变分伴随数据同化在海表面温度预报中的应用研究

将变分伴随数据同化技术应用于海表面温度(SST)数值预报.采用中国近海海表面温度短期数值预报模式,将船舶测报海表面温度同化到该模型中,对SST初始场进行优化.文中给出了中国近海SST数值预报同化模型5d试报结果与观测值的比较,整个区域的均绝差由同化前的2.71℃降至0.87℃,即变分伴随数据同化对改进SST数值预报的效果是比较明显的,表明它可成为SST数值预报初始化的新方法.     

遥感资料同化对海表温度预报的影响

本文将AMSR-E卫星微波遥感海表温资料运用到渤黄东海海表面温度短期数值预报模式当中.数值预报模式利用伴随方法实现了预报模式的初值场优化.微波遥感海表温资料与海表面温度短期数值预报模式有机结合后的试验结果表明:将预报结果和船舶报资料进行比对时,将遥感资料引入到数据同化的结果要明显优于仅同化船舶报资料的结果,且均方差大部...     

集合同化方法在黄海海域海表面温度同化中的应用

The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.     

结合注意力机制的区域型海表面温度预报算法

海表面温度预报在海洋相关领域具有重要的实用价值,随着遥感信息采集技术的不断发展和完善,区域内海表面温度数据采集的完整性得到了保障。现今大多数方法在预报海表面温度时,只考虑了海表面温度的时间相关性,并未利用其空间相关性,使得预报精度受到限制。针对该问题,本文将区域内每天的海表面温度数据作为一个矩阵输入模型,便于时间和空间信息的提取,并提出了CA-ConvLSTM模型来预报海表面温度。该模型首先利用卷积层对海表面温度矩阵进行局部特征提取,然后通过注意力模型为矩阵序列分配权重,将权重与矩阵序列对应相乘得到加权特征序列,最后,利用ConvLSTM进行预报,获得未来一天或五天内的海表面温度。通过实验确定模型的结构、输入尺寸和k值,再将CA-ConvLSTM与SVR、LSTM和ConvLSTM进行对比。实验结果表明:CA-ConvLSTM的均方根误差(Root Mean Square Error,RMSE)和预报精度(Prediction Accuracy,PACC)指标均要优于其他三种预报方法,验证了本文方法的有效性。     

EnKF集合同化下黄海海雾数值确定性预报初始场构造方法的探究

在黄海海雾的数值模拟中,EnKF（ensemble Kalman filter）是一种优于3DVAR（three-dimensional variational）的数据同化方法。研究发现,对EnKF初始场集合体采取常用的集合平均所产生的确定性预报初始场,会出现初始场中海雾在预报开始后就迅速消失以及接下来海雾难以生成的异常现象。通过详细的海雾个例研究,清晰地揭示并解释了此现象,指出这是集合平均造成初始场中云水与温度湿度之间存在不协调关系所导致的后果,并提出了一种择优加权平均方法来取代常用的集合平均。研究结果表明,海雾确定性预报采用择优加权平均所构建的初始场,可以消除上述异常现象,显著改进海雾模拟效果。     

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.     

海洋流场的计算——数值预报

从海洋流场计算的原理出发，简要地分析了海洋流场数值预报方法，并以浅水流场为例介绍了流场数值模拟的过程，讨论了数值计算中的一些问题。     

基于D-S理论的卫星遥感海表面温度和同化数据融合方法

利用卫星遥感获取海表面温度实时性强,但其数据质量随大气环境而波动;利用同化技术求解的海表面温度局部精度高,但覆盖面与实时性差.鉴于两种技术适应不同情况,拟使用信息融合技术实现两者优势互补,即根据各自的优势和局限性对数据点进行一致性评价和加权融合计算,在提高精度的同时对结果的不确定性进行量化和评价.以我国东部海域海表温度为例的采样试验结果表明,融合法更适应复杂的环境.     

