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。     

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.     

最优插值法在东海赤潮高发区赤潮生消过程数值模拟中的应用

将最优插值法与赤潮数值模型相结合,针对时空分辨率不统一的卫星遥感数据及海洋常规调查数据,进行了东海赤潮高发区海表温度场及海表营养盐浓度场的数据同化研究,并在此基础上进行了该海区春季赤潮过程的数值模拟研究。数据同化结果显示,最优插值数据同化方法不仅在一定程度上修正了海表温度场模拟结果的误差,尤其体现在对赤潮藻的生长影响较大的温度区段18—20℃等值线的南移,而且较为明显地优化了研究海域海表营养盐浓度场,使模拟结果更加接近观测值。对比赤潮过程数值模拟结果发现,在未加同化情况下,海区赤潮藻并未形成大规模赤潮,并且消散较快;而在加入同化后,研究海域交替出现了硅藻及甲藻的大范围赤潮,赤潮生消过程与现场调查数据分析结果较为一致。耦合了最优插值数据同化方法的赤潮数值模型,由于具有改进海洋环境背景场的优势,因此可以在赤潮预报预警工作中得到较好应用。     

卫星高度计资料在三维海温和盐度数值预报中的应用

随着卫星遥感观测技术的发展,越来越多的卫星观测资料被应用于数值模式的同化研究中.基于国家海洋环境预报中心西北太平洋三维湿盐流预报系统,利用法国CLS中心的沿轨高度计资料的海表面高度异常的融合数据,结合基于三维变分的OVALS(ocean variational analysis system)同化系统,在垂向将海面高度...     

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.     

用客观分析生成中国近海及邻近海域温度盐度格点数据集

利用LEVITUS方法对中国近海及邻近海域历史观测的温度和盐度资料进行客观分析,生成累年各月标准层1/2°×1/2°的格点数据集.文中选择给出一些深度标准层次上的平面等值线图.并用该方法分析船舶观测SST数据,将其分析结果应用于中国近海海表面温度短期数值预报的变分伴随资料同化试验中,表明资料的客观分析对海洋数值模拟和预报的重要性.     

数据同化在海洋数值产品制作及预报中的应用研究

讨论了海洋中数据同化的目的,意义,各种数据同化方法,国内外发展现状及其在海洋数值产品制作及预报中的应用。文中还介绍了数据同化方法中的客观分析法和伴随法的原理,结合海洋中的实际问题进行了数据同化试验,给出了相应的同化试验结果,并讨论了二阶伴随理论。     

基于时空四维多尺度分析方法的卫星遥感和现场观测联合同化研究

充分融合使用卫星遥感与现场观测信息，构建高质量的水下温盐场是海洋科学研究发展的前沿课题。目前，绝大多数同化系统使用的同化方案，均需要假设要素在海表与水下存在某种人为预先设定的关系，从而导致得到的温盐分析场人为性较强，不能完全客观地反映真实的海洋状态。本研究提出了一种不做任何关系假设，仅依靠不同种类的观测资料在时间和空间上的相互补充作用，融合卫星遥感与现场观测资料，进行时空四维多尺度分析的方案。通过与分别单独同化这两类观测资料的试验结果相比，该方案既可以得到较精准的温盐剖面结构特征，又能够反映出海面中尺度变化的细节信息，最大化地提取了观测资料中的多尺度信息，实现了“1+1> 2”的效果，构建了完全客观的温盐分析场。研究结果还表明，同化卫星遥感海表面盐度资料可以有效改善温度和盐度的分析，证明了海表面盐度观测在温盐同化中的重要性。     

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...     

海洋初始状态对预报2002/03年中太平洋型厄尔尼诺事件的作用

2002/03年厄尔尼诺事件,是暖海温中心出现在赤道中太平洋区域的一种新型厄尔尼诺,即中太平洋型厄尔尼诺。本文基于一个厄尔尼诺预测系统,利用三组回报试验来详细区分海洋表层和次表层初始状态对预报2002/03年中太平洋型厄尔尼诺事件的作用,并由此来探寻对预报厄尔尼诺演变过程最有利的初始条件。回报试验分为三组:(1)仅同化海表温度观测(sea surface temperature;简称SST)来优化海洋表层初始状态(Assim_SST);(2)仅同化海表高度观测(sea level;简称SL)来更新海洋次表层初始状态(Assim_SL);(3)同时同化SST和SL观测来一起更新海洋表层和次表层初始状态(Assim_SST+SL)。回报试验结果表明,三种不同的初始条件都可以使模式提前一年成功地预报2002/03年厄尔尼诺事件,并且"Assim_SST+SL"回报试验的效果最好。三组回报试验结果间的对比表明:海洋表层和次表层初始状态均对成功地预报该事件有重要作用,但其作用分别集中在事件发展的不同阶段。精确的海洋表层初始状态更容易激发模式预报出一次厄尔尼诺事件,而更合理的海洋次表层初始状态则能有效地提高厄尔尼诺事件预报的强度。     

