MASNUM-WAM海浪模式集合Kalman滤波同化研究-I.风场扰动对海浪模拟影响

模式集合样本的代表性和观测信息的可靠性是制约数据同化效果的重要因素,而前者对海浪模式同化的影响尤为显著。由于海浪模式对初始场的敏感性较弱,来自大气的风输入源函数是海浪的重要能量输入,如何合理地对风输入进行扰动,构造海浪的集合模式运行,是实现和改进海浪模式集合Kalman滤波同化的关键问题。为了实现海浪模式集合运行,本文提出了风场的三种集合扰动方案,分别为：纯随机数、随机场和时间滞后的风场扰动方法。本研究利用2014年1月ECMWF全球风场,基于这三种风场扰动方法开展了集合海浪模式的集合运行实验,并统计分析了海浪特征要素（有效波高）和二维波数谱对风场扰动的响应。结果表明,随机场集合扰动方案所构造的风场集合效果最佳,所得海浪模拟结果的集合样本发散度适中,能够较为合理地反映背景误差的统计特征,可用于进一步的集合Kalman滤波海浪数据同化实验。     

MASNUM-WAM海浪模式集合Kalman滤波同化研究-Ⅰ.风场扰动对海浪模拟影响

模式集合样本的代表性和观测信息的可靠性是制约数据同化效果的重要因素,而前者对海浪模式同化的影响尤为显著。由于海浪模式对初始场的敏感性较弱,来自大气的风输入源函数是海浪的重要能量输入,如何合理地对风输入进行扰动,构造海浪的集合模式运行,是实现和改进海浪模式集合Kalman滤波同化的关键问题。为了实现海浪模式集合运行,本文提出了风场的三种集合扰动方案,分别为:纯随机数、随机场和时间滞后的风场扰动方法。本研究利用2014年1月ECMWF全球风场,基于这三种风场扰动方法开展了集合海浪模式的集合运行实验,并统计分析了海浪特征要素(有效波高)和二维波数谱对风场扰动的响应。结果表明,随机场集合扰动方案所构造的风场集合效果最佳,所得海浪模拟结果的集合样本发散度适中,能够较为合理地反映背景误差的统计特征,可用于进一步的集合Kalman滤波海浪数据同化实验。     

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。     

集合最优插值方法在北印度洋海浪同化中的应用

基于第三代海浪模式WaveWatch III,采用