渤、黄、东海潮汐潮流的数值模拟

利用球坐标系中的二维非线性潮波方程组,数值计算了渤、黄、东海全海区的全日及半日潮汐潮流。沿岸81个潮位站的计算与实测值的比较表明,M₂分潮振幅差平均为7.2cm,相角差为6.4°,m₁分潮振幅差平均为2.6cm,相角差为7.4°,计算与实测符合良好。潮流的比较结果表明,计算与实测的符合程度也是比较好的。文中给出的同潮图同Fang(1986)给出的实测与数值的综合结果基本一致。本计算还证实或首次给出了若干圆流点。如对M₂分潮流,证实了在北黄海山东北部近海及南黄海北部各存在一对圆流点,并在浙江北部近海新发现一对圆流点;对m₁分潮流在苏北浅滩外侧发现一个圆流点,另外在东海东北部(济州岛东南)新给出两个圆流点,东海东南部的弱流区存在三个圆流点,此外,文中还分别讨论了M₂及m₁分潮能通量的传播和消耗情况,并指出从太平洋经吐噶喇海峡及冲绳至宫古岛之间的水道传入东海的m₁分潮,在遇到陆坡的阻挡后,其中有相当部分潮能被反射回太平洋。     

GPS掩星资料三维变分同化对台风模式预报的改进试验

本文尝试了GPSRO COSMIC资料在中尺度数值模式中的应用,利用COSMIC资料受云和降水影响较小,且有高数据精度、高垂直分辨率等优点,以改善模式初始场,进而提高预报准确度。模式采用中尺度气象模式WRF V3.0.1版本及其三维变分同化系统3DVAR,利用NCEP再分析资料、GTS资料和COSMIC资料对2009年第8号台风"莫拉克"登陆台湾岛前到登陆台湾岛的过程进行了模拟试验,并对温度、露点温度、对流有效位能等要素进行了诊断分析。试验结果表明:该项试验成功将COSMIC资料同化进模式,加深对"莫拉克"热力结构特征的了解,有效改善台风降水和路径预报,其中仅屏东县单点降水预报提高600 mm左右,24 h预报路径误差提高80 km以上。同时对提高台风强度预报起到积极作用。     

海洋水温垂直分布数据同化方法研究

以一维海洋水温模型为例,利用伴随法进行海洋观测数据同化试验,以便为水温的数值预报提供较准确的初始场.文中利用泛函的Gâteaux微分和Hilbert空间上伴随算子的概念讨论了连续的伴随模型的建立,并通过选择适当的差分格式离散伴随模型,使其保持连续时的伴随关系,同时给出了水温初始场最优化过程及相应的同化试验数值结果.     

The long-term variability of sea surface temperature in the seas east of China in the past 40 a

The global surface temperature change since the mid-19th century has caused general concern and intensive study. However, long-term changes in the marginal seas, including the seas east of China, are not well understood because long-term observations are sparse and, even when they exist, they are over limited areas. Preliminary results on the long-term variability of sea surface temperature (SST) in summer and winter in the seas east of China during the period of 1957-2001 are reported using the Ocean Science Database of Institute of Oceanology, Chinese Academy of Sciences, the coastal hydrological station in situ and satellite data. The results show well-defined warming trends in the study area. However warming and cooling trends vary from decade to decade, with steady and rapid warming trends after the 1980s and complicated spatial patterns. The distribution of SST variation is intricate and more blurred in the areas far away from the Kuroshio system. Both historical and satellite data sets show significant warming trends after 1985. The warming trends are larger and spread to wider areas in winter than in summer, which means decrease in the seasonal cycle of SST probably linked with recently observed increase of the tropical zooplankton species in the region. Spatial structures of the SST trends are roughly consistent with the circulation pattern especially in winter when the meridional SST gradients are larger, suggesting that a horizontal advection may play an important role in the long-term SST variability in winter.     

Trends of sea surface temperature and sea surface temperature fronts in the South China Sea during 2003–2017

The trends of the sea surface temperature(SST) and SST fronts in the South China Sea(SCS) are analyzed during2003–2017 using high-resolution satellite data. The linear trend of the basin averaged SST is 0.31°C per decade,with the strongest warming identified in southeastern Vietnam. Although the rate of warming is comparable in summer and winter for the entire basin, the corresponding spatial patterns of the linear trend are substantially different between them. The SST trend to the west of the Luzon Strait is characterized by rapid warming in summer, exceeding approximately 0.6°C per decade, but the trend is insignificant in winter. The strongest warming trend occurs in the southeast of Vietnam in winter, with much less pronounced warming in summer. A positive trend of SST fronts is identified for the coast of China and is associated with increasing wind stress. The increasing trend of SST fronts is also found in the east of Vietnam. Large-scale circulation, such as El Ni?o, can influence the trends of the SST and SST fronts. A significant correlation is found between the SST anomaly and Ni?o3.4 index, and the ENSO signal leads by eight months. The basin averaged SST linear trends increase after the El Ni?o event(2009–2010), which is, at least, due to the rapid warming rate causing by the enhanced northeasterly wind. Peaks of positive anomalous SST and negatively anomalous SST fronts are found to co-occur with the strong El Ni?o events.     

