国家气候中心全球海洋资料四维同化系统在热带太平洋的结果初步分析

介绍了国家气候中心(NCC)“全球海洋资料四维同化系统”(简称NCC-GODAS).该系统包含观测资料预处理系统、插值分析系统和所应用的动力模式.插值分析系统采用四维同化技术方案,在时间上设置一个4周的窗口,将此窗口之内的观测资料以一定的权重插入插值分析系统,在空间上采用三维变分方案.海洋动力模式为“九五”期间LASG研制的L30T63OGCM 1 0版本.文中考察了该系统从1982年到2003年3月在热带太平洋的部分同化分析结果,并与NCEP的再分析资料和EMC/NCEP的太平洋区域海洋资料同化系统的结果进行了对比分析.结果显示,该系统的同化结果(如SST,SSTA,Nino指数,次表层海温变化等)与NCEP的同期同化结果具有很好的一致性.同时,该系统得出的海洋同化资料气候场与海洋模式气候场相比,有显著的改进.表明该系统具有较好的同化能力,其同化结果可为海-气耦合模式进行季节和跨季节的气候预测业务提供可信的海洋初始场资料,同时还可以为相关研究提供海洋分析场.     

集合资料同化方法的理论框架及其在海洋资料同化的研究展望

在海洋动力系统的数值模拟中,海洋资料同化是一种能够有效融合多源海洋观测资料和数值模式的方法。它不仅可以显著地提高数值模拟的效果,构造海洋再分析资料场,还能有效减少海洋和气候预报时模式初始条件的不确定性。因此,海洋资料同化对于海洋研究和业务化应用具有非常重要的意义。资料同化方法的研究一直是大气、海洋科学的热门课题之一。其中,集合卡尔曼滤波器(EnKF)是一种有效的资料同化方法,自提出以来经过了20多年的发展和改进,已经在海洋资料同化中得到了广泛的研究和应用。近年来,随着动力模式的不断发展和计算能力的提高,粒子滤波器由于不受模型线性和误差高斯分布假设的约束,也逐渐成为了当前资料同化方法研究的热点。本文分析和总结了目前关于集合卡尔曼滤波器和粒子滤波器的一些最新理论研究结果,在贝叶斯滤波理论的框架下讨论了这两类算法的关联和区别,以及各自在资料同化实践中的优势和不足。在此基础上,我们探讨了粒子滤波器应用于海洋模式资料同化的主要困难和目前可行的一些解决方法,展望了集合资料同化方法研究的新趋势,为集合资料同化方法的进一步发展和应用提供理论基础。     

资料同化技术的发展及其在海洋科学中的应用

同顾了资料同化技术,特别是基于最优控制和统计估计这两大理论基础发展起来的几种资料同化方法的研究进展,以及这些方法在海洋科学研究中的应用现状.可以看到,由于海洋观测资料(如地转海洋学实时观测阵(Array for Real-time Geostrophic Oceanography,Argo)、热带大气海洋阵列(Trop...     

基于Cressman客观分析的南海北部海区数据同化实验

利用POM模式和空间插值法构造的同化方案对南海北部的航次观测资料进行了数据同化模拟.结果表明,数据同化有效兼顾了模式模拟和航次观测,在一定程度上纠正了模式模拟值相对于真实数据的误差;同化结果与模拟结果相比更接近观测值,在航次断面及其附近海域同化结果误差较小;另一方面,由于航次资料站点相对较少,在无观测数据的海域模式同化不是很理想,有待改进.此方法可为南海大型航次及其他资料同化提供一个再分析资料的实验性参考.     

集合最优平滑同化方法的研究与应用

海洋状态场的历史变化过程对其分布状态有重要影响。在观测资料稀疏的情况下,合理利用历史观测资料能够为海洋数据同化提供大量有效信息。然而在目前的顺序资料同化过程中,往往只同化当前时刻的观测数据,没有考虑到历史观测资料对当前状态的约束。四维变分虽然可以体现变量在时间维度的演变过程,但引入伴随方程会增加计算代价。本文基于集合最优平滑同化算法（Ensemble Optimal Smoothing,EnOS）探讨了一种在数据同化中加入历史观测资料的简易可行方案,其能够根据历史观测数据估计当前状态,并进行单点同化实验和区域同化实验来验证该方案的有效性。实验结果表明,将历史观测资料引入到同化过程中可以把控时间演变趋势,减小分析数据与真实值之间的偏差,更有效地消除数值模式误差,提高同化质量。     

