Research on data assimilation and features analysis of storm surge in the Shanghai offshore areas

Ding Jun; Lü Xin; Yao Yaqian; Meng Xin; Jiang Xuemin; Wu Xuyun; Ge Jianzhong

doi:10.12284/hyxb2021049

Volume 43 Issue 3

Apr. 2021

Turn off MathJax

Article Contents

Article Navigation > Haiyang Xuebao > 2021 > 43(3): 135-145

Ding Jun，Lü Xin，Yao Yaqian, et al. Research on data assimilation and features analysis of storm surge in the Shanghai offshore areas[J]. Haiyang Xuebao，2021, 43(3)：135–145 doi: 10.12284/hyxb2021049

Citation:

PDF( 4346 KB)

Research on data assimilation and features analysis of storm surge in the Shanghai offshore areas

doi: 10.12284/hyxb2021049

1.
Shanghai Ocean Monitoring and Forecasting Center, Shanghai 200062, China
2.
East China Sea Forecasting Center, State Oceanic Administration, Shanghai 200136, China
3.
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China

Received Date: 2020-01-07
Rev Recd Date: 2020-05-01

Available Online: 2021-03-13

Publish Date: 2021-04-23

Abstract

Abstract

Storm surge is a complex ocean phenomenon which is sensitive to many factors and has attracted much attention. In this paper, based on covariance localization method of ensemble Kalman filter(EnKF), the storm surge data of different sources, different error and different spatial and temporal resolution calculated by tide gauge stations and FVCOM model were assimilated and fused for the first time. Taking the storm surge process of typhoon 201810 landing in Shanghai for example, the optimal solution of 72-hourly storm surge in the Shanghai offshore areas was obtained and verified, the setting range of set sample number and Schur radius were given. The results show that the root mean square error of storm surge calculated by the observed stations and the model is 0.20 m, while calculated by the observed stations and assimilation is 0.07 m, which is improved by 65%, the root mean square error calculated by independent observation and assimilation is 0.09 m, the ratio of set dispersion to root mean square error is 0.90, the assimilation effect is better and credible. The assimilated storm water increment field can clearly and accurately depict the characteristics of double peaks storm surge, typhone eye surge and frontal surge which can be better used for the research of storm surge, correction of numerical simulation and marine disaster prevention.
- storm tide,
- storm surge,
- data assimilation,
- EnKF,
- marine disaster,
- Shanghai offshore areas

FullText(HTML)

References(43)

