Assimilation of Surface Geostrophic Currents in the East Sea Using the Ensemble Kalman Filter

Abstract The conventional ocean data assimilation process typically involves assimilating hydrographic data, such as temperature and salinity measurements, obtained from both satellites and in-situ observations. This study introduces a novel approach to enhance ocean circulation modeling by assimilating surface geostrophic currents derived from satellite altimetry data using the ensemble Kalman filter. To match the time scales for the variability in the observed surface geostrophic currents and the model currents, the current velocities from the model were low-pass filtered. The optimal cut-off period for the low-pass filter was determined to be 31 days in the East Sea. Eight sensitivity experiments were then conducted to examine the effects of observation error and low-pass filtering during the assimilation of surface geostrophic current data. Assimilation experiments with surface geostrophic current data improved surface currents but had minor negative impacts on the temperature and salinity when compared with assimilation experiments without surface geostrophic current data. Notably, the experiment with an observation error of 10 cm/s for the geostrophic current outperformed the other experiments. Surface geostrophic current assimilation improved the sea surface temperature during winter and effectively modified surface current patterns during autumn in the East Sea. Assimilating satellite-derived surface geostrophic currents in the ocean circulation model thus enhanced the accuracy of surface circulation simulation. This improvement in ocean analysis data offers significant benefits for understanding ocean climate change and for developing marine management strategies.