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

• Published in: Elementa (Washington, D.C.) Vol. 5
• Main Authors: Sarmiento, Daniel P., Davis, Kenneth J., Deng, Aijun, Lauvaux, Thomas, Brewer, Alan, Hardesty, Michael
• Format: Journal Article
• Language: English
• Published: Oakland University of California Press, Journals & Digital Publishing Division 01.05.2017; BioOne
• Subjects: Air temperature; Atmospheric models; Boundary layer; Boundary layers; Canopies; Configurations; Energy; Enthalpy; Errors; Geometry; Heat; Heat flux; Hydrology; Land cover; Latent heat; Planetary boundary layer; Radiation; Rain; Sensible heat; Sensitivity analysis; Simulation; Statistical analysis; Summer; Urban areas; Urban meteorology; Urban modeling; Weather; Wind; Wind direction; Wind speed; Winter; WRF Model sensitivity
• ISSN: 2325-1026; 2325-1026
• DOI: 10.1525/elementa.132

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.