南海灾害性波浪基本特征研究
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摘要
本文基于1991-2016年全球卫星高度计融合数据对南海灾害性波浪基本特征进行了分析,根据灾害性波浪诱发天气类型不同,将其分为"台风浪"和"非台风浪"。依此主线,对两类波浪在南海不同海域的特征进行了研究,并提出了用于定量研究两类波浪强度关系的台风浪权重系数(W),得到了两类波浪在南海相对强弱关系的分布规律,量化研究了南海灾害性波浪的特征。本文以卫星高度计波高数据为样本进行了极值分析,得到了南海重现期波浪要素整体分布规律,研究发现W值大小与广义极值曲线类型显著相关。
Based on the global merged altimeter wave height database(1991-2016), the characteristics of hazardous waves in the South China Sea(SCS) are investigated. The hazardous waves in the SCS can be classified into typhoon waves and non-typhoon waves in accordance with the inducing weather system. In terms of this classifying standard, a factor termed as typhoon wave weight factor(W) is defined to reveal the quantitative relationship between the typhoon waves and the non-typhoon waves. In accordance with quantitative analysis of the hazardous waves, it is revealed that hazardous waves in different regional oceans are highlighted with different statistical characteristics. Based on the annual extreme waves sampled from the merged altimeter wave data, extreme value analysis is carried out to predict the return period wave height in the SCS. It is proved that there is significant correlation between the W and the types of GEV.
Based on the global merged altimeter wave height database(1991-2016), the characteristics of hazardous waves in the South China Sea(SCS) are investigated. The hazardous waves in the SCS can be classified into typhoon waves and non-typhoon waves in accordance with the inducing weather system. In terms of this classifying standard, a factor termed as typhoon wave weight factor(W) is defined to reveal the quantitative relationship between the typhoon waves and the non-typhoon waves. In accordance with quantitative analysis of the hazardous waves, it is revealed that hazardous waves in different regional oceans are highlighted with different statistical characteristics. Based on the annual extreme waves sampled from the merged altimeter wave data, extreme value analysis is carried out to predict the return period wave height in the SCS. It is proved that there is significant correlation between the W and the types of GEV.
引文
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[2] Dobson E, Monaldo F, Goldhirsh J, et al. Validation of Geosat altimeter-derived wind speeds and significant wave heights using buoy data[J]. Journal of Geophysical Research, 1987, 92(C10): 10719-10731.
[3] Young I R, Zieger S, Babanin A V. Development and application of a global satellite database of wind and wave conditions[C]//Proceedings of the ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. St. John's, Newfoundland, Canada: ASME, 2015.
[4] Vinoth J, Young I R. Global estimates of extreme wind speed and wave height[J]. Journal of Climate, 2011, 24(6): 1647-1665.
[5] 齐义泉, 施平, 毛庆文. 南海海面风场和浪场季平均特征的卫星遥感分析[J]. 中国海洋平台, 1997, 12(3): 118-122. Qi Yiquan, Shi Ping, Mao Qingwen. The satellite remote sensing analysis of seasonal average characteristics of wind field and wave field in South China Sea[J]. China Offshore Platform, 1997, 12(3): 118-122.
[6] 裘沙怡, 梁楚进, 董昌明, 等. 南海海面风、浪场时空变化特征及其关系分析[J]. 海洋学研究, 2013, 31(4): 1-9. Qiu Shayi, Liang Chujin, Dong Changming, et al. Analysis of the temporal and spatial variations in the wind and wave over the South China Sea[J]. Journal of Marine Sciences, 2013, 31(4): 1-9.
[7] 周水华, 俞胜宾, 冯伟忠, 等. 基于多卫星融合资料的南海浪高时空分布特征研究[J]. 海洋科学, 2013, 37(10): 71-77. Zhou Shuihua, Yu Shengbin, Feng Weizhong, et al. Temporal and spatial distribution characteristics of wave height in the South China Sea based on multi-satellite merged data[J]. Marine Sciences, 2013, 37(10): 71-77.
[8] 许富祥. 中国近海及其邻近海域灾害性海浪的时空分布[J]. 海洋学报, 1996, 18(2): 26-31. Xu Fuxiang. Temporal and spatial distribution of hazardous waves in the China sea and the neighboring oceans[J]. Haiyang Xuebao, 1996, 18(2): 26-31.
[9] 邢闯, 李本霞. 中国近海2012年灾害性海浪分析及2013年预测[J]. 海洋预报, 2013, 30(3): 1-8. Xing Chuang, Li Benxia. Analysis of disastrous wave of China seas in 2012 and prediction for 2013[J]. Marine Forecasts, 2013, 30(3): 1-8.
