Wave height statistical characteristic analysis
Abstract When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. The...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Guilin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Chinese journal of oceanology and limnology - Beijing : Science Press, 1982, 37(2018), 2 vom: 06. Mai, Seite 448-460 |
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| Übergeordnetes Werk: |
volume:37 ; year:2018 ; number:2 ; day:06 ; month:05 ; pages:448-460 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00343-019-8006-3 |
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SPR004283287 |
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| 520 | |a Abstract When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MF-DFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data. | ||
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10.1007/s00343-019-8006-3 doi (DE-627)SPR004283287 (SPR)s00343-019-8006-3-e DE-627 ger DE-627 rakwb eng Liu, Guilin verfasserin aut Wave height statistical characteristic analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MF-DFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data. wave height (dpeaa)DE-He213 partition function (dpeaa)DE-He213 multifractal spectrum (dpeaa)DE-He213 multifractal detrended fluctuation analysis (MF-DFA) (dpeaa)DE-He213 signal mode decomposition (dpeaa)DE-He213 Chen, Baiyu aut Wang, Liping aut Zhang, Shuaifang aut Zhang, Kuangyuan aut Lei, Xi aut Enthalten in Chinese journal of oceanology and limnology Beijing : Science Press, 1982 37(2018), 2 vom: 06. Mai, Seite 448-460 (DE-627)521479606 (DE-600)2262662-1 1993-5005 nnns volume:37 year:2018 number:2 day:06 month:05 pages:448-460 https://dx.doi.org/10.1007/s00343-019-8006-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_374 GBV_ILN_612 GBV_ILN_2522 GBV_ILN_2700 AR 37 2018 2 06 05 448-460 |
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10.1007/s00343-019-8006-3 doi (DE-627)SPR004283287 (SPR)s00343-019-8006-3-e DE-627 ger DE-627 rakwb eng Liu, Guilin verfasserin aut Wave height statistical characteristic analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MF-DFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data. wave height (dpeaa)DE-He213 partition function (dpeaa)DE-He213 multifractal spectrum (dpeaa)DE-He213 multifractal detrended fluctuation analysis (MF-DFA) (dpeaa)DE-He213 signal mode decomposition (dpeaa)DE-He213 Chen, Baiyu aut Wang, Liping aut Zhang, Shuaifang aut Zhang, Kuangyuan aut Lei, Xi aut Enthalten in Chinese journal of oceanology and limnology Beijing : Science Press, 1982 37(2018), 2 vom: 06. Mai, Seite 448-460 (DE-627)521479606 (DE-600)2262662-1 1993-5005 nnns volume:37 year:2018 number:2 day:06 month:05 pages:448-460 https://dx.doi.org/10.1007/s00343-019-8006-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_374 GBV_ILN_612 GBV_ILN_2522 GBV_ILN_2700 AR 37 2018 2 06 05 448-460 |
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10.1007/s00343-019-8006-3 doi (DE-627)SPR004283287 (SPR)s00343-019-8006-3-e DE-627 ger DE-627 rakwb eng Liu, Guilin verfasserin aut Wave height statistical characteristic analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MF-DFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data. wave height (dpeaa)DE-He213 partition function (dpeaa)DE-He213 multifractal spectrum (dpeaa)DE-He213 multifractal detrended fluctuation analysis (MF-DFA) (dpeaa)DE-He213 signal mode decomposition (dpeaa)DE-He213 Chen, Baiyu aut Wang, Liping aut Zhang, Shuaifang aut Zhang, Kuangyuan aut Lei, Xi aut Enthalten in Chinese journal of oceanology and limnology Beijing : Science Press, 1982 37(2018), 2 vom: 06. Mai, Seite 448-460 (DE-627)521479606 (DE-600)2262662-1 1993-5005 nnns volume:37 year:2018 number:2 day:06 month:05 pages:448-460 https://dx.doi.org/10.1007/s00343-019-8006-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_374 GBV_ILN_612 GBV_ILN_2522 GBV_ILN_2700 AR 37 2018 2 06 05 448-460 |
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10.1007/s00343-019-8006-3 doi (DE-627)SPR004283287 (SPR)s00343-019-8006-3-e DE-627 ger DE-627 rakwb eng Liu, Guilin verfasserin aut Wave height statistical characteristic analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MF-DFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data. wave height (dpeaa)DE-He213 partition function (dpeaa)DE-He213 multifractal spectrum (dpeaa)DE-He213 multifractal detrended fluctuation analysis (MF-DFA) (dpeaa)DE-He213 signal mode decomposition (dpeaa)DE-He213 Chen, Baiyu aut Wang, Liping aut Zhang, Shuaifang aut Zhang, Kuangyuan aut Lei, Xi aut Enthalten in Chinese journal of oceanology and limnology Beijing : Science Press, 1982 37(2018), 2 vom: 06. Mai, Seite 448-460 (DE-627)521479606 (DE-600)2262662-1 1993-5005 nnns volume:37 year:2018 number:2 day:06 month:05 pages:448-460 https://dx.doi.org/10.1007/s00343-019-8006-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_374 GBV_ILN_612 GBV_ILN_2522 GBV_ILN_2700 AR 37 2018 2 06 05 448-460 |
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10.1007/s00343-019-8006-3 doi (DE-627)SPR004283287 (SPR)s00343-019-8006-3-e DE-627 ger DE-627 rakwb eng Liu, Guilin verfasserin aut Wave height statistical characteristic analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MF-DFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data. wave height (dpeaa)DE-He213 partition function (dpeaa)DE-He213 multifractal spectrum (dpeaa)DE-He213 multifractal detrended fluctuation analysis (MF-DFA) (dpeaa)DE-He213 signal mode decomposition (dpeaa)DE-He213 Chen, Baiyu aut Wang, Liping aut Zhang, Shuaifang aut Zhang, Kuangyuan aut Lei, Xi aut Enthalten in Chinese journal of oceanology and limnology Beijing : Science Press, 1982 37(2018), 2 vom: 06. Mai, Seite 448-460 (DE-627)521479606 (DE-600)2262662-1 1993-5005 nnns volume:37 year:2018 number:2 day:06 month:05 pages:448-460 https://dx.doi.org/10.1007/s00343-019-8006-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_374 GBV_ILN_612 GBV_ILN_2522 GBV_ILN_2700 AR 37 2018 2 06 05 448-460 |
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Wave height statistical characteristic analysis |
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Liu, Guilin |
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Chinese journal of oceanology and limnology |
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Chinese journal of oceanology and limnology |
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eng |
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2018 |
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Liu, Guilin Chen, Baiyu Wang, Liping Zhang, Shuaifang Zhang, Kuangyuan Lei, Xi |
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37 |
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Elektronische Aufsätze |
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Liu, Guilin |
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10.1007/s00343-019-8006-3 |
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wave height statistical characteristic analysis |
| title_auth |
Wave height statistical characteristic analysis |
| abstract |
Abstract When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MF-DFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data. © Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstractGer |
Abstract When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MF-DFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data. © Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MF-DFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data. © Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 |
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Wave height statistical characteristic analysis |
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Chen, Baiyu Wang, Liping Zhang, Shuaifang Zhang, Kuangyuan Lei, Xi |
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Chen, Baiyu Wang, Liping Zhang, Shuaifang Zhang, Kuangyuan Lei, Xi |
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2024-07-04T00:28:35.708Z |
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