Assimilation of surface currents into a regionalmodel over Qingdao coastal waters of China

Surface currents measured by high frequency （HF） radar arrays are assimilated into a regional ocean model over Qingdao coastal waters based on Kalman filter method. A series of numerical experiments are per- formed to evaluate the performance of the data assimilation schemes. In order to optimize the analysis pro- cedure in the traditional ensemble Kalman filter （ENKF）, a different analysis scheme called quasiensemble Kaman filter （QENKF） is proposed. The comparisons between the ENKF and the QENKF suggest that both them can improve the simulated error and the spatial structure. The estimations of the background error covariance （BEC） are also assessed by comparing three different methods： Monte Carlo method; Canadian quick covariance （CQC） method and data uncertainty engine （DUE） method. A significant reduction of the root-mean-square （RMS） errors between model results and the observations shows that the CQC method is able to better reproduce the error statistics for this coastal ocean model and the corresponding external forcing. In addition, the sensibility of the data assimilation system to the ensemble size is also analyzed by means of different scales of the ensemble size used in the experiments. It is found that given the balance of the computational cost and the forecasting accuracy, the ensemble size of 50 will be an appropriate choice in the Qingdao coastal waters.     

Inferred variables in data assimilation: quantifying sensitivity to inaccurate error statistics

The ability of data assimilation systems to infer unobserved variables has brought major benefits to atmospheric and oceanographic sciences. Information is transferred from observations to unobserved variables in two ways: through the temporal evolution of the predictive equations (either a forecast model or its adjoint) or through an error covariance matrix (or a parametrized approximation to the error covariance). Here, it is found that high frequency information tends to flow through the former route, low frequency through the latter. It is also noted that using the Kalman Filter analysis to estimate the correlation between the observed and unobserved variables can lead to a biased result because of an error correlation: this error correlation is absent when the Kalman Smoother is used.     

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.     

A numerical storm surge forecast model with Kalman filter

ImUcrIONThe deterministic storm stirge nurnrical fOrecast Tnedel has played an imPOrtant role inroutine storm surge real-time fOrecast. But somtimes the error of forecast is still large by usingdeterministic medels (Je1esnianshi et al., l992). The source of these errors mainly comesfrom (1 ) errors of wind stress and medel's open boundary, (2) non--optimized medel param-eter, (3) error of model equations, (4) error of medel's numrical methed, etc. The effec-ti ve methed to solve this probl…     

Coupling a two-way nested primitive equation model and a statistical SST predictor of the Ligurian Sea via data assimilation

A primitive equation model and a statistical predictor are coupled by data assimilation in order to combine the strength of both approaches. In this work, the system of two-way nested models centred in the Ligurian Sea and the satellite-based ocean forecasting (SOFT) system predicting the sea surface temperature (SST) are used. The data assimilation scheme is a simplified reduced order Kalman filter based on a constant error space. The assimilation of predicted SST improves the forecast of the hydrodynamic model compared to the forecast obtained by assimilating past SST observations used by the statistical predictor. This study shows that the SST of the SOFT predictor can be used to correct atmospheric heat fluxes. Traditionally this is done by relaxing the model SST towards the climatological SST. Therefore, the assimilation of SOFT SST and climatological SST are also compared.     