[10] 王祥涛, 王文毅. 我国海洋1966-1990年≥6米灾害性海浪的统计分析[C]//天气海洋与航海安全论文集. 延吉: 中国航海学会, 2000. Wang Xiangtao, Wang Wenyi. Statistcial analysis of disastrous waves with height above 6 m in the China seas from 1966-1990[C]//National Symposium on Weather, Ocean and Marine Safety. Yanji: China Institute of Navigation, 2000.
[11] 周良明, 吴伦宇, 郭佩芳, 等. 应用WAVEWATCH-Ⅲ模式对南海的波浪场进行数值计算、统计分析和研究[J]. 热带海洋学报, 2007, 26(5): 1-8. Zhou Liangming, Wu Lunyu, Guo Peifang, et al. Simulation and study of wave in South China Sea using WAVEWATCH-Ⅲ[J]. Journal of Tropical Oceanography, 2007, 26(5): 1-8.
[12] 王智峰, 董胜. 三沙近海海域灾害性海浪数值推算[J]. 海洋开发与管理, 2013(S1): 98-101. Wang Zhifeng, Dong Sheng. Numerical simulation of disastrous waves in the Sansha oceans[J]. Ocean Development and Management, 2013(S1): 98-101.
[13] 国家海洋局. 国家海洋局公报2016年第1号[R]. 北京: 国家海洋局, 2016. State Oceanic Administration, People′s Republic of China. Bulletin of the state oceanic administration 2016-1[R]. Beijing: State Oceanic Administration, People′s Republic of China, 2016.
[14] Ash E. Deliverable D.7 product user guide phase 3[R]. Leatherhead, Surrey, UK: Logica UK Ltd, 2013.
[15] Ash E, Carter D. Deliverable D. 18 annual quality control report[R]. Leatherhead, Surrey, UK: Logica UK Ltd, 2012.
[16] Viselli A M, Forristall G Z, Pearce B R, et al. Estimation of extreme wave and wind design parameters for offshore wind turbines in the Gulf of Maine using a POT method[J]. Ocean Engineering, 2015, 104: 649-658.
[17] Orimolade A P, Haver S, Gudmestad O T. Estimation of extreme significant wave heights and the associated uncertainties: a case study using NORA10 hindcast data for the Barents Sea[J]. Marine Structures, 2016, 49: 1-17.
[18] Carter D J T. Estimating extreme wave heights in the Ne Atlantic from Geosat data[R]. Brook Road, Wormley, Godalming Surrey GU8 5UB: Institute of Oceanographic Sciences Deacon Laboratory, 1993.
[19] Alves J H G M, Young I R. On estimating extreme wave heights using combined Geosat, Topex/Poseidon and ERS-1 altimeter data[J]. Applied Ocean Research, 2003, 25(4): 167-186.
[20] Challenor P G, Wimmer W, Ashton I. Climate change and extreme wave heights in the north Atlantic[C]//Proceedings of 2004 Envisat & ERS Symposium. Salzburg, Austria: ADS, 2004.
[21] Hughes J D, Vaze J. Non-stationarity driven by long-term change in catchment storage: possibilities and implications[C]//Proceedings of the 26th General Assembly of the International Union of Geodesy and Geophysics. Prague, Czech Republic: IAHS, 2015.
[22] Milly P C D, Betancourt J, Falkenmark M, et al. Stationarity is dead: whither water management?[J]. Science, 2008, 319(5863): 573-574.
[23] Gray V. Climate change 2007: the physical science basis: summary for policymakers[R]. Geneva, Switzerland: Intergovernmental Panel on Climate Change (IPCC), 2007.
[24] Vanem E. Non-stationary extreme value models to account for trends and shifts in the extreme wave climate due to climate change[J]. Applied Ocean Research, 2015, 52: 201-211.
[25] 盛骤, 谢式千, 潘承毅. 概率论与数理统计[M]. 4版. 北京: 高等教育出版社, 2012. Sheng Zhou, Xie Shiqian, Pan Chengyi. Probability Theory and Mathematical Statistics[M]. 4th ed. Beijing: Higher Education Press, 2012.
[26] Gilleland E, Katz R W. ExtRemes 2.0: an extreme value analysis package in R[J]. Journal of Statistical Software, 2016, 72(8):1-39.
[27] Stephens S A, Ramsay D L. Extreme cyclone wave climate in the Southwest Pacific Ocean: influence of the El Niňo Southern Oscillation and projected climate change[J]. Global and Planetary Change, 2014, 123: 13-26.
[28] Klein Tank A M G, Zwiers F W, Zhang X. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation[R]. Geneva, Switzerland: World Meteorological Organization (WMO), 2009.
[29] Katz R W. Statistical Methods for Nonstationary Extremes[M]//AghaKouchak A, Easterling D, Hsu K, et al. Extremes in a Changing Climate: Detection, Analysis and Uncertainty. Dordrecht: Springer, 2013.