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Objective array design for three-dimensional temperature and salinity observation:Application to the South China Sea

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Acta Oceanologica Sinica 2022年第7期41卷 65-77页

作者： Mengxue Qu Zexun Wei Yanfeng Wang Yonggang Wang Tengfei Xu First Institute of Oceanography and Key Laboratory of Marine Science and Numerical ModelingMinistry of Natural ResourcesQingdao266061China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and Technology(Qingdao)Qingdao266237China Shandong Key Laboratory of Marine Science and Numerical Modeling Qingdao266061China

In this study,a moored array optimization tool(MAOT)was developed and applied to the South China Sea(SCS)with a focus on three-dimensional temperature and salinity observations.Application of the MAOT involves two steps:(1)deriving a set of optimal arrays that are independent of each other for different variables at different depths based on an empirical orthogonal function method,and(2)consolidating these arrays using a K-center clustering algorithm.Compared with the assumed initial array consisting of 17 mooring sites located on a 3°×3°horizontal grid,the consolidated array improved the observing ability for three-dimensional temperature and salinity in the SCS with optimization efficiencies of 19.03%and 21.38%,respectively.Experiments with an increased number of moored sites showed that the most cost-effective option is a total of 20 moorings,improving the observing ability with optimization efficiencies up to 26.54%for temperature and 27.25%for salinity.The design of an objective array relies on the ocean phenomenon of interest and its spatial and temporal scales.In this study,we focus on basin-scale variations in temperature and salinity in the SCS,and thus our consolidated array may not well resolve mesoscale processes.The MAOT can be extended to include other variables and multi-scale variability and can be applied to other regions.

关键词： optimal array design observation system simulation experiment South China Sea empirical orthogonal function K-center clustering

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