青岛近海夏季表层海流特征分析及数值同化研究
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摘要
利用布放在青岛近海的高频地波雷达观测海流,结合浮标记录海流和气象要素数据、验潮站水位数据和锚系ADCP海流数据等多种观测数据,本文对胶州湾及青岛近海区域的2008年夏季表层海流特征进行了综合分析；在观测数据分析的基础上,进一步从数值模拟角度对研究海域的潮汐动力过程进行了数值模拟,对地波雷达资料进行数值同化研究。
首先,对观测的表层海流进行可靠性检验,对比雷达观测海流和浮标观测海流,二者之间的误差约为10cm/s,且在统计意义上高度相关,说明雷达海流数据质量可靠,能够较好地刻画出青岛近海表层海流的特点。
进一步通过气象要素数据、验潮站水位数据和锚系ADCP海流数据分析青岛近海的表层海流特征,揭示潮汐,风,地形以及水位等因素对研究区域表层流的影响,特别是由观测数据揭示了青岛近海区域潮流椭圆类型的空间分布特征。结合气象观测数据与海流数据使我们认识了局地风对青岛近海表层日均余流的控制作用。综合利用海流数据,水位记录和风速数据,揭示了控制胶州湾湾口夏季出入流动量平衡的因子：由胶州湾内外水位差诱导的压强梯度力和随天文潮变化的非线性潮流应力。从雷达海流计算的表层辐合辐散,揭示了局地上升流的频发区域,浮标记录的温度数据也揭示了潮流引起的局地辐合辐散对温度日变化的重要影响。
其次,将FVCOM数值模型,雷达海流和集合卡曼滤波方法相结合,构建青岛近海表层海流数值同化系统。针对青岛近海区域的海流特点,我们对数值同化系统的关键部分进行了完善,主要包括：
1,以集合卡曼滤波方法为基础,采用不同的数值同化方案(传统集合卡曼滤波和准集合卡曼滤波),对比两种不同方案对数值预报结果误差的改善,以及对预报海流空间结构的改进,同时也考虑到两种不同方案在计算效率上的差异,确定准集合卡曼滤波方法为本数值同化系统的最佳方案。
2,鉴于数据同化效果对背景误差估计的敏感性,我们选取三种不同的背景误差生成方法：蒙特卡洛方法；快速生成方法；数据不确定性产生器方法。通过对比三种方法在本数值同化系统的同化效果,确定其适用性。在实施三种方法的过程中,充分考虑了数值模型的特点以及青岛近海的潮汐潮流特征。
通过一系列数值同化实验测试以上两部分的改善,以数值预报结果的均方根误差得到最大程度减少为标准,确立了适用于青岛近海的最优同化方案和背景误差估计方法,即以准集合卡曼滤波方法为方案,通过快速生成方法估计背景误差场。同时,我们还研究了数值同化系统对状态集合个数的敏感性,发现在本数值同化系统中,状态集合个数不超过50为宜。
This paper combines surface current measured by High Frequency Radar deployed in Qingdao Coastal sea, meteorologic variables recorded by buoy, sea level from tide gauge and current of mooring ADCP,try to analyze the summer surface current over Qingdao near-shore and Jiaozhou Bay area, then simulate the tidal dynamics in study area and assimilate these summer surface current into numerical model.
Before the data assimilation, these surface currents are validated. We also compare the HF radar current and Buoy current, find that their averaged difference is about 10cm/s, and they are highly statistically correlated, meaning that HF radar currents are credible, and able to capture the feature of the near shore current.
In order to analyze the characteristics of Qingdao near-shore current, data analysis with all available dataset are performed. The result of current analysis reveals the influence from tide, wind, topography and sea level on surface current, particularly, it demonstrates the new feature of the spatial distribution of tidal ellipse. Both the wind dataset and current dataset are helpful to know that local wind control the variability of surface residual current. Besides, the integration of current data sea level and wind, shows that two factors control the outflow and inflow near the mouth of Jiaozhou Bay:Pressure gradient induced by different seal level in and out of the Bay; Nonlinear tidal stress which varies with the spring and neap。The divergence and convergence calculated from HF radar current reveals the area where upwelling takes place frequently, the sea temperature recorded by buoy show that the local divergence(convergence) influence the diurnal variability.
Secondly, Surface current data assimilation system over Qingdao Coastal sea is built basing on FVCOM, HF radar current and Ensemble Kalman Filter. If the feature of Qingdao near-shore current are taken into account, we improve the key part of our data assimilation system, including:
1, Based on Ensemble Kalman Filter(ENKF) method, we adopt different assimilation method (Traditional ENKF and quasi-ENKF),then compare the improvement of forecasting error for these two method and the improvement of the spatial structure of the forecasting current. If the difference about integration efficiency are also considered, quasi-ENKF is the best choice for this data assimilation system.
2,The assimilation effect are sensible to the background error estimate, therefore, we select three different methods to generate background error:Monte Carlo Method; Canadian Quick Covariance (CQC) Method and Data Uncertainty Engine(DUE) Method. The applicability of these three methods depend on their assimilation effect. Please notice that, the character of tidal current in study area are taken into consideration.
