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The Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) is a recently-developed observational system that collects, archives, and quality controls atmospheric, surface, and soil data in real-time from 90 stations across Oklahoma. Ten of the 90 sites, termed "super sites", are equipped with additional sonic anemometry and four-component net radiometers to provide complete observations of the surface energy balance. Oklahoma Mesonet and OASIS data are used in this study to validate the sensitivity and accuracy of a land-surface scheme within a numerical prediction model.

The Advanced Regional Prediction System (ARPS) is a three-dimensional, nonhydrostatic mesoscale model developed by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The land-surface model (LSM) used within ARPS is the Interactions Soil Biosphere Atmosphere (ISBA) scheme. Mesonet and OASIS data collected from the super site located in Norman, Oklahoma, are used as verification for the ISBA.

Research presented in this study outlines the challenges in developing, maintaining, and using in-situ data for model validation. Such problems as instrument error, surface heterogeneity, and non-closure of the surface energy bud et limit 9 data accuracy. Preliminary results of model validation focus on the sensitivity of the soil physics within the ISBA scheme. Model sensitivity to vegetation cover, surface roughness, and soil type are investigated. Furthermore, several recent improvements to ISBA are evaluated and compared to observations. This study concludes that the sensitivity of the ISBA to a priori soil and vegetation type is detrimental for this scheme to be used in a mesoscale model without improved treatment of surface heterogeneity.