Research on data assimilation and features analysis of storm surge in the Shanghai offshore areas
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摘要: 风暴潮是一种复杂的对众多因素敏感又备受关注的海洋现象。本文基于协方差局地化的集合卡尔曼滤波方法（EnKF），选择201810号台风“安比”登陆上海的风暴潮过程，首次将海洋站和FVCOM数值模拟的不同来源、不同误差信息、不同时空分辨率的风暴潮进行数据同化融合，获得了逐72 h的上海海域风暴潮的最优解，进行了同化结果评估验证，并给出了集合样本数和Schur半径设置范围。结果表明，实测计算和数值模拟的风暴增减水之间均方根误差为0.20 m，实测和同化计算的风暴增减水之间均方根误差为0.07 m，准确度提高了65%；独立观测和同化计算的风暴增减水均方根误差为0.09 m，集合离散度与均方根误差比值为0.90，同化效果较好且可信；同化后的风暴增减水能够较好地刻画双峰增水、台风眼增水、增水锋面等特征，对于风暴潮研究、数值模拟结果订正、海洋防灾减灾等有重要意义。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.
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Key words:
- storm tide /
- storm surge /
- data assimilation /
- EnKF /
- marine disaster /
- Shanghai offshore areas
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图 1 数据同化区域、监测站点分布和部分台风路径
Fig. 1 Data assimilation region, monitoring stations and parts of typhoon track
图 2 长江口海域计算网格和水深
Fig. 2 Computing grids and water depth in the Changjiang River Estuary
图 3 同化实验的6个站风暴增减水时间序列
Fig. 3 Storm surge time series of 6 stations participating in assimilation
图 4 未参加同化的3个站风暴增减水时间序列
Fig. 4 Storm surge time series of 3 stations not participating in assimilation
图 6 台风登陆前数值模拟的增减水场
Fig. 6 Storm surge field calculated by model before the typhoon landing
图 7 台风登陆前同化实验的增减水场
Fig. 7 Storm surge field of assimilation before the typhoon landing
图 8 2018年7月22日12:00时长江口断面各站增水值
Fig. 8 Storm surge of the Changjiang River Estuary section stations at 12:00 July 22, 2018
图 9 2018年7月22日12:00时杭州湾北断面各站增水值
Fig. 9 Storm surge of the Hangzhou Bay section stations at 12:00 July 22, 2018
图 10 不同集合样本数及Schur半径同化误差
Fig. 10 The assimilation results error of different set samples and Schur radius
表 1 同化检验实验设计
Tab. 1 Experimental design of assimilation verification
实验方案 实验名称 起始状态 观测资料 步长 方法 实验目标 A 控制实验 2018年7月20日15时（世界时）FVCOM预报结果 无 1 h FVCOM数值模拟 9个站数值模拟的风暴增减水序列（72 h）；典型时刻数值模拟的风暴增减水场 2016年1月1日00时实测水位 9个站实测水位 1 h 调和分析 9个站实测水位计算的风暴增减水序列（72 h） B 同化实验 2018年7月20日15时（世界时）FVCOM预报结果 6个站实测水位计算的风暴增减水序列 1 h EnKF 6个站同化的风暴增减水序列（72 h）；3个站同化的风暴增减水序列（72 h）；典型时刻同化的风暴增减水场 
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表 2 典型时刻9个海洋站增减水对比
Tab. 2 Storm surge comparisons of the 9 stations as typical moment
海洋站 实测水位计算的增水/m 数值模拟的增水/m 同化后的增水/m 实测水位计算与数值模拟 实测水位计算与同化 绝对偏差/m 均方根误差/m 绝对偏差/m 均方根误差/m 崇明南门 0.34 0.06 0.40 0.28 0.31 0.06 0.12 堡镇 0.61 0.06 0.39 0.55 0.22 吴淞 0.39 0.07 0.34 0.32 0.05 高桥 0.46 0.06 0.33 0.40 0.13 佘山 0.38 −0.05 0.36 0.43 0.02 大戢山 0.04 −0.06 0.13 0.10 0.09 芦潮港 0.13 −0.04 0.13 0.17 0.00 滩浒岛 −0.10 −0.05 0.07 0.05 0.17 金山嘴 −0.12 −0.08 0.04 0.04 0.16
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