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

The assimilation of position data from Lagrangian observing platforms is underdeveloped in operational applications because of two main challenges: 1) nonlinear growth of model and observation error in the Lagrangian trajectories, and 2) the high dimensionality of realistic models. In this study, we propose a localized Lagrangian data assimilation (LaDA) method that is based on the Local Ensemble Transform Kalman Filter (LETKF). The algorithm is tested with “identical twin” approach of Observing System Simulation Experiments (OSSEs) using a simple double gyre configuration of the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model. Further attempt of using the eddy-resolving model in configuration of Gulf of Mexico (GoM) and Modular Ocean Model version 6 (MOM6) is examined by applying the historical observation of Grand Lagrangian Deployment (GLAD). Results show that with a proper choice of localization radius, the LaDA can outperform conventional assimilation of surface in situ temperature and salinity measurements. The improvements are seen not only in the surface state estimate, but also throughout the ocean column to deep layer. The impacts of localization radius and model error in estimating accuracy of both fluid and drifter states are further investigated.