Assimilation of middepth velocities from Argo floats in the western South China Sea
Pinqiang Wang (National University of Defense Technology)
Weimin Zhang (National University of Defense Technology, Laboratory of Software Engineering for Complex Systems)
Huizan Wang (National University of Defense Technology)
Haijin Dai (National University of Defense Technology)
Xiaohui Wang (College of Meteorology and Oceanography, National University of Defense Technology, TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Previous studies are mainly limited to temperature and salinity (T/S) profiling data assimilation, while data assimilation based on Argo float trajectory information has received less research focus. In this study, a new method was proposed to assimilate Argo trajectory data: The middepth (indicates the parking depth of Argo floats in this study, ~1200 m) velocities are estimated from Argo trajectories and subsequently assimilated into the Regional Ocean Model System (ROMS) using four-dimensional variational data assimilation (4DVAR) method. This method can avoid a complicated float trajectory model in direct position assimilation. The 2-month assimilation experiments in South China Sea (SCS) showed that this proposed method can effectively assimilate Argo trajectory information into the model and improve middepth velocity field by adjusting the unbalanced component in the velocity increments. The assimilation of the Argo trajectory-derived middepth velocity with other observations (satellite observations and T/S profiling data) together yielded the best performance, and the velocity fields at the float parking depth are more consistent with the Argo float trajectories. In addition, this method will not decrease the assimilation performance of other observations [i.e., sea level anomaly (SLA), sea surface temperature (SST), and T/S profiles], which is indicative of compatibility with other observations in the 4DVAR assimilation system.