{"title":"Ocean Tides near Hawaii from Satellite Altimeter Data. Part III","authors":"Yibo Zhang, Shengyi Jiao, Yuzhe Wang, Yonggang Wang, Xianqing Lv","doi":"10.1175/jtech-d-22-0052.1","DOIUrl":null,"url":null,"abstract":"Abstract The Chebyshev polynomial fitting (CPF) method has been proved to be effective to construct reliable cotidal charts for the eight major tidal constituents (M 2 , S 2 , K 1 , O 1 , N 2 , K 2 , P 1 , and Q 1 ) and six minor tidal constituents (2N 2 , J 1 , L 2 , Mu 2 , Nu 2 , and T 2 ) near Hawaii in Part I and Part II, respectively. In this paper, this method is extended to estimate the harmonic constants of four long-period tidal constituents (M f , M m , S a , and S sa ). The harmonic constants obtained by this method were compared with those from the TPXO9, Finite Element Solutions 2014 (FES2014), and Empirical Ocean Tide 20 (EOT20) models, using benchmark data from satellite altimeters and eight tide gauges. The accuracies of the M f and M m constituents derived from the CPF method are comparable to those from the models, but the accuracies of the S a and S sa constituents are significantly higher than those from the FES2014 and EOT20 models. The results indicate that the CPF method is also effective for estimating harmonic constants of long-period tidal constituents. Furthermore, since the CPF method relies only on satellite altimeter data, it is an easier-to-use method than these ocean tide models.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":"7 1","pages":"0"},"PeriodicalIF":1.9000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Atmospheric and Oceanic Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1175/jtech-d-22-0052.1","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, OCEAN","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract The Chebyshev polynomial fitting (CPF) method has been proved to be effective to construct reliable cotidal charts for the eight major tidal constituents (M 2 , S 2 , K 1 , O 1 , N 2 , K 2 , P 1 , and Q 1 ) and six minor tidal constituents (2N 2 , J 1 , L 2 , Mu 2 , Nu 2 , and T 2 ) near Hawaii in Part I and Part II, respectively. In this paper, this method is extended to estimate the harmonic constants of four long-period tidal constituents (M f , M m , S a , and S sa ). The harmonic constants obtained by this method were compared with those from the TPXO9, Finite Element Solutions 2014 (FES2014), and Empirical Ocean Tide 20 (EOT20) models, using benchmark data from satellite altimeters and eight tide gauges. The accuracies of the M f and M m constituents derived from the CPF method are comparable to those from the models, but the accuracies of the S a and S sa constituents are significantly higher than those from the FES2014 and EOT20 models. The results indicate that the CPF method is also effective for estimating harmonic constants of long-period tidal constituents. Furthermore, since the CPF method relies only on satellite altimeter data, it is an easier-to-use method than these ocean tide models.
摘要切比雪夫多项式拟合(CPF)方法已经被证明是有效的构建可靠的八大潮汐成分同潮时图表(M 2 S 2 K 1 O, N, K, P,和问1)和六个小潮汐成分(2 N 2, J 1 L 2μ2,ν2,和T 2)夏威夷附近海域在第一部分和第二部分,分别。本文将该方法推广到四种长周期潮汐成分(M f, M M, sa, sa)的谐波常数估计。利用卫星高度计和8个潮汐计的基准数据,将该方法得到的调和常数与TPXO9、Finite Element Solutions 2014 (FES2014)和Empirical Ocean Tide 20 (EOT20)模型的调和常数进行了比较。CPF方法得到的M - f和M - M成分的精度与模型相当,但S - a和S - sa成分的精度显著高于FES2014和EOT20模型。结果表明,CPF方法对长周期潮汐成分的谐波常数估计也是有效的。此外,由于CPF方法仅依赖于卫星高度计数据,因此它比这些海潮模型更容易使用。
期刊介绍:
The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.