Evaluation of the Assumptions made in a Land Surface Data Assimilation Technique

Satellite data assimilation provides the numerical weather prediction community with a means to include high spatial resolution land surface information in numerical simulations. A technique has been developed for assimilating GOES-derived skin temperature tendencies into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. This technique has shown promise in improving numerical simulations, however, with any land surface data assimilation technique, critical assumptions must be made to account for difficult to measure parameters such as moisture availability. The assumptions made in this satellite data assimilation technique are evaluated to determine whether the errors in the assumptions impact the validity and overall performance of the technique. Two case studies, including the 2000 Texas air quality study (TexAQ 2000) and the First International Flux experiment (FIFE) are used to validate surface fluxes produced when the land surface assimilation technique is applied.