Lagrangian Data Assimilation of Surface Drifters Using the Local Ensemble Transform Kalman Filter

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 defining a new localization method based on drifters in model space, we are managed to implement the Lagrangian assimilation by the Local Ensemble Transform Kalman Filter (LETKF) to update not only the ocean states (T/S/U/V) at surface and in deep ocean, but also the drifter locations as well 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).