Land data assimilation systems

Soil moisture, temperature, and snow are integrated states, so errors in land surface forcing and parameterization accumulate in these stores, which leads to incorrect surface water and energy partitioning. However, many innovative new high-resolution land surface observations are becoming available that will provide the additional information necessary to constrain land surface predictions at regional to global scales. These constraints can be imposed in two ways. Firstly, by forcing the land surface primarily by observations (such as precipitation and radiation), the often severe atmospheric numerical weather prediction land surface forcing biases can be avoided. Secondly, by employing innovative land surface data assimilation techniques, observations of land surface storages such as soil temperature and moisture can be used to constrain unrealistic simulated storages. Land data assimilation systems (LDAS), are basically uncoupled land surface models that are forced primarily by observations, and are therefore not affected by NWP forcing biases. Land data assimilation systems also have the ability to maximize the utility of limited land surface observations by propagating their information throughout the land system to unmeasured times and locations