Abstract:
Global retrievals of surface soil moisture from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) are available from June 2002 to present. Our eventual goal is to utilize these data to improve the initialization of the land surface component in NASA's seasonal climate forecast system. In this paper, we present a merged dataset produced by the assimilation of the AMSR-E soil moisture retrievals into the NASA Catchment land surface model.

The assimilation system is based on the Ensemble Kalman filter (EnKF). While the satellite and model data may contain consistent and useful information in their seasonal cycle and anomaly signals that can be merged and maximized in a data assimilation system, it is well known that soil moisture climatologies from different data sources typically differ strongly. In our asimilation system, we apply a simple and effective method of bias removal that utilizes a mapping between the cumulative distribution functions (cdf) of the satellite and model data. Due to the relatively short satellite record, the cdf estimates are based on the ergodic substitution of variability in space for variability in time.

The merged dataset produced by the assimilation system are evaluated against available in situ measurements, and the impact of the AMSR-E retrievals on the model estimates is documented.