Abstract:
The land surface freeze/thaw (F/T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and net primary productivity at the land surface. To support the level 4 soil moisture and carbon products (value-added, i.e. using a combination of remote sensing data and modeling) for the planned NASA Soil Moisture Active Passive (SMAP) mission, an F/T assimilation algorithm is developed for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F/T state in the GEOS-5 Catchment land surface model. A rule-based approach that incorporates model and observational errors is developed and used for assimilating the categorical F/T measurements into the land surface model (F/T analysis). An Observing System Simulation Experiment is conducted using synthetically generated measurements of the F/T state for a region in North America (90-110 W longitude, 45-55 N latitude). The synthetic 'truth' is generated using the NASA Catchment land surface model forced with surface meteorological fields from the Modern-Era Retrospective Reanalysis for Research and Applications (MERRA). To generate synthetic measurements, the true categorical F/T state is corrupted with a prescribed amount of F/T classification error. The assimilation experiment employs the same Catchment model except that forcing errors (relative to truth) are introduced via the application of meteorological forcing fields from the Global Land Data Assimilation System (GLDAS). The effect of the F/T analysis and classification error on land surface temperature and soil temperature predictions is examined in this research.