A major problem area for improved numerical modeling of near surface variables is the sensitivity of model predictions to the accuracy of key land surface parameters. In particular, the forecasts of near surface quantities such as 2-m temperatures and relative humidity and 10-m winds are influenced strongly by the state of the land surface, as is precipitation. The ability to predict these parameters is important to a wide variety of human activities, ranging from planning outdoor and weekend activities to transportation routing and energy conservation. Vegetation characteristics such as fractional vegetation coverage (FVEG) and leaf area index (LAI) arguably are the most important land surface parameters that need to be defined accurately. However, present practices for defining these parameters are overly simplistic and typically are based only on climatology. Yet vegetation responds to daily variations in rainfall and is far from static. Recent studies have demonstrated potentially large impacts from specifying both FVEG and LAI, at high spatial and temporal resolution, in a state-of-the-art coupled atmosphere-land surface modeling system.

In this study we will outline an approach to produce daily updates of FVEG and LAI at 1-km resolution and include these vegetation parameters in mesoscale and cloud-scale numerical weather prediction models. This research is a unique collaboration of expertise between three institutions in the key areas of mesoscale atmospheric modeling, land surface modeling and the generation of real-time, high resolution, satellite-derived land surface parameters. The focus region for this study is the Great Plains, especially during the growing season (March to October). The expected impacts of this research are improvements to short-range predictions of near surface variables such as temperature and moisture, thereby affecting power load, air quality, and convective weather forecasting, all of which have significant economic implications.