MASNUM-WAM海浪模式集合Kalman滤波同化研究——Ⅱ.集合样本对同化效果的影响

背景误差相关结构的确定是影响海浪同化效果的关键因素之一。集合Kalman滤波是一种较为成熟的同化方法,其可以对背景误差进行实时更新和动态估计,现已广泛应用于海洋和大气领域的研究。本文基于MASNUM-WAM海浪模式,分别采用静态样本集合Kalman滤波和EAKF方法,针对2014年全球海域开展海浪数据同化实验,同化资料为Jason-2卫星高度计数据,利用Saral卫星高度计资料对同化实验结果进行检验。结果表明,两组同化方案均有效提高了海浪模式的模拟水平,EAKF方案在风场变化较大的西风带区域表现显著优于静态样本集合Kalman滤波方案,但总体上两者相差不大。综合考虑计算成本和同化效果,静态样本集合Kalman滤波方案更适用于海浪业务化预报。     

Comparison of methods for argo drifters data assimilation into a hydrodynamical model of the ocean

Different data assimilation methods such as an extended Kalman filter, the optimal interpolation method, and a method based on the Fokker-Planck equation applications are considered. Data from the ARGO drifters are assimilated into the HYCOM shallow water model (University of Miami, USA). Throughout the study, the schemes and methods of parallel computations with an MPI library are used. The results of the computations with assimilations are compared between themselves and with independent observations. The method based on the Fokker-Planck equation and the extended Kalman filter are preferable because they give better results than the optimal interpolation scheme. The various model characteristics of the ocean, such as the heat content fields and others, are analyzed after the data assimilation.     

Kalman滤波风暴潮数值预报四维同化模式研究进展

本文首先介绍了Kalman滤波在风暴潮数值预报中的应用，特别介绍了近年来国际上发展的一些在实际中可行的次优化Kalman滤波算法。并通过一个稳态Kalman滤波风暴潮数值预报模式的实例表明，使用资料同化可以明显改进风暴潮后报结果；资料同化能够提供更为合理的预报初始场，对风暴潮的短期预报有较明显的改进。一旦没有资料同化到模式中去，预报结果很快接近确定性模式。     

影响黄渤海的温带气旋数值集合预报试验

为了改进温带气旋数值预报的精度,基于WRF(Weather Research and Forecasting)模式,利用GSI(Gridpoint Statistical Interpolation)-EnKF(Ensemble Kalman Filter)系统,设计了一套温带气旋集合预报方法,其具有的2种选择方案通过滤掉质量较差的集合成员从而将集合成员数目控制在10以内,达到了大幅降低集合预报计算量的目的。针对2020年7月一次影响黄海的温带气旋个例,开展了一系列决定性预报与集合预报的数值对比试验。分析结果如下:1)不采取任何择优方案的集合预报效果就已经明显优于决定性预报,而采取择优方案使得预报效果进一步得到提升;2)预报初始时刻择优(直接择优方案)的集合预报效果远不如短时积分3 h后才进行择优(积分择优方案)的预报效果; 3)积分择优方案优于直接择优方案的原因是,初始场集合体中的成员经过短时积分后其误差得以放大而使得择优更加准确。多个例的应用结果进一步表明,本文提出的积分择优方案温带气旋集合预报方法具有较好的业务预报应用前景。     

The representer method, the ensemble Kalman filter and the ensemble Kalman smoother: A comparison study using a nonlinear reduced gravity ocean model

This paper compares contending advanced data assimilation algorithms using the same dynamical model and measurements. Assimilation experiments use the ensemble Kalman filter (EnKF), the ensemble Kalman smoother (EnKS) and the representer method involving a nonlinear model and synthetic measurements of a mesoscale eddy. Twin model experiments provide the “truth” and assimilated state. The difference between truth and assimilation state is a mispositioning of an eddy in the initial state affected by a temporal shift. The systems are constructed to represent the dynamics, error covariances and data density as similarly as possible, though because of the differing assumptions in the system derivations subtle differences do occur. The results reflect some of these differences in the tangent linear assumption made in the representer adjoint and the temporal covariance of the EnKF, which does not correct initial condition errors. These differences are assessed through the accuracy of each method as a function of measurement density. Results indicate that these methods are comparably accurate for sufficiently dense measurement networks; and each is able to correct the position of a purposefully misplaced mesoscale eddy. As measurement density is decreased, the EnKS and the representer method retain accuracy longer than the EnKF. While the representer method is more accurate than the sequential methods within the time period covered by the observations (particularly during the first part of the assimilation time), the representer method is less accurate during later times and during the forecast time period for sparse networks as the tangent linear assumption becomes less accurate. Furthermore, the representer method proves to be significantly more costly (2–4 times) than the EnKS and EnKF even with only a few outer iterations of the iterated indirect representer method.