Application of deep learning technique to the sea surface height prediction in the South China Sea

A deep-learning-based method, called ConvLSTMP3, is developed to predict the sea surface heights(SSHs).ConvLSTMP3 is data-driven by treating the SSH prediction problem as the one of extracting the spatial-temporal features of SSHs, in which the spatial features are "learned" by convolutional operations while the temporal features are tracked by long short term memory(LSTM). Trained by a reanalysis dataset of the South China Sea(SCS), ConvLSTMP3 is applied to the SSH prediction in a region of the SCS east off Vietnam coast featured with eddied and offshore currents in summer. Experimental results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4% averaged over a 15-d prediction period. In particular,ConvLSTMP3 shows a better performance in predicting the temporal evolution of mesoscale eddies in the region than a full-dynamics ocean model. Given the much less computation in the prediction required by ConvLSTMP3,our study suggests that the deep learning technique is very useful and effective in the SSH prediction, and could be an alternative way in the operational prediction for ocean environments in the future.     

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。     

渤黄东海40年温盐流模拟与初步分析

使用ROMS(regional oceanic modeling system)模式模拟了40年的渤黄东海温盐流,数据包括三维的温度、盐度、流速、流向和海表高度,同时包含了逐小时的潮汐信息。将模拟结果与观测资料和卫星反演数据进行对比,检验了模式准确性。整体上,模式模拟的水位与近岸观测值基本一致,能够准确再现风产生的增水;模式较为准确的再现了渤黄东海的温度分布,在深水区模拟的温盐剖面与观测值基本一致;模式模拟渤黄东海区域的海表高度和海表流与卫星反演结果相比偏小,但分布趋势相近。模式结果可以为研究气候变化对水位的影响和黄海暖舌的扩散过程等现象提供数据支持。     

渤海、黄海、东海AVHRR海表温度场的季节变化特征

海表温度场表征了海洋热力、动力过程和海洋与大气相互作用的综合结果.它不仅是研究海面水汽和热量交换的一个重要物理参数,也为海洋环流、水团、海洋锋、上升流和海水混合等海洋学课题的研究提供一种直观的指示量.20世纪60年代以来,我国海洋工作者在历次海上观测和台站资料的基础上,对渤海、黄海、东海表层温度的空间分布和变化进行了较为详细的分析研究[1～4],并绘制了系列的水温气候图集.这些研究成果对认识黄海、东海海域的平均海表温度场的分布、变化以及相关物理海洋现象的研究起到了重要的作用.     

渤、黄、东海8个主要分潮的数值模拟研究

应用MIKE数值模拟软件,采用无结构三角形网格,建立一套计算区域包括整个渤海、黄海、东海以及东海大陆架和琉球群岛的高分辨率数值模型,考虑了实际水深和岸线,外海开边界采用西北太平洋大模型结果的潮位提供,模拟了东中国海潮波的波动过程,对潮波垂直运动过程进行调和分析,得到了渤海、黄海、东海的M2,S2,K1,O1以及N2,K2,P1,Q1八个主要分潮的传播和分布特征。利用中国沿海14个潮位站的调和常数对模型结果进行了验证,验证结果显示模型较为准确可靠。研究结果表明:4个主要半日潮(全日潮)在渤、黄、东海的传播情形基本相似,即潮波在渤海、黄海、东海沿岸的传播性质上类似沿岸开尔文波的传播形态,并且成功再现了计算海域的4个半日分潮无潮点和2个全日分潮无潮点。全日潮振幅各无潮点附近振幅最小,而海湾的波腹区振幅最大,东海潮差呈现近岸方向振幅大、离岸方向振幅小,浙闽沿海振幅也较大,黄海振幅相对较小,渤海振幅在辽东湾和渤海湾顶最大,两个无潮点周边振幅较小。