Brightness Temperature Versus Surface Soil Moisture Assimilation
Jeffrey P. Walker, and Durga D. Kandel
Power Point Presentation
Accurate initialisation of land surface soil moisture and temperature is crucial for improved weather and climate prediction in coupled land-ocean-atmosphere models. Remote sensing methods provide the necessary measurements for constraining off-line land surface model predictions to be used in coupled-model initialisation. Low frequency passive microwave is the remote sensor of choice as it has all-weather capability with 1 to 3 day repeat coverage, and is sensitive to both near-surface soil moisture and temperature. However, to be useful for prediction studies, deeper soil moisture and temperature must be inferred from the near-surface observations using a data assimilation framework. The current practice of data assimilation uses a derived near-surface soil moisture product rather than the passive microwave measurements of brightness temperatures directly. The potential advantages for assimilating the brightness temperature are i) a capability to constrain soil temperature in addition to soil moisture prediction, and ii) that soil temperature estimates used in deriving the soil moisture product may be poor, yielding a poor near-surface soil moisture retrieval. However, it is currently unclear if it is better to assimilate the derived soil moisture product or the raw brightness temperature observations. Therefore, this study explores the potential improvement in soil moisture prediction accuracy achieved by direct assimilation of brightness temperature data relative to a retrieved soil moisture product. This study uses C-band data from the Scanning Multifrequency Microwave Radiometer (SMMR) and the Catchment Land Surface Model for all of Australia.