Assimilation of Freeze–Thaw Observations into the NASA Catchment Land Surface Model
- Citation data:
Journal of Hydrometeorology, ISSN: 1525-755X, Vol: 16, Issue: 2, Page: 730-743
- Publication Year:
- Repository URL:
- https://scholarworks.umt.edu/ntsg_pubs/301; https://scholarworks.umt.edu/cgi/viewcontent.cgi?article=1300&context=ntsg_pubs
- Earth and Planetary Sciences; Data assimilation; Land surface model
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 vegetation productivity at the land surface. In this study, an F/T assimilation algorithm was 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. The F/T analysis is a rulebased approach that adjusts Catchment model state variables in response to binary F/T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F/T observations. The assimilation of perfect (error free) F/T observations reduced the root-mean-square errors (RMSEs) of surface temperature and soil temperature by 0.2068 and 0.061°C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7%and 3.1%, respectively). For a maximum classification error CEof 10%in the synthetic F/T observations, the F/T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178° and 0.036°C, respectively. For CE= 20%, the F/T assimilation still reduces the RMSE of model surface temperature estimates by 0.149°C but yields no improvement over the model soil temperature estimates. The F/T assimilation scheme is being developed to exploit planned F/T products from the NASA Soil Moisture Active Passive (SMAP) mission.