Effect of MODAS Data on Convergence Zone Distance Prediction

Underwater acoustic environment (UAE) prediction is essential for accurately estimating sonar performance and making credible tactical decisions. Modular Ocean Data Assimilation System (MODAS) provides an efficient way to reconstruct real-time three-dimensional ocean sound speed field and has potential use in UAE prediction. However, the effect of MODAS on UAE prediction is not clear, which prevents MODAS from being used in practice. Taking into account that convergence zone (CZ) is a typical feature of deep-sea UAE, this paper studies the effect of MODAS on CZ distance prediction. A method is proposed to describe the accuracy of CZ distance prediction quantitatively. The method takes the CZ distance as a random variable and uses Monte Carlo sampling method to obtain the probability density function (PDF) of CZ distance. The performance of MODAS on CZ distance prediction is numerically simulated in a range-independent and a mesoscale eddy waveguides. It is shown that: (1) In the range-independent waveguide, if the sound speed disturbance of internal waves with respect to the background sound speed profile is less than the RMSE of MODAS, the prediction of CZ distance for local measured sound velocity profile is more accurate. (2) In the mesoscale eddy waveguide, the performance of MODAS is better and the performance improves as the RMSE of MODAS decreases.