ABSTRACT

This article proposes the tracking algorithm based on density clustering of time scale and mesoscale eddy of Kalman filtering using the fused SLA data of altimeter. Firstly, the definitive density clustering based on time scale discovers the potential association pattern between data, and screens out the data set of mesoscale eddy trajectory. With regard to the data set with time scale conflict, it analyzes the Kalman filtering, eliminates the noise points and obtains the correct mesoscale eddy trajectory. Secondly, it turns the tracking process into an algorithm that supports batch processing by applying the data processing method to the mesoscale eddy-tracking algorithm, which solves the problem of single serialization and high time and space complexity of the traditional tracking algorithm. Based on the algorithm, this article selects the experimental data of the South China Sea for the mesoscale eddy-tracking test. The experiment turns out that the algorithm can better reveal the life course of mesoscale eddy and evolution rule of physical oceanography according to spatial scale, amplitude and eddy duration, etc.

摘要

本文利用高度计的融合SLA数据，提出了基于时间尺度密度聚类和卡尔曼滤波中尺度涡度的跟踪算法。首先，基于时间尺度的确定性密度聚类发现数据之间潜在的关联模式，筛选出中尺度涡动轨迹的数据集。针对存在时间尺度冲突的数据集，进行卡尔曼滤波分析，去除噪声点，得到正确的中尺度涡轨迹。其次，将数据处理方法应用到中尺度涡流跟踪算法中，将跟踪过程变成支持批处理的算法，解决了传统跟踪算法序列化单一、时空复杂度高的问题。基于算法，本文选取南海的实验数据进行中尺度涡跟踪试验。实验结果表明，该算法能够根据空间尺度、振幅和涡旋持续时间等，更好地揭示中尺度涡旋的生命历程和物理海洋学的演化规律。