Impact of utilization of MODIS derived land products in mesoscale model simulations

Jonathan G. Fairman, Jr., Department of Atmospheric Science, The University of Alabama in Huntsville, fairman@nsstc.uah.edu
Valentine G. Anantharaj, Geophysical Research Institute, Mississippi State University, val@gri.msstate.edu
Aaron J. Song, Earth System Science Center, The University of Alabama in Huntsville, asong@nsstc.uah.edu
Udaysankar S. Nair, Department of Atmospheric Science, The University of Alabama in Huntsville, nair@nsstc.uah.edu (Presenting)
Yuling Wu, Earth System Science Center, The University of Alabama in Huntsville, wuy@nsstc.uah.edu

Land-atmosphere interactions significantly influence the meteorological processes in the boundary layer. The nature of land surface vegetation, soil type and spatial distribution of soil moisture all affect the transfer of energy between the land surface and the atmosphere. This in turn impacts the evolution of temperature, humidity, turbulence in boundary layer and convective cloud formation. In addition, land surface spatial heterogeneity and associated contrasts in surface energetics induce mesoscale circulation patterns that affect not only boundary layer convective cloud formation but also larger scale convective systems. Thus realistic representation of land surface characteristics in numerical models is important for forecasting of boundary layer meteorology.

This poster will examine the impact of utilizing 1 km spatial resolution MODIS NDVI, LAI and broadband albedo products in the Regional Atmospheric Modeling System (RAMS) to specify realistic distributions of land surface parameters. NDVI is used for computing vegetation fraction. The MODIS broadband albedo product is used along with computed vegetation fraction and parameterized values of bare soil albedo to estimate albedo of the vegetation cover. Simulations that utilize default RAMS land surface characteristics and model simulations utilizing MODIS land surface products are compared against surface observations for both spring and fall seasons.