D. P. Sarmiento

TL;DR: In this paper, the authors developed the first comprehensive monitoring system of CO2 emissions at high resolution over the city of Indianapolis using a uniquely dense network of surface towers measuring continuously the atmospheric concentrations of greenhouse gases (GHGs).

TL;DR: The Indianapolis Flux Experiment’s scientific objectives are to quantify CO2 and CH4 emission rates at 1 km 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.

TL;DR: In this paper, a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF) is presented.

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.

TL;DR: In this paper, a set of simulations was performed by implementing different planetary boundary layer (PBL) schemes, urban canopy models (UCMs), and a model subroutine was created in order to reduce an inherent model overestimation of urban land cover.