Land Surface Model Data Assimilation for Atmospheric Prediction

Accurate latent and sensible heat flux prediction in response to land surface soil moisture at midlatitudes has been shown to be as important as sea surface temperature in making accurate precipitation prediction at mid-latitudes over land (Koster et al., 2000). Unfortunately, land surface models typically give a poor prediction of soil moisture and atmospheric feedback, with large differences between predictions from different models even when using the same parameters, inputs, and initial conditions (Houser et al., 2001). To overcome this limitation, assimilation of observed quantities has been pursued. One of the earliest approaches has been the assimilation of screen level air temperature and relative humidity (eg. Mahfouf, 1991), which are only weakly related to soil moisture and not widely observed in remote areas. More recently, a wide range of alternate assimilation approaches have been explored for accurate land surface model prediction of soil moisture. Such approaches include the assimilation of i) remotely sensed near-surface soil moisture, ii) streamflow, iii) changes in terrestrial gravity, and iv) remotely sensed latent and sensible heat flux. This paper briefly describes progress from these approaches.