Mesoscale modeling of Atlanta, GA utilizing a new high-resolution landcover data set

Simulation of urban meteorology using conventional mesoscale models is often less than ideal due to the fact that the land surface schemes coupled with such models were designed for natural landscapes. The urban landscape is quite different from natural, more vegetated land areas in terms of the surface radiation and energy balance as well as the turbulent fluxes of energy which are altered by the built environment. Accurate land use characterization has the potential to improve the accuracy of the modeling results. Unfortunately, most conventional land use data sets are either not of sufficient spatial resolution or do not have good differentiation of urban land use types to accurately characterize the urban land surface. In a meteorological and air quality modeling study for the Atlanta, GA metropolitan area, we incorporated an improved high-resolution land use characterization data set within MM5. The dataset provides a more accurate representation of the current land use and allows a more robust assessment of future land use changes through the use of a Spatial Growth Model (SGM). Meteorological forecasts made using the high-resolution land use data for summertime episodes in 1999 and 2000 were compared against the lower-resolution traditional land use data previously used. Use of the high-resolution data improved performance of the meteorological model substantially, with the overall daytime cold bias reduced by over 30%. In additional, land use changes in the Atlanta area due to urbanization were predicted through 2030 using the SGM. Modeling simulations with the projected land use predicted higher urban air temperatures. The incorporation of urban heat island mitigation strategies (i.e., highly reflective roofing and increased tree canopy) partially offset this warming trend.