Numerical Weather Prediction (NWP) model forecasts of the near-surface stability structure are of critical importance to chemical and biological dispersion modeling efforts. The accuracy of the forecasts is closely tied to accurate forecasts of the soil moisture and energy balance at the earth’s surface. In order to deal with these problems, Land Surface Models (LSM) are used to better predict the surface conditions. One of the more sophisticated and widely applied LSMs in current NWP models is the NOAH LSM. In order to capture the full predictive benefit of the NOAH LSM in NWP model forecasts it is necessary to initialize the LSM with realistic soil temperature and moisture fields. In an effort to accomplish this the NOAH LSM has been coupled with the Local Analysis and Prediction System (LAPS) so that it can be used to keep a running initialization state that has had the benefit of a long history of exposure to the observed weather preceding the NWP forecast time.

Before proceeding to use the LSM within a NWP model, the performance of the LSM when driven by observed data must first be verified. To accomplish this task the LSM domain was placed over the southern plains of the United States so that the values of the LSM could be compared to Atmospheric Radiation Measurement (ARM) Program measurements. The ARM Program maintains sensors throughout Oklahoma and Kansas that measure many different quantities including soil moisture, soil temperature, latent heat flux, sensible heat flux, and others. By graphically comparing the actual values of the soil parameters from the ARM data with the LSM output, strengths and weaknesses within the model are found.