By series of data assimilation experiments, we test our improvement of the system. The standard for assimilation effect is the ability to decrease forecasting error. We finally determine the best assimilation method and best background error estimate method.i.e. quasi-ENKF and CQC method. Besides, the sensibility of ensemble size is also studied in our paper, and find that the it is better to restrict ensemble size below 50.
首先,对观测的表层海流进行可靠性检验,对比雷达观测海流和浮标观测海流,二者之间的误差约为10cm/s,且在统计意义上高度相关,说明雷达海流数据质量可靠,能够较好地刻画出青岛近海表层海流的特点。
进一步通过气象要素数据、验潮站水位数据和锚系ADCP海流数据分析青岛近海的表层海流特征,揭示潮汐,风,地形以及水位等因素对研究区域表层流的影响,特别是由观测数据揭示了青岛近海区域潮流椭圆类型的空间分布特征。结合气象观测数据与海流数据使我们认识了局地风对青岛近海表层日均余流的控制作用。综合利用海流数据,水位记录和风速数据,揭示了控制胶州湾湾口夏季出入流动量平衡的因子：由胶州湾内外水位差诱导的压强梯度力和随天文潮变化的非线性潮流应力。从雷达海流计算的表层辐合辐散,揭示了局地上升流的频发区域,浮标记录的温度数据也揭示了潮流引起的局地辐合辐散对温度日变化的重要影响。
其次,将FVCOM数值模型,雷达海流和集合卡曼滤波方法相结合,构建青岛近海表层海流数值同化系统。针对青岛近海区域的海流特点,我们对数值同化系统的关键部分进行了完善,主要包括：
1,以集合卡曼滤波方法为基础,采用不同的数值同化方案(传统集合卡曼滤波和准集合卡曼滤波),对比两种不同方案对数值预报结果误差的改善,以及对预报海流空间结构的改进,同时也考虑到两种不同方案在计算效率上的差异,确定准集合卡曼滤波方法为本数值同化系统的最佳方案。
2,鉴于数据同化效果对背景误差估计的敏感性,我们选取三种不同的背景误差生成方法：蒙特卡洛方法；快速生成方法；数据不确定性产生器方法。通过对比三种方法在本数值同化系统的同化效果,确定其适用性。在实施三种方法的过程中,充分考虑了数值模型的特点以及青岛近海的潮汐潮流特征。
通过一系列数值同化实验测试以上两部分的改善,以数值预报结果的均方根误差得到最大程度减少为标准,确立了适用于青岛近海的最优同化方案和背景误差估计方法,即以准集合卡曼滤波方法为方案,通过快速生成方法估计背景误差场。同时,我们还研究了数值同化系统对状态集合个数的敏感性,发现在本数值同化系统中,状态集合个数不超过50为宜。
This paper combines surface current measured by High Frequency Radar deployed in Qingdao Coastal sea, meteorologic variables recorded by buoy, sea level from tide gauge and current of mooring ADCP,try to analyze the summer surface current over Qingdao near-shore and Jiaozhou Bay area, then simulate the tidal dynamics in study area and assimilate these summer surface current into numerical model.
Before the data assimilation, these surface currents are validated. We also compare the HF radar current and Buoy current, find that their averaged difference is about 10cm/s, and they are highly statistically correlated, meaning that HF radar currents are credible, and able to capture the feature of the near shore current.
In order to analyze the characteristics of Qingdao near-shore current, data analysis with all available dataset are performed. The result of current analysis reveals the influence from tide, wind, topography and sea level on surface current, particularly, it demonstrates the new feature of the spatial distribution of tidal ellipse. Both the wind dataset and current dataset are helpful to know that local wind control the variability of surface residual current. Besides, the integration of current data sea level and wind, shows that two factors control the outflow and inflow near the mouth of Jiaozhou Bay:Pressure gradient induced by different seal level in and out of the Bay; Nonlinear tidal stress which varies with the spring and neap。The divergence and convergence calculated from HF radar current reveals the area where upwelling takes place frequently, the sea temperature recorded by buoy show that the local divergence(convergence) influence the diurnal variability.
Secondly, Surface current data assimilation system over Qingdao Coastal sea is built basing on FVCOM, HF radar current and Ensemble Kalman Filter. If the feature of Qingdao near-shore current are taken into account, we improve the key part of our data assimilation system, including:
1, Based on Ensemble Kalman Filter(ENKF) method, we adopt different assimilation method (Traditional ENKF and quasi-ENKF),then compare the improvement of forecasting error for these two method and the improvement of the spatial structure of the forecasting current. If the difference about integration efficiency are also considered, quasi-ENKF is the best choice for this data assimilation system.
2,The assimilation effect are sensible to the background error estimate, therefore, we select three different methods to generate background error:Monte Carlo Method; Canadian Quick Covariance (CQC) Method and Data Uncertainty Engine(DUE) Method. The applicability of these three methods depend on their assimilation effect. Please notice that, the character of tidal current in study area are taken into consideration.
By series of data assimilation experiments, we test our improvement of the system. The standard for assimilation effect is the ability to decrease forecasting error. We finally determine the best assimilation method and best background error estimate method.i.e. quasi-ENKF and CQC method. Besides, the sensibility of ensemble size is also studied in our paper, and find that the it is better to restrict ensemble size below 50.
引文
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