Abstract:
Recent progress in the field of land surface data assimilation has
been extensive. Operational systems for the off-line integration of
land surface models with of observed meteorological forcing data are
already in place and are producing regional and global estimates of
land surface conditions for a variety of clients. These systems are
obvious starting points for the development of true data assimilation
systems, i.e., systems that use comprehensive error analysis to
incorporate radiances or geophysical retrievals of land surface
conditions (e.g., soil moisture, snow, surface temperature, and
vegetation state) into the state estimates produced by the land
surface models. Indeed, the time is ripe for the development of such
systems for operational applications, given that land state
information is now provided with unprecedented coverage in time and
space by satellite sensors. Prototype systems already exist and show
the desired improvements in the estimation of land states.
Land surface data assimilation, however, comes with many challenges. The penetration depth of the satellite signal is limited; soil moisture estimates, for example, can only be retrieved by satellite for the top few millimeters of soil when vegetation is sparse and cannot be retrieved at all under dense vegetation. The land surface boasts an extensive heterogeneity relative to that of the ocean and atmosphere, complicating the interpretation of the signal. Furthermore, satellite retrievals and land model variables are generally not directly comparable, sometimes exhibit strikingly different climatologies, and are thus not easily combined. These difficult issues have been the subject of much research in recent years. In the present talk we will discuss the current state of land surface data assimilation, with some emphasis on the supporting research.