Exploring the Potential of Strongly Coupled Lagrangian Data Assimilation in an Ocean–Atmosphere System Monthly Weather Review

Precise measurements of ocean surface flow velocities are essential for refining forecasts in a coupled ocean–atmosphere system. While oceanic data are generally sparse, surface drifters present an opportunity by providing detailed and frequently observed sea surface currents, which are a critical component in the dynamics at air–sea interface. Such observations could potentially address the usual data gaps in a coupled ocean–atmosphere assimilation system. In this study, we investigate the implications of assimilating drifter data within a coupled system with intermediate complexity based on a quasigeostrophic model—Modular Arbitrary-Order Ocean–Atmosphere Model (MAOOAM)—using observing system simulation experiments (OSSEs). Two main strategies for assimilating surface drifter data include the Eulerian approach, which translates Lagrangian positions into Eulerian velocity, and the fully Lagrangian method, which integrates both original fluid states and augmented drifter state variables into the system state vector. We evaluated both Lagrangian and Eulerian drifter assimilation techniques using the ensemble transform Kalman filter (ETKF) across two different coupling intensities within MAOOAM between the atmosphere and the ocean: one featuring strong interaction and the other featuring weak interaction. Our findings indicate a clear advantage of the Lagrangian method over the Eulerian, especially in estimating ocean streamfunctions and temperature. When combined with a large ensemble size and a short data assimilation (DA) window, the Lagrangian ensemble method adeptly manages atmospheric state error propagation. Additionally, as a preliminary demonstration, we evaluated a hybrid particle filter/ensemble Kalman filter (PF/EnKF) approach for Lagrangian DA in the coupled system with long DA windows, which can outperform the EnKF under specific configurations. Grant no. NA19NES4320002 Grant no. NA22OAR4320151 Grant no. NA24NESX432C0001