Wednesday, 12 January 2005: 5:00 PM
The influence of improved land surface and soil data on mesoscale model predictions
One of the most difficult aspects in the evaluation of land surface models is the lack of observational data for accurate specification of the model initial conditions. Routine observations of fractional vegetation coverage and leaf area index (LAI) are not available at high resolution (~1 km), nor are observations of soil moisture and soil temperature. This gap in our observational capabilities seriously hampers the evaluation and improvement of land surface model parameterizations, since model errors may be related to improper initial conditions as much as to inaccuracies in the model formulations. To overcome these difficulties, two unique data sets are used. First, fractional vegetation coverage and LAI are derived from biweekly maximum normalized difference vegetation index (NDVI) composites at 1 km resolution obtained from daily observations by the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration satellites. Second, the Oklahoma Mesonet measures soil moisture and soil temperature at 15-minute intervals. Combined, these two data sets provide significantly improved initial conditions for land surface models and allow us to evaluate the utility of the land surface models with much greater confidence and detail than previously.
The value of these two data sources to land surface model initializations is evaluated using the Penn StateľNCAR fifth-generation Mesoscale Model (MM5). Forecasts that both include and neglect these unique land surface observations are compared. Results are verified against the dense network of surface observations afforded by the Oklahoma Mesonet, including surface flux data derived from special sensors available at some of the Mesonet sites. Implications for further data requirements are discussed.