Estimates of land surface conditions (including soil moisture, snow, terrestrial water storage, and skin temperature) are available from
observations, for example, from NASA satellites, and
numerical models of land surface processes.
The latter are driven with precipitation and other near-surface meteorological fields from observations, atmospheric model integrations, or atmospheric data assimilation systems. But land surface observations as well as model estimates are subject to errors. Moreover, satellite observations typically have gaps (in time and in space) and are available only under certain conditions. A land data assimilation system produces enhanced estimates of land surface conditions by correcting the modeled land surface fields toward the observational estimates, with the degree of correction determined by the levels of error associated with each.
Two assimilation strategies are currently pursued at the GMAO. Off-line assimilation involves driving a land surface model (that is not coupled to an atmospheric model) with meteorological forcing data while updating land surface conditions in response to observations. In such a system, the land assimilation has no impact - no feedback - on the land surface forcing and the atmospheric state. In coupled assimilation, the land surface analysis is performed in conjunction with the atmospheric analysis during integrations of the coupled land-atmosphere system. In essence, the assimilated land data then help improve the estimation of atmospheric states, and the assimilated atmospheric data help improve the estimation of land states.
The core of the GMAO land data assimilation system is based on the Ensemble Kalman filter (EnKF), a Monte-Carlo approach to the nonlinear filtering problem. The EnKF is based on the approximation of the conditional probability densities of interest by a finite number of randomly generated model trajectories. It is particularly well suited to the nonlinear and intermittent character of land surface processes.