References

[1]	Dube S K, Rao A D, Sinha P C, et al. Storm surges: worst coastal marine hazard[M]//Murthy C R, Sinha P C, Rao Y R. Modelling and Monitoring of Coastal Marine Processes. Dordrecht: Springer, 2008: 125−140.
[2]	Maduwantha M G P, Wijayaratna T M N. An estimation of storm surge heights of colombo coastline[C]//Moratuwa Engineering Research Conference (MERCon). Moratuwa, Sri Lanka: IEEE, 2019.
[3]	NOAA. Introduction to storm surge guide[EB/OL]. [2020−11−10]. https://www.nhc.noaa.gov/surge/surge_intro.pdf.
[4]	王喜年, 叶琳. 我国沿海风暴潮的监测及其预报[J]. 海洋预报, 1992, 9(3): 28−37. doi: 10.11737/j.issn.1003-0239.1992.03.004 Wang Xinian, Ye Lin. Monitoring and forecasting of storm surges along the coastal areas of China[J]. Marine Forecasts, 1992, 9(3): 28−37. doi: 10.11737/j.issn.1003-0239.1992.03.004
[5]	刘俊. 关注风暴潮、巨浪、潮汐[M]. 北京: 军事科学出版社, 2011. Liu Jun. Focus on Storm Surges, Huge Waves, and Tides[M]. Beijing: Military Science Press, 2011.
[6]	Thuy N B, Tien T Q, Wettre C, et al. Monsoon-induced surge during high tides at the southeast coast of vietnam: a numerical modeling study[J]. Geosciences, 2019, 9(2): 72. doi: 10.3390/geosciences9020072
[7]	冯士筰, 李凤岐, 李少菁. 海洋科学导论[M]. 北京: 高等教育出版社, 1999. Feng Shizuo, Li Fengqi, Li Shaojing. An Introduction to Marine Science[M]. Beijing: Higher Education Press, 1999.
[8]	Chen Nan, Han Guoqi, Yang Jingsong, et al. Hurricane Sandy storm surges observed by HY-2A satellite altimetry and tide gauges[J]. Journal of Geophysical Research: Oceans, 2014, 119(7): 4542−4548. doi: 10.1002/2013JC009782
[9]	周黔生. 对风暴潮增水及其计算方法的一点见解[J]. 海洋预报, 1992, 9(3): 56−59. doi: 10.11737/j.issn.1003-0239.1992.03.007 Zhou Qiansheng. Point of view on storm surge water and its calculation method[J]. Marine Forecasts, 1992, 9(3): 56−59. doi: 10.11737/j.issn.1003-0239.1992.03.007
[10]	Evensen G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics[J]. Journal of Geophysical Research: Oceans, 1994, 99(C5): 10143−10162. doi: 10.1029/94JC00572
[11]	Houtekamer P L, Zhang Fuqing. Review of the ensemble Kalman filter for atmospheric data assimilation[J]. Monthly Weather Review, 2016, 144(12): 4489−4532. doi: 10.1175/MWR-D-15-0440.1
[12]	Houtekamer P L, Mitchell H L. Data assimilation using an ensemble Kalman filter technique[J]. Monthly Weather Review, 1998, 126(3): 796−811. doi: 10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2
[13]	Butler T, Altaf M U, Dawson C, et al. Data assimilation within the advanced circulation (ADCIRC) modeling framework for hurricane storm surge forecasting[J]. Monthly Weather Review, 2012, 140(7): 2215−2231. doi: 10.1175/MWR-D-11-00118.1
[14]	Altaf M U, Butler T, Mayo T, et al. A comparison of ensemble Kalman filters for storm surge assimilation[J]. Monthly Weather Review, 2014, 142(8): 2899−2914. doi: 10.1175/MWR-D-13-00266.1
[15]	Colle B A, Bowman M J, Roberts K J, et al. Exploring water level sensitivity for Metropolitan New York during Sandy (2012) using ensemble storm surge simulations[J]. Journal of Marine Science and Engineering, 2015, 3(2): 428−443. doi: 10.3390/jmse3020428
[16]	Etala P, Saraceno M, Echevarría P, et al. An investigation of ensemble-based assimilation of satellite altimetry and tide gauge data in storm surge prediction[J]. Ocean Dynamics, 2015, 65(3): 435−447. doi: 10.1007/s10236-015-0808-z
[17]	冯士筰. 风暴潮导论[M]. 北京: 科学出版社, 1982. Feng Shizuo. Introduction to Storm Surge[M]. Beijing: Science Press, 1982.
[18]	赵长进. 长江口及其邻近海区无结构网格风暴潮模式的建立与应用[D]. 上海: 华东师范大学, 2014: 1−69. Zhao Changjin. Development and analysis of the unstructured numerical model for storm surge in the Changjiang Estuary[D]. Shanghai: East China Normal University, 2014: 1−69.
[19]	Wahl T, Mudersbach C, Jensen J. Statistical assessment of storm surge scenarios within integrated risk analyses[J]. Coastal Engineering Journal, 2015, 57(1): 1540003.
[20]	Houtekamer P L, Mitchell H L. A sequential ensemble Kalman filter for atmospheric data assimilation[J]. Monthly Weather Review, 2001, 129(1): 123−137. doi: 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2
[21]	庄照荣, 薛纪善, 李兴良. GRAPES集合卡尔曼滤波资料同化系统Ⅰ:系统设计及初步试验[J]. 气象学报, 2011, 69(4): 620−630. Zhuang Zhaorong, Xue Jishan, Li Xingliang. The GRAPES ensemble Kalman filter data assimilation system. Part I: design and its tentative experiment[J]. Acta Meteorologica Sinica, 2011, 69(4): 620−630.
[22]	Oregon State University. TPXO global tide models[DB/OL]. [2019−11−12]. http://volkov.oce.orst.edu/tides/global.html.
