2021
DOI: 10.1175/jtech-d-20-0072.1
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Ocean Tides near Hawaii from Satellite Altimeter Data. Part I

Abstract: Sufficient and accurate tide data are essential for analyzing physical processes in the ocean. A method is developed to spatially fit the tidal amplitude and phase lag data along satellite altimeter tracks near Hawaii and construct reliable cotidal charts by using the Chebyshev polynomials. The method is completely dependent on satellite altimeter data. By using the cross-validation method, the optimal orders of Chebyshev polynomials are determined and the polynomial coefficients are calculated by the least sq… Show more

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Cited by 6 publications
(11 citation statements)
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References 37 publications
(42 reference statements)
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“…The harmonic constants calculated by the adjoint assimilation model and the harmonic constants of the X-TRACK satellite are together fitted by the CPF method, in order to obtain the high-resolution harmonic constants in the full field. More details on data, methodology and error assessment can be found in Part I (Xu et al, 2021).…”
Section: The Cpf Methodsmentioning
confidence: 99%
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“…The harmonic constants calculated by the adjoint assimilation model and the harmonic constants of the X-TRACK satellite are together fitted by the CPF method, in order to obtain the high-resolution harmonic constants in the full field. More details on data, methodology and error assessment can be found in Part I (Xu et al, 2021).…”
Section: The Cpf Methodsmentioning
confidence: 99%
“…From the previous experience of other researchers, too high an order can cause overfitting [28]. To avoid over-fitting, the optimal order is determined using the cross-validation method.…”
Section: The Cpf Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…Based on this physical constraint, a fitting function can help construct the marine elements, and the difference between the observed value and the fitted data larger than the given threshold will be discriminated as outliers (Tan et al, 2021). The cubic spline fitting function has become an extremely important numerical fitting method due to its good stability and smoothness, and it has achieved good application results in data analysis (Jiang et al, 2018;Jin et al, 2018;Zong et al, 2018;Wang et al, 2019;Xu et al, 2021). Based on the above situation, this paper aims to propose an outlier detection method based on Cubic Spline Fitting.…”
Section: Introductionmentioning
confidence: 99%