Preliminary Application of Control Theory in Ocean Circulation Simulation: A Study of Ensemble Kalman Filter Data Assimilation Method

<jats:p>Ocean circulation models are essential tools for understanding and predicting marine dynamics, yet they often suffer from systematic biases and uncertainties in initial conditions. This study presents a preliminary application of control theory to ocean circulation simulation through the implementation of the Ensemble Kalman Filter (EnKF) data assimilation method in the East China Sea. Using a highresolution ocean model configured for the Kuroshio Current system at the PN section based on real observational characteristics from Andres et al. (2008), we conducted 30 independent 20-year simulation experiments (totaling 600 simulation years) comparing free-running model output, EnKFassimilated results, and synthetic observations. The EnKF assimilation system with 30 ensemble members and 30-day assimilation cycles significantly reduced the root mean square error (RMSE) of volume transport from 3.457 ± 0.019 Sv to 3.009 ± 0.127 Sv, representing a mean improvement of 12.96% (p &lt; 0.0001, Cohen's d = 4.839). The systematic bias was reduced from -2.292 Sv to 0.030 Sv (98.7% reduction). All 30 experiments showed consistent improvement, ranging from 3.75% to 20.32%, demonstrating robust performance. Comprehensive reliability analysis including Shapiro-Wilk normality tests, Levene variance homogeneity test, and 95% confidence interval analysis confirmed the statistical validity of the results. The 95% confidence interval for RMSE improvement was [0.396, 0.500] Sv. These results highlight the potential of control theory methods to enhance ocean circulation prediction accuracy and reliability, providing a foundation for operational ocean forecasting systems.</jats:p>