海洋资料同化对气候季节-年际预测技巧及初始场的影响试验

集合卡尔曼滤波(Ensemble Kalman filter, EnKF)是一种国内外广泛使用的海洋资料同化方案, 用集合成员的状态集合表征模式的背景误差协方差, 结合观测误差协方差, 计算卡尔曼增益矩阵, 有效地将观测信息添加到模式初始场中。由于季节、年际预测很大程度上受到初始场的影响, 因此资料同化可以提高模式的预测性能。本文在NUIST-CFS1.0预测系统逐日SST nudging的初始化方案上, 利用EnKF在每个月末将全场(full field)海表温度(sea surface temperature, SST)、温盐廓线(in-situ temperature and salinity profiles, T-S profiles)以及卫星观测海平面高度异常(sea level anomalies, SLA)观测资料同化到模式初始场中, 对比分析了无海洋资料同化以及加入同化后初始场的区别、加入海洋资料同化后模式提前1~24个月预测性能的差异以及对于厄尔尼诺-南方涛动(El Niño-southern oscillation, ENSO)预测技巧的影响。结果表明, 加入海洋资料同化能有效地改进初始场, 并且呈现随深度增加初始场改进越显著的特征。加入同化后, 对全球SST、次表层海水温度的平均预测技巧均有一定的提高, 也表现出随深度增加预测技巧改进越明显的特征。但加入海洋资料同化后, 模式对ENSO的预测技巧有所下降, 可能是由于模式误差的存在, 使得同化后的预测初始场从接近观测的状态又逐渐恢复到与模式动力相匹配的状态, 加剧了赤道太平洋冷舌偏西、中东部偏暖的气候平均态漂移。     

一个稳态Kalman滤波风暴潮数值预报模式

利用Kalman滤波资料同化技术将海洋站水位观测资料融入二维线性风暴潮模式中,研制具有资料同化能力的风暴潮预报模式,改进风暴潮模式计算结果.通过在风暴潮模式的动量方程中加入模式噪声项来修正模式本身和气象强迫力的不确定性.确定性模式的输出通过带有观测噪声的观测方程与可利用的海洋站的潮位观测资料联系起来.假定初始的模式噪声和观测噪声满足均值为0的高斯分布,用迭代法得到计算区域的状态向量的稳态Kalman滤波,进而得到风暴潮模式输出的最优线性校正结果.利用这种资料同化技术,对1956年发生在东海的一次强风暴潮过程进行了后报试验,结果表明,该同化方法对短期风暴潮水位后(预)报有一定的改进.     

ARGO观测资料的2种同化方案

使用POM(Princeton Ocean Model)海洋环流模式模拟西北太平洋海域的温盐场,之后运行中国科学院大气物理研究所同化系统(Ocean Variational Analysis System,OVALS),使用Argo观测网格化产品进行2006年整个计算域三维变分同化。无论从平面分布还是断面分布来看,除海表面温度(SST)外,温度同化的效果都比较好,尤其是800 m水深以浅,均方差值甚至能减小1.0℃以上。而且随着同化积分时间的增加,同化后误差减小越来越显著,3、4月份的同化效果明显高于1、2月份。与OFES(OGCM for the Earth Simulator)海洋模式结果相比,同化在一定程度上改进了模拟结果。在POM(Princeton Ocean Model)数值模拟基础上,利用3个西太平洋代表性浮标的观测资料,运用松弛逼近法同化2006年6月断面数据。当松弛系数取0.5时,模拟时间大约半天,模拟值就能快速逼近或完全等于观测值。虽然只对观测剖面进行了数据同化,但该同化可以产生更大范围的效果,而且这种效果可以持续更长时间。     

基于拉格朗日拟序结构的黑潮区域海流运输结构提取和分析

海流的拉格朗日运动对于研究物质输送有着重要意义，拉格朗日拟序结构(LCSs)作为研究海流结构的新型方法，相比于传统欧拉方法更为客观。本文提出了一种新的计算LCSs束的方法，基于25年的平均速度场，利用变分方法计算得到黑潮区域的气候态LCSs，并通过简化合并的方法得到了气候态LCSs束，该LCSs束能够突出地显示出海流特性和运输模式，其代表的平均拉格朗日环流有很强的约束作用，且具有鲁棒性。最终我们获得了气候态下12个月份的流场结构图，揭示了月周期性拉格朗日环流规律。本文还利用虚拟粒子输运、多年浮标轨迹以及气候态温盐异常3种方法进行了验证，与拉格朗日运输模式相吻合，证明了海流拉格朗日拟序结构的准确性和可靠性。     

