Studies of the influence of soil moisture on atmospheric circulation over a wide range of spatio-temporal scales have been performed by many investigators. The results demonstrated that an accurate initialization and evolution of soil moisture is essential for obtaining an appropriate depiction of atmosphere-surface interactions and the associated impacts onweather and climate. Due to a lack of observation data, especially in high latitudes, the initial soil moisture is specified poorly in current models.

In this study, we incorporate a satellite data assimilation method following Jones et al.(1998) into the Penn State/NCAR MM5 modeling system to retrieve soil moisture. As earlier studies have indicated that the surface temperature is most sensitive to the soil moisture (relative to other factors), during the mid-morning hours, we assume that the difference between simulated skin temperature and the observed one in mid-morning can be minimized by adjustments to the soil moisture. Thus the observed skin temperature is the key for the soil moisture initialization scheme.

For high latitudes, several methods of estimating surface temperature using thermal satellite data from the Advanced Very High Resolution Radiometer (AVHRR) on NOAA polar-orbiting platforms have been developed. We choose to employ the retrieval algorithm for clear-sky surface-temperature from Key et al. (1997). In this algorithm, two 'split-window' infrared channels at approximately 11 um and 12 um from AVHRR are used.

In our presentation we will present results from case study simulation experiments over high latitudes to demonstrate the utility of the soil moisture initialization scheme containing the components described above as implemented within the MM5 modeling system.