基于演化算符的南海海面高度异常中长期统计预报

水下移动平台行动时需要1～3个月左右海洋数值预测预报结果，但是当前数值预报技术受对应的气象驱动场预报时效的限制，难以提供10 d以上的数值预报产品。鉴于海水在动力热力上具有较大的惯性，海洋内区有其自身的演化规律，本研究设计了一种基于演化算符的统计预测方法，利用历史卫星遥感资料构建海洋状态变量中长期演化矩阵，并结合惯性预报模型，构建了最终的南海海洋中长期统计预报模型，能够提供1～60 d逐日的南海海面高度异常预测结果，开展数值试验验证了该方法的有效性，结果表明，在起报后15 d内，预报结果与卫星资料的相关系数均大于0.8，在起报60 d内，相关系数仍高于0.6。

Medium and long term statistical prediction of sea surface height anomaly in the South China Sea based on evolutionary operator

When performing work, underwater platforms usually need about 1-month to 3-months ocean numerical forecasts results. However, the current numerical forecasting technology is limited to the corresponding weather-driven field forecasting, and it is difficult to provide numerical forecast products for more than 10 days. Considering that the seawater itself has large inertia physically, and the inner ocean area has its own evolutionary principles, a statistical prediction method based on evolutionary operator is proposed. The method uses historical satellite remote sensing data to construct a long-term evolution matrix of ocean state variables, and then combines with the inertial prediction model. The final medium- and long-term statistical prediction model of the South China Sea is constructed, which can provide the prediction results for 1 d to 60 d of daily sea surface height anomaly in the South China Sea. We carry out the numerical experiments to show the effectiveness of the method. The results show that the correlation coefficients between the forecast results and the satellite data are all higher than 0.8 within 15 days and higher than 0.6 within 60 days after the start date of forecast.