[23]	《海洋图集》编委会. 渤海黄海东海海洋图集(水文)[M]. 北京: 海洋出版社, 1993. Editorial Board for Marine Atlas. Marine Atlas of the Bohai Sea, Yellow Sea and East China Sea (Hydrology)[M]. Beijing: China Ocean Press, 1993.
[24]	林佳. 台风侵袭下的超级城市上海[J]. 中国减灾, 2018(19): 18−21. doi: 10.3969/j.issn.1002-4549.2018.19.007 Lin Jia. The super city of Shanghai under the typhoon[J]. Disaster Reduction in China, 2018(19): 18−21. doi: 10.3969/j.issn.1002-4549.2018.19.007
[25]	中央气象台. 台风网[DB/OL]. [2019−11−20]. http://typhoon.nmc.cn/web.html. National Meteorological Center. The typhoon web[DB/OL]. [2019−11−20]. http://typhoon.nmc.cn/web.html.
[26]	陈宗镛. 潮汐学[M]. 北京: 科学出版社, 1980. Chen Zongyong. Tidology[M]. Beijing: Science Press, 1980.
[27]	Chen Changsheng, Liu Hedong, Beardsley R C. An unstructured grid, finite-volume, three-dimensional, primitive equations ocean model: application to coastal ocean and estuaries[J]. Journal of Atmospheric and Oceanic Technology, 2003, 20(1): 159−186. doi: 10.1175/1520-0426(2003)020<0159:AUGFVT>2.0.CO;2
[28]	Chen C, Beardsley R C, Cowles G, et al. An unstructured grid, finite-volume community ocean model FVCOM user manual[R]. Cambridge: NOAA, 2013.
[29]	Yang Zhaoqing, Wang Taiping, Leung R, et al. A modeling study of coastal inundation induced by storm surge, sea-level rise, and subsidence in the Gulf of Mexico[J]. Natural Hazards, 2014, 71(3): 1771−1794. doi: 10.1007/s11069-013-0974-6
[30]	Leith C S. Theoretical skill of Monte Carlo forecast[J]. Monthly Weather Review, 1974, 102(6): 409−418. doi: 10.1175/1520-0493(1974)102<0409:TSOMCF>2.0.CO;2
[31]	Palmer T N, Mureau R, Molteni F. The Monte Carlo forecast[J]. Weather, 1990, 45(6): 198−207. doi: 10.1002/j.1477-8696.1990.tb05619.x
[32]	韩培, 舒红, 许剑辉. EnKF同化的背景误差协方差矩阵局地化对比研究[J]. 地球科学进展, 2014, 29(10): 1175−1185. doi: 10.11867/j.issn.1001-8166.2014.10.1175 Han Pei, Shu Hong, Xu Jianhui. A comparative study of background error covariance localization in EnKF data assimilation[J]. Advances in Earth Science, 2014, 29(10): 1175−1185. doi: 10.11867/j.issn.1001-8166.2014.10.1175
[33]	Gaspari G, Cohn S E. Construction of correlation functions in two and three dimensions[J]. Quarterly Journal of the Royal Meteorological Society, 1999, 125(554): 723−757. doi: 10.1002/qj.49712555417
[34]	ECMWF. Evaluation of probabilistic prediction systems[EB/OL]. [2019−11−25]. https://www.ecmwf.int/en/elibrary/12555-evaluation-probabilistic-prediction-systems.
[35]	Jolliffe I T, Stephenson D B. Forecast Verification: A Practitioner's Guide in Atmospheric Science[M]. Chichester: Wiley and Sons Ltd, 2003.
[36]	范宇恩. GRAPES区域集合预报侧边界及模式扰动试验[D]. 成都: 成都信息工程大学, 2019. Fan Yuen. The lateral boundary and mode disturbance test of GRAPES region ensemble prediction model[D]. Chengdu: Chengdu University of Information Engineering, 2019.
[37]	高士博, 闵锦忠, 黄丹莲. EnKF局地化算法对雷达资料同化的影响研究[J]. 大气科学学报, 2016, 39(5): 633−642. Gao Shibo, Min Jinzhong, Huang Danlian. Research on the impact of localization methods on radar data assimilation using the ensemble Kalman filter[J]. Transactions of Atmospheric Sciences, 2016, 39(5): 633−642.
[38]	陈吉余, 沈焕庭, 恽才兴, 等. 长江河口动力过程和地貌演变[M]. 上海: 上海科学技术出版社, 1990. Chen Jiyu, Shen Huanting, Yun Caixing, et al. The Dynamical Process and Physiognomy Evolvement in the Yangtze River[M]. Shanghai: Shanghai Scientific and Technical Publishers, 1990.
[39]	张文舟, 胡建宇, 商少平, 等. 福建沿海风暴潮特征的分析[J]. 海洋通报, 2004, 23(3): 12−19. doi: 10.3969/j.issn.1001-6392.2004.03.002 Zhang Wenzhou, Hu Jianyu, Shang Shaoping, et al. On the characteristics of storm surges along Fujian coast[J]. Marine Science Bulletin, 2004, 23(3): 12−19. doi: 10.3969/j.issn.1001-6392.2004.03.002
[40]	刘俊民, 余新晓. 水文与水资源学[M]. 北京: 中国林业出版社, 1999. Liu Junmin, Yu Xinxiao. Hydrology and Water Resources[M]. Beijing: China Forestry Publishing House, 1999.
[41]	盖尔·埃文森. 数据同化—集合卡尔曼滤波[M]. 2版. 刘厂, 赵玉新, 高峰, 译. 北京: 国防工业出版社, 2017. Evensen G. Data Assimilation—The Ensemble Kalman Filter[M]. 2nd ed. Liu Chang, Zhao Yuxin, Gao Feng, trans. Beijing: National Defence Industry Press, 2017.
[42]	王泽人, 马荣华, 段洪涛, 等. 基于集合卡尔曼滤波的太湖叶绿素a浓度同化试验系统设计及实现[J]. 中国科学院大学学报, 2013, 30(5): 628−636. Wang Zeren, Ma Ronghua, Duan Hongtao, et al. Design and implementation of an experimental data assimilation system for chlorophyll-a in Lake Taihu based on the ensemble Kalman filter[J]. Journal of University of Chinese Academy of Sciences, 2013, 30(5): 628−636.
[43]	Hamill T M, Whitaker J S, Snyder C. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter[J]. Monthly Weather Review, 2001, 129(11): 2776−2790. doi: 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2