Improving forecasts of the surface heat fluxes that drive the evolution of the planetary boundary layer in numerical weather prediction models begins with an accurate specification of the initial state of the land surface. Penn State–NCAR fifth-generation Mesoscale Model (MM5) forecasts are initialized with fractional vegetation coverage and leaf area index derived from satellite observations and soil temperature and moisture observations obtained from the Oklahoma Mesonet at several soil depths. Forecasts show that despite initially providing the model with the best possible characterization of the land surface, MM5 systematically underestimates latent heat fluxes for several case studies. This result indicates the necessity for adjustments to the land surface model physics.

Utilizing the wealth of available Oklahoma Mesonet observations of both soil and lower atmospheric conditions, including observations of surface energy fluxes, a principal-component regression reveals simple relationships between latent heat flux and other available surface observations. Development of a new parameterization for evaporation from bare soil takes advantage of periods of very dry conditions observed across Oklahoma. Combining this with a new empirical canopy transpiration scheme within MM5 yields improved sensible and latent heat flux forecasts and better partitioning of the surface energy budget. Surface temperature and mixing ratio forecasts show improvement when compared with the dense network of observations from the Oklahoma Mesonet.