG measurements yield a detectable urban signal, well-suited as input to an urban inversion system given appropriate attention to sampling time, sampling altitude and quantification of background conditions.

As part of the Indianapolis Flux (INFLUX) experiment, the accuracy and biases of simulated meteorological fields were assessed for the city of Indianapolis, IN. The INFLUX project allows for a unique opportunity to conduct an extensive observation-to-model comparison in order to assess model errors for the following meteorological variables: latent heat and sensible heat fluxes, air temperature near the surface and in the planetary boundary layer (PBL), wind speed and direction, and PBL height. In order to test the sensitivity of meteorological simulations to different model packages, a set of simulations was performed by implementing different PBL schemes, urban canopy models (UCMs), and a model subroutine that was created in order to reduce an inherent model overestimation of urban land cover. It was found that accurately representing the amount of urban cover in the simulations reduced the biases in most cases during the summertime (SUMMER) simulations. The simulations that used the BEP urban canopy model and the Bougeault & Lacarrere (BouLac) PBL scheme had the smallest biases in the wintertime (WINTER) simulations for most meteorological variables, with the exception being wind direction. The model configuration chosen had a larger impact on model errors during the WINTER simulations, whereas the differences between most of the model configurations during the SUMMER simulations were not statistically significant. By learning the behaviors of different PBL schemes and urban canopy models, researchers can start to understand the expected biases in certain model configurations for their own simulations and have a hypothesis as to the potential errors and biases that might occur when using a multi-physics ensemble based modeling approach.

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.

The objective of the Indianapolis Flux Experiment (INFLUX) is to develop, evaluate and improve methods for measuring greenhouse gas (GHG) emissions from cities. INFLUX’s scientific objectives are to quantify CO 2 and CH 4 emission rates at 1 km 2 resolution with a 10% or better accuracy and precision, to determine whole-city emissions with similar skill, and to achieve high (weekly or finer) temporal resolution at both spatial resolutions. The experiment employs atmospheric GHG measurements from both towers and aircraft, atmospheric transport observations and models, and activity-based inventory products to quantify urban GHG emissions. Multiple, independent methods for estimating urban emissions are a central facet of our experimental design. INFLUX was initiated in 2010 and measurements and analyses are ongoing. To date we have quantified urban atmospheric GHG enhancements using aircraft and towers with measurements collected over multiple years, and have estimated whole-city CO 2 and CH 4 emissions using aircraft and tower GHG measurements, and inventory methods. Significant differences exist across methods; these differences have not yet been resolved; research to reduce uncertainties and reconcile these differences is underway. Sectorally- and spatially-resolved flux estimates, and detection of changes of fluxes over time, are also active research topics. Major challenges include developing methods for distinguishing anthropogenic from biogenic CO 2 fluxes, improving our ability to interpret atmospheric GHG measurements close to urban GHG sources and across a broader range of atmospheric stability conditions, and quantifying uncertainties in inventory data products. INFLUX data and tools are intended to serve as an open resource and test bed for future investigations. Well-documented, public archival of data and methods is under development in support of this objective.