One of the challenges in land surface modeling involves specifying accurately the initial state of the land surface. Most efforts have focused upon using multi-year climatologies to specify the fractional coverage of vegetation. For example, the National Center for Environmental Prediction (NCEP) Eta model uses a a five-year satellite climatology of monthly Normalized Difference Vegetation Index (NDVI) values to define the fractional vegetation coverage, or greenness, at 1/8 degree resolution. These data are valid on the 15th of every month and are interpolated temporally for daily runs. Yet vegetation characteristics change from year-to-year and are influenced by short-lived events such as fires, crop harvesting, and hailstorms that will be missed using a climatological data base.

To explore the importance of the initial state vegetation characteristics to numerical weather forecasts, we examine how the Eta model responds to a different method of initializing fractional vegetation coverage. Studies have shown that several vegetation metrics can be estimated from bi-weekly, 1-km resolution NDVI maximum-value composites derived from NOAA's Advanced Very High Resolution Radiometer (AVHRR). This approach may allow for near real-time estimations of fractional vegetation coverage to be used. Several numerical forecasts of the Eta model, using both climatological and near real-time values of fractional vegetation coverage, are compared to examine the potential importance of variations in vegetation to forecasts of 2 m temperatures and surface fluxes.