Land surface modeling and satellite passive microwave imagery: a comparison of top soil moisture and surface temperature estimates

Abstract

Improved accuracy in defining initial conditions for fully-coupled numerical weather prediction models (NWP) along with continuous internal bias corrections for baseline data generated by uncoupled Land Surface Models (LSM), is expected to lead to improved short-term to long-range weather forecasting capability. Because land surface parameters are highly integrated states, errors in land surface forcing, model physics and parameterization tend to accumulate in the land surface stores of these models, such as soil moisture and surface temperature. This has a direct effect on the model's water and energy balance calculations, and will eventually result in inaccurate weather predictions. Surface soil moisture and surface temperature estimates obtained with a recently improved retrieval algorithm from the Advanced Microwave Scanner Radiometer (AMSR) aboard NASA's Earth Observing System (EOS) Aqua satellite are evaluated against model output of the Community Noah Land Surface Model operated within the Land Information System (LIS) forced with atmospheric data of the NCEP Global Data Assimilation System (GDAS). The surface temperature retrievals and Noah LSM output are further evaluated against local measurements from the Mesonet observational grid in Oklahoma. Preliminary analysis presented here shows a potential to improve simulated surface temperature estimates of the Noah model by assimilating satellite derived surface temperature fields. The potential for updating (top) soil moisture seems to be more restricted, mainly as a result of the relatively thick top soil layer of the model as compared to the passive microwave emanation depth.