INFLUX: Comparisons of modeled and observed surface energy dynamics over varying urban landscapes in Indianapolis, IN Daniel P. Sarmiento, Kenneth Davis, Thomas Lauvaux, Natasha Miles, Scott Richardson Pennsylvania State University WRF lacks sub-tile gridding: Potential effects on surface energy balance estimations Model to observation comparison of surface energy fluxes Indianapolis FLUX Project (INFLUX) Background: Twelve towers are currently online in Indianapolis, including four flux towers (Site 1, 2, 3, and 4). (right) Flux tower sites were chosen based on the land surface that is most representative of Indianapolis (below). In WRF, an individual tile is assigned a surface classification based on the most common classification that is contained within that tile. Therefore, information about the surface is lost as the model grid spacing is increased, which can lead to errors when resolving the surface energy fluxes. A comparison of surface energy fluxes was performed with data collected from Site 02. The following figures depict a 10 day period in January. The modeled results (dashed line) and the observations (solid line) for various surface energy components are shown. This potential loss of information can be demonstrated by using maps with grid spacings of 3km (left) and 1km (right) of a 27km x 27km area of Indianapolis. These maps show the surface classifications for the 27km2 domain. Sensible heat fluxes (left) were overestimated by 30% to 100% in WRF. Site 01 Site 02 Site 03 Site 04 Latent heat fluxes (right) in the model rarely went above 0 W m-2, however, observations had daytime fluxes as high as 50 W m-2. 1km x 1km land surface classifications of the flux tower sites based on NLCD data for 2006 (left). We take the surface energy variables at each grid point and plot a distribution. Model to observation comparison of meteorological variables Consistent underestimation of the friction velocity is seen in the WRF results. Since we are interested in using WRF to model transport of greenhouse gases and perform atmospheric inversions, it is important to compare the WRF model results to surface stations located in the Indianapolis area. The model runs failed to capture the magnitude of the observed diurnal variations in air temperature (bottom left) and wind speed (right). There is a large spread in the wind direction (bottom right) and there seems to be a consistent counterclockwise bias in the model. The raw data collected at Site 02 has been treated with a tilt correction algorithm (Wilczak et al., 2000), despiking algorithm (Vickers & Mahrt, 1997), and the WPL correction for air density corrections (Webb et al., 1980). Small positive values of both the sensible heat flux (top left) and the latent heat flux (top right) tend to be overrepresented in the 3km domain. There are also small discrepancies in the higher extremes (> 250 W m-2) of the surface fluxes. Conclusions Even with 4D data assimilation, there still are errors in the modeled winds and air temperature, which can introduce errors in the transport and atmospheric inversion models. Without sub-tile gridding, surface energy fluxes in the model will tend to be at the extremes (0 – 50 W m-2 or > 300 W m-2). Extensive work will be needed to have the model match surface energy flux observations. Small values of the friction velocity (left) are underrepresented in the 3km domain and higher values are overrepresented. Acknowledgments Funding for this project was provided by NIST (National Institute of Standards and Technology) Contact email: dps217@psu.edu The following schemes were used in this run: PBL: MYNN2.5 Surface Layer: MYNN Land Surface: NOAH Urban Scheme: None http://influx.psu.edu