Skill-assessments of statistical and Ensemble Kalman Filter data assimilative analyses using surface and deep observations in the Gulf of Mexico

A new data assimilation algorithm （Quasi- EnKF） in ocean modeling, based on the Ensemble Kalman Filter scheme, is proposed in this paper. This algorithm assimilates not only surface measurements （sea surface height）, but also deep （-2000 m） temperature observations from the Gulf of Mexico into regional ocean models. With the use of the Princeton Ocean Model （POM）, integrated for approximately two years by assimilating both surface and deep observations, this new algorithm was compared to an existing assimilation algorithm （Mellor-Ezer Scheme） at different resolutions. The results show that, by comparing the observations, the new algorithm out-performs the existing one.

基于海洋机器人观测的高分辨率海洋环境预测方法

海洋机器人是海洋环境观测应用的重要工具。利用海洋机器人获取的稀疏观测数据,实现区域海洋环境要素空间场高分辨率预测具有重要应用需求。面向该需求,提出一种基于海洋机器人稀疏观测数据的高分辨率海洋环境预测方法。以卷积长短期记忆神经网络ConvLSTM为基础,建立数据驱动的海洋环境空间场时间序列预测模型。应用集合卡尔曼滤波方法同化海洋机器人观测数据,实现神经网络预测模型在线学习,使模型逐步逼近海洋环境状态、预测输出逐步逼近观测数据。针对大尺度海洋环境高空间分辨率预测带来的高计算复杂性问题,引入非均匀空间分辨率预测策略,有效应对计算挑战。利用水下滑翔机南海北部观测实验数据进行了区域海洋环境二维水平面温度场预测实验,结果验证了方法的有效性。