Assessing the Impact of Assimilating Lagrangian Surface Drifters during the Gulf of Mexico Grand Lagrangian Deployment (GLAD)

Abstract:

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 work, the impact of an augmented-state Lagrangian data assimilation (LaDA) method using Local Ensemble Transform Filter (LETKF) is investigated within a realistic ocean data assimilation (DA) system. We use direct location data from 300 surface drifters released in the Gulf of Mexico (GoM) by the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) during the summer 2012 Grand Lagrangian Deployment (GLAD) experiment. These drifter observations are directly assimilated into a realistic eddy-resolving GoM configuration of the Modular Ocean Model version 6 (MOM6) of the Geophysical Fluid Dynamics Laboratory (GFDL). Ocean states (T/S/U/V) are updated at both the surface and at depth by utilizing dynamic forecast error covariance statistics. Four experiments are conducted: 1) a free run generated by MOM6; 2) a DA experiment assimilating the Lagrangian drifter locations from GLAD; 3) a DA experiment assimilating temperature and salinity profile observations from World Ocean Database 2018 (WOD18); and 4) a DA experiment assimilating both drifter and the profile observations. The LaDA results are then compared with the traditional assimilation using the drifter-derived velocity field from the same GLAD database. In addition, we evaluate the impact of the LaDA algorithm on different eddy-permitting and eddy-resolving model resolutions to determine the most effective horizontal resolutions for assimilating drifter position data using LaDA.