基于ROMS模式的南海SST与SSH四维变分同化研究

卫星遥感观测获得了大量高分辨率的海面实时信息,包括海面温度（SST）和海面高度（SSH）等,同化进入数值模式可有效提升模拟精度。本文基于ROMS模式与四维变分同化方法（4DVAR）,使用AVHRR SST和AVISO SSH数据,开展了南海区域同化实验。为检验同化的效果,分别利用HYCOM再分析资料和Argo温盐实测数据分析了同化结果的海面高度、流场及温盐剖面的精度。对比结果表明,SST和SSH的同化能够改善ROMS的模拟结果:同化后海面高度场能够更为准确地捕捉海洋的中尺度特征,与HYCOM海面高度再分析资料相比,平均绝对偏差和均方根误差分别为0.054 m和0.066 m;与HYCOM 10 m层流场相比,东向与北向流速平均绝对偏差分别为0.12 m/s和0.11 m/s,相比未同化均提升约0.01 m/s;温盐同化结果与Argo温盐实测具有较高的一致性,温度和盐度平均绝对偏差为0.45℃、0.077,均方根误差为0.91℃、0.11,单个的温盐廓线对比说明,同化结果与HYCOM再分析资料精度相当。     

基于适应性观测误差和温盐偏差控制的EnKF温盐廓线资料同化试验

通过开展2008年夏季南海北部开放航次CTD的温盐廓线数据资料同化试验,本文采取了观测误差适应的方法来防止EnKF滤波发散问题;同时,从背景误差协方差和温盐模式偏差关系入手,在同化中引入温盐控制来减小模式偏差对同化结果的影响。对于改进的同化方案进行了试验验证,并用卫星高度计观测数据,OSCAR流速数据,走航ADCP数据作为独立观测数据检验。结果证明新的EnKF同化策略能够有效地减小温盐均方根误差。同时整个同化系统能有效地改善高度场和流场的模拟。     

OSTIA数据在中国近海业务化环流模型中的同化应用

The prediction of sea surface temperature(SST) is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea(BYECS). One is based on a surface net heat flux correction, named as Qcorrection(QC), which nudges the flux correction to the model equation; the other is ensemble optimal interpolation(En OI), which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis(OSTIA) system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error(RMSE) of the four experiments is 1.74, 1.16, 1.30 and 0.91°C, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively.Although both two methods are effective in assimilating the SST, the En OI shows more advantages than the QC,and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions.     

利用卫星遥感海面温度、高度联合反演南海北部三维温度场

利用历史观测得到的温度剖面数据,通过严格筛选和插值,建立了南海北部的气候态垂向温度剖面。随后,利用回归统计分析的方法构建了海面温度异常(SSTA)、海面高度异常(SSHA)联合扩展温度剖面的经验回归模型,并采用卫星遥感得到的SST和SSH数据扩展了南海北部的三维海洋温度场,其时间分辨率为天,空间分辨率为0.25°×0.25°。通过与观测数据的对比研究,扩展得到的温度场可以较为准确地反映南海北部温度剖面的结构特征,并且能有效地体现出一些中尺度变化过程。结果表明,本研究反演得到的三维温度扩展场是较为可靠的,它可以作为海洋数值模型的初始场,实现现场观测数据和卫星遥感数据的互补,有助于更好地分析南海北部温度场的三维结构及变化特征。     

Sequential forecasting of the surface and subsurface conditions in the Japan Sea

This study estimates a realistic change of the Japan Sea by assimilating satellite measurements into an eddy-resolving circulation model. Suboptimal but feasible assimilation schemes of approximate filtering and nudging play essential roles in the system. The sequential update of error covariance significantly outperforms the asymptotic covariance in the sequential assimilation due to the irregular sampling patterns from multiple altimeter satellites. The best estimates show an average rms difference of only 1.2°C from the radiometer data, and also explain about half of the sea level variance measured by the altimeter observation. The subsurface conditions associated with the mesoscale variabilities are also improved, especially in the Tsushima Warm Current region. It is demonstrated that the forecast limit strongly depends on variable, depth, and location.     

南海中尺度涡强度的季节和年际变化分析

为了研究南海中尺度涡强度的季节和年际变化规律,利用Matlab提取50 a(1958~2007年)简单海洋资料同化(Simple Ocean Data Assimilation,SODA)月平均数据集中流场和海表面高度场数据,应用一个涡旋自动探测算法对南海中尺度涡初始生成位置进行分析,并分析了海表面高度异常均方根值的季节变化和年际变化。结果表明:50 a里南海中尺度涡主要分布在吕宋岛西北海域、吕宋岛西南海域和越南以东广大海域,秋、冬季中尺度涡能量较高,春季中尺度涡最弱,中尺度涡强度高值区年际变化明显。从季节变化上看,海面高度异常均方根春、夏季最小,秋冬季最大;从年际变化上看,与同时期Nino3指数有显著负相关,周期大约为3 a。     

南海北部海面高度的季节内变异及其传播特征 *

利用25年(1993—2017)的卫星高度计资料, 采用复经验正交函数(complex empirical orthogonal function, CEOF)方法, 分析南海北部海区海面高度季节内变异的时空分布及传播特征。标准差分析表明, 南海北部海面高度的季节内变异(intra-seasonal variability of sea level anomalies, SLA-ISV)在沿陆坡外侧区较强, 且SLA-ISV表现出明显的季节性变化, 冬半年强于夏半年。CEOF前两个主要模态能较好地揭示研究海区SLA-ISV的时空分布及其传播特征, 并表明SLA-ISV的强度受到季节性变化和年际变化的调制。全年CEOF的第一模态揭示SLA-ISV从台湾岛西南至西沙群岛以东区域的冬半年西南向传播特征; 而全年CEOF的第二模态则表现了SLA-ISV分别在台湾岛西南和东沙群岛西南的西南向传播特征。南海北部中尺度涡季节变化统计分析表明, CEOF的分解结果与南海北部的涡旋活动一致。