1986～1989年间热带太平洋环流数值模拟与观测资料间的比较研究──Ⅰ.海面起伏

本工作较详细地分析、比较了１９８６～１９８９年间热带太平洋环流数值模拟结果与相应的观测资料。作为第一部分，本文给出由中国科学院大气物理研究所自由表面海洋环流模式模拟得到的热带太平洋海面起伏与热带太平洋１８个海岛站水位计和美国地球卫星（Ｇｅｏｓａｔ）观测资料间的比较。结果表明，模式成功地模拟出与１９８６－１９８７ＥｌＮｉ　ｏ而和１９８８ＬａＮｉ　ａ事件相联系的热带太平洋水位场时空演变。如１９８６年下半年暖水在日界线附近堆累和水位上升；１９８６年底这一高水位区以Ｋｅｌｖｉｎ波形式迅速向东太平洋和南美沿岸地区传播，且该地区水位场演变呈双峰结构。模式海面起伏与１８个海岛站间平均相关系数高达０．７９，与其平均误差为４．５ｃｍ；与Ｇｅｏｓａｔ观测相比，模式海面起伏相关系数的区域平均值为０．３６；文中还讨论了对模式的进一步改进工作和海面起伏观测资料在自由表面海洋环流模式中的同化应用等问题。     

Assimilation of simulated float data in Lagrangian coordinates

We implement an approach for the accurate assimilation of Lagrangian data into regional general ocean circulation models. The forward model is expressed in Lagrangian coordinates and simulated float data are incorporated into the model via four-dimensional variational data assimilation. We show that forward solutions computed in Lagrangian coordinates are reliable for time periods of up to 100 days with phase speeds of 1 m/s and deformation radius of 35 km. The position and depth of simulated floats are assimilated into the viscous, Lagrangian shallow water equations. The weights for the errors in the model and data are varied and the assimilation results react appropriately. We show the effect of different spatial and temporal samplings of float data on all Lagrangian trajectories in the computational domain. At the end of the assimilation period, results from the Lagrangian shallow water equations could be interpolated and used as initial and boundary conditions in an Eulerian general ocean circulation model.     

海洋数据同化与数据融合技术应用综述

简述了不同数据同化和数据融合方法在海洋环境监测与预测方面的应用、国内外相关业务单位的海洋分析和预报系统的现状,以及海洋数据同化将来的业务化应用的发展趋势。四维变分和集合卡尔曼滤波正在成为国际上海洋环境分析与预报的主要应用方向,海-气耦合数据同化以及海冰数据同化是目前数据同化方法研究的热点。     

The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model

An ensemble optimal interpolation (EnOI) data assimilation method is applied in the BCC_CSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework. Pseudo-observations of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), temperature and salinity (T/S) profiles were first generated in a free model run. Then, a series of sensitivity tests initialized with predefined bias were conducted for a one-year period; this involved a free run (CTR) and seven assimilation runs. These tests allowed us to check the analysis field accuracy against the “truth”. As expected, data assimilation improved all investigated quantities; the joint assimilation of all variables gave more improved results than assimilating them separately. One-year predictions initialized from the seven runs and CTR were then conducted and compared. The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles, but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies. The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles, while surface data assimilation became more important at higher latitudes, particularly near the western boundary currents. The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables. Finally, a central Pacific El Ni?o was well predicted from the joint assimilation of surface data, indicating the importance of joint assimilation of SST, SSH, and SSS for ENSO predictions.     

