Lagrangian data assimilation of surface drifters using the Local Ensemble Transform Kalman Filter (LETKF)

An Oceanic-LETKF data assimilation system has been designed for the National Centers for Environmental Prediction (NCEP), as the ensemble component of the pre-operational Hybrid Global Ocean Data Assimilation System (Hybrid-GODAS). As is traditional, the existing Hybrid-GODAS assimilates only in situ temperature and salinity profiles, and satellite surface measurements. To make better use of surface drifters from the Global Drifter Program (GDP), we have implemented the capability to perform Lagrangian assimilation of the surface drifter positions. By using covariances between the 3D ocean state and the drifter positions, we are able to update both the ocean state (T/S/U/V) and drifter locations from the drifter observations alone. We conduct Observing System Simulation Experiments (OSSEs) to evaluate the impact of assimilating (a) drifters alone, (b) profiles alone, and (c) both drifters and profiles. These experiments are in preparation to use real observations of GDP drifter locations in combination with the in situ profiles and satellite data to enhance the next generation NOAA/NCEP Climate Forecast System (CFSv3).