Initialization of MM5/WRF simulations with ALEXI-derived volumetric soil moisture estimates

Soil moisture plays a vital role in the partitioning of sensible and latent heat fluxes in the surface energy budget; however, high spatial-resolution observations of soil moisture distributions are difficult to acquire. The ALEXI model contains the two-source land-surface representation of Norman et al. (1995), which partitions surface fluxes and radiometric temperature into canopy and soil contributions based on the fraction of vegetation cover within the scene. Anderson et al. (1997) and Mecikalski (1999) detail the implementation of ALEXI as a regional-scale application over the continental United States. This model relies on remote sensing data to operate, including GOES-derived surface brightness temperature changes, satellite-derived land cover properties, and limited synoptic weather data to operate (Mecikalski, 1999). This version of the ALEXI algorithm has been run daily on a 10 km resolution grid from the years 2002 to present. ALEXI diagnoses a fraction of potential evapotranspiration (fPET) for both the surface layer (0 – 5 cm) and root-zone (5 – 200 cm), given a calculation of the potential ET associated with the canopy and soil components of each pixel scene. The fraction of potential ET can be directly related to a fraction of available water, which in turn can be used to calculate volumetric soil moisture for a given soil texture.

Upon successful validation of ALEXI-derived volumetric soil moisture, these observations will be used to initialize mesoscale simulations using both the Weather and Forecasting Model (WRF) and MM5. The procedure used is a unique implementation of GOES satellite-estimated soil moisture, a method that has not previously been attempted. The focus of this presentation is to examine the effects of ALEXI-derived volumetric soil moisture on the simulations during several case study days in 2003 and 2004. The soil moisture estimates from ALEXI will initially be used to initialize these models at a spatial resolution of 10 km. Initialization of high-resolution land-surface characteristic datasets within our model simulations such as fraction of vegetative cover and leaf area index (LAI) will also be examined as these datasets are native to the calculation of parameters used in the derivation of ALEXI soil moisture. This process will help to quantify the sensitivity and importance of a higher-resolution soil moisture dataset, and one that does not rely on the assimilation of antecedent precipitation. Results will be quantified through statistical verification techniques.