A comprehensive assessment of land surface-atmosphere interactions in a WRF/Urban modeling system for Indianapolis, IN

Abstract

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.

Joint inverse estimation of fossil fuel and biogenic CO2 fluxes in an urban environment: An observing system simulation experiment to assess the impact of multiple uncertainties

“…parameterization schemes, boundary and initial conditions, and spatial resolution) complicate the assessment of transport errors (Isaac et al, 2014). Evaluation and minimization of transport errors can be achieved by improving model parameterizations (Sarmiento et al, 2017) and by assimilating site-specific meteorological observations . We note also that our study makes the simplifying assumption of uncorrelated transport errors, whereas, in reality, transport errors are likely to be correlated, especially at the spatiotemporal scales characteristic of an urban study.…”

“…The Indianapolis Flux Experiment (INFLUX, http://sites.psu.edu/influx/) was proposed to develop, test and improve methods to estimate anthropogenic GHG emissions from cities, using Indianapolis as a test bed . This project uses aircraft (Cambaliza et al, 2014;Heimburger et al, 2017) and a high-density surface tower network Richardson et al, 2017) combined with high-resolution atmospheric modeling Sarmiento et al, 2017) to infer CO 2 ff emissions at 1 km spatial resolution (Lauvaux et al, 2016). Figure 1 shows the distribution of instrumented towers and daytime average surface CO 2 fluxes during the first 10 days of September 2013.…”

“…CO measurements are more readily available, while flask measurements of 14 C are expensive and discontinuous, although CO is a less accurate tracer of CO 2 ff than 14 C (Levin and Karstens, 2007). Another challenge is minimization of uncertainty in high-resolution atmospheric transport models used to simulate trace gas transport in an urban setting, where complex boundary layer structures may be formed due to the land-use/land-cover change and intensive human activities (Wang et al, 2011;Sarmiento et al, 2017;Gaudet et al, 2017). In addition, the inverse estimation of CO 2 ff emissions is constrained by atmospheric CO 2 measurements, and the trade-off between measurement density and quality is an important emerging debate for urban GHG monitoring (Wu et al, 2016;Shusterman et al, 2016;Turner et al, 2016;Martin et al, 2017).…”

Exploration of the impact of nearby sources on urban atmospheric inversions using large eddy simulation

“…The Noah land surface model was used for the simulation, with some tiles re-classified based on the 2006 version of the 30-m National Land Cover Database (NLCD) (Homer et al 2015;Sarmiento et al 2017). Time-dependent emission data from the plant in 2013 were supplied by data obtained from the EPA Clean Air Market Division (CAMD) Emission Tracking System/Continuous Emissions Monitoring system (ETS/CEMs) for electrical generation, also used in the Hestia database over Indianapolis (Gurney et al 2012).…”

“…Transport error can also be due to an erroneous prediction of the prevailing 'mean' component of the wind, incorrect simulation or parameterization of turbulent perturbations to that wind, and errors introduced by model physical parameterizations (e.g., Lin and Gerbig, 2005). Errors in the surface energy balance, temperature profiles, and planetary boundary layer (PBL) structure may all indirectly lead to transport error by causing errors in the mean or turbulent wind field (Lauvaux and Davis, 2014;Sarmiento et al 2017;Deng et al 2017).…”