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上海邻近海域风暴潮数据同化与特征分析

丁骏 吕忻 姚雅倩 孟鑫 姜雪敏 吴旭云 葛建忠

丁骏,吕忻,姚雅倩,等. 上海邻近海域风暴潮数据同化与特征分析[J]. 海洋学报,2021,43(3):135–145 doi: 10.12284/hyxb2021049
引用本文: 丁骏,吕忻,姚雅倩,等. 上海邻近海域风暴潮数据同化与特征分析[J]. 海洋学报,2021,43(3):135–145 doi: 10.12284/hyxb2021049
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: 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

上海邻近海域风暴潮数据同化与特征分析

doi: 10.12284/hyxb2021049
基金项目: 上海市海洋局科研项目(沪海科2018-07)
详细信息
    作者简介:

    丁骏(1986-),男,江苏省东台市人,工程师,主要从事海洋数值预报研究。E-mail:jding@shou.edu.cn

    通讯作者:

    吕忻(1986-),女,工程师,主要从事海洋管理研究。E-mail:lvxin@ecs.mnr.gov.cn

  • 中图分类号: P731.23

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

  • 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。
  • 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  5  集合离散度和均方根误差对比

    Fig.  5  The comparison of set dispersion and root mean square error

    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1 hFVCOM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 hEnKF6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
    下载: 导出CSV

    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0.340.060.400.280.310.060.12
    堡镇0.610.060.390.550.22
    吴淞0.390.070.340.320.05
    高桥0.460.060.330.400.13
    佘山0.38−0.050.360.430.02
    大戢山0.04−0.060.130.100.09
    芦潮港0.13−0.040.130.170.00
    滩浒岛−0.10−0.050.070.050.17
    金山嘴−0.12−0.080.040.040.16
    下载: 导出CSV
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  • 收稿日期:  2020-01-07
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