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Estimating temperature and salinity profiles using empirical orthogonal functions and clustering on historical measurements

机译：使用经验正交函数估算温度和盐度分布并基于历史测量结果进行聚类

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摘要

Oceanographic climatology is normally estimated by dividing the world’s oceans into geographical boxes of fixed shape and size, where each box is represented by a climatological salinity and temperature profile. The climatological profile is typically an average of historical measurements from that region. Since an arbitrarily chosen box may contain different types of water masses both in space and time, an averaged profile may be a statistically improbable or even non-physical representation. This paper proposes a new approach that employs empirical orthogonal functions in combination with a clustering technique to divide the world’s oceans into climatological regions. Each region is represented by a cluster that is determined by minimising the variance of the state variables within each cluster. All profiles contained in a cluster are statistically similar to each other and statistically different from profiles in other clusters. Each cluster is then represented by mean temperature and salinity profiles and a mean position. Methods for estimating climatological profiles from the cluster information are examined, and their performances are compared to a conventional method of estimating climatology. The comparisons show that the new methods outperform conventional methods and are particularly effective in areas where oceanographic fronts are present.
机译：通常通过将世界上的海洋划分为形状和大小固定的地理方框来估计海洋气候，其中每个方框都由气候盐度和温度分布表示。气候概况通常是该地区历史测量值的平均值。由于任意选择的框在空间和时间上都可能包含不同类型的水团，因此平均轮廓可能在统计上是不可能的，甚至是非物理的表示。本文提出了一种新方法，该方法将经验正交函数与聚类技术结合使用，将世界海洋划分为气候区域。每个区域由一个聚类表示，该聚类通过最小化每个聚类中状态变量的方差来确定。集群中包含的所有概要文件在统计上彼此相似，并且在统计上与其他集群中的概要文件不同。然后，用平均温度和盐度曲线以及平均位置表示每个聚类。从聚类信息估计气候概况的方法进行了检查，并将它们的性能与传统的估计气候方法进行了比较。比较表明，新方法优于常规方法，并且在存在海洋学前沿的地区特别有效。

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