利用热带太平洋TAO浮标阵列评估全球高分辨率浪-潮-流耦合模式同化结果

In order to evaluate the assimilation results from a global high resolution ocean model, the buoy observations from tropical atmosphere ocean(TAO) during August 2014 to July 2015 are employed. The horizontal resolution of wave-tide-circulation coupled ocean model developed by The First Institute of Oceanography(FIOCOM model) is 0.1°×0.1°, and ensemble adjustment Kalman filter is used to assimilate the sea surface temperature(SST), sea level anomaly(SLA) and Argo temperature/salinity profiles. The simulation results with and without data assimilation are examined. First, the overall statistic errors of model results are analyzed. The scatter diagrams of model simulations versus observations and corresponding error probability density distribution show that the errors of all the observed variables, including the temperature, isotherm depth of 20°C(D20), salinity and two horizontal component of velocity are reduced to some extent with a maximum improvement of 54% after assimilation. Second, time-averaged variables are used to investigate the horizontal and vertical structures of the model results. Owing to the data assimilation, the biases of the time-averaged distribution are reduced more than70% for the temperature and D20 especially in the eastern Pacific. The obvious improvement of D20 which represents the upper mixed layer depth indicates that the structure of the temperature after the data assimilation becomes more close to the reality and the vertical structure of the upper ocean becomes more reasonable. At last,the physical processes of time series are compared with observations. The time evolution processes of all variables after the data assimilation are more consistent with the observations. The temperature bias and RMSE of D20 are reduced by 76% and 56% respectively with the data assimilation. More events during this period are also reproduced after the data assimilation. Under the condition of strong 2014/2016 El Ni?o, the Equatorial Undercurrent(EUC) from the TAO is gradually increased during August to November in 2014, and followed by a decreasing process. Since the improvement of the structure in the upper ocean, these events of the EUC can be clearly found in the assimilation results. In conclusion, the data assimilation in this global high resolution model has successfully reduced the model biases and improved the structures of the upper ocean, and the physical processes in reality can be well produced.     

The impact of mean dynamic topography on a sea-level anomaly assimilation in the South China Sea based on an eddy-resolving model

The sea-level anomaly (SLA) from a satellite altimeter has a high accuracy and can be used to improve ocean state estimation by assimilation techniques. However, the lack of an accurate mean dynamic topography (MDT) is still a bothersome issue in an ocean data assimilation. The previous studies showed that the errors in MDT have significant impacts on assimilation results, especially on the time-mean components of ocean states and on the time variant parts of states via nonlinear ocean dynamics. The temporal-spatial differences of three MDTs and their impacts on the SLA analysis are focused on in the South China Sea (SCS). The theoretical analysis shows that even for linear models, the errors in MDT have impacts on the SLA analysis using a sequential data assimilation scheme. Assimilation experiments, based on EnOI scheme and HYCOM, with three MDTs from July 2003 to June 2004 also show that the SLA assimilation is very sensitive to the choice of different MDTs in the SCS with obvious differences between the experimental results and observations in the centre of the SCS and in the vicinity of the Philippine Islands. A new MDT for assimilation of SLA data in the SCS was proposed. The results from the assimilation experiment with this new MDT show a marked reduction (increase) in the RMSEs (correlation coefficient) between the experimental and observed SLA. Furthermore, the subsurface temperature field is also improved with this new MDT in the SCS.     

State Estimation of the North Pacific Ocean by a Four-Dimensional Variational Data Assimilation Experiment

A four-dimensional variational data assimilation system has been applied to an experiment to describe the dynamic state of the North Pacific Ocean. A synthesis of available observational records and a sophisticated ocean general circulation model produces a dynamically consistent dataset, which, in contrast to the nudging approach, provides realistic features of the seasonally-varying ocean circulation with no artificial sources/sinks for temperature and salinity fields. This new dataset enables us to estimate heat and water mass transports in addition to the qualification of water mass formation and movement processes. A sensitivity experiment on our assimilation system reveals that the origin of the North Pacific Intermediate Water can be traced back to the Sea of Okhotsk and the Bering Sea in the subarctic region and to the subtropical Kuroshio region further south. These results demonstrate that our data assimilation system is a very powerful tool for the identification and characterization of ocean variabilities and for our understanding of the dynamic state of ocean circulation. This revised version was published online in July 2006 with corrections to the Cover Date.     

气候模式中海洋数据同化对热带降水偏差的影响

本文采用海洋卫星观测海表温度(SST)和海面高度异常(SLA)数据,对国家海洋局第一海洋研究所地球系统模式FIO-ESM(First Institute of Oceanography Earth System Model version 1.0)中海洋模式分量进行了集合调整卡尔曼滤波(EAKF)同化,对比分析了大气环流、湿度和云量对海洋数据同化的响应,探讨了海洋同化对热带降水模拟偏差的影响。结果表明:海洋数据同化能有效改善海表温度和上层海洋热含量的模拟,30°S~30°N纬度带内年平均SST的绝均差降低60%。同化后大气模式模拟的赤道两侧信风得到明显改善,上升气流在赤道以北热带地区增强而在赤道以南热带地区减弱,热带降水模拟的动力结构更为合理,水汽和云量分布也更切合实际。热带年平均降水的空间分布和强度在同化后均得到改善,赤道以南的纬向年平均降水峰值显著降低,降水偏差明显减小,同化后30°S~30°N纬度带内年平均降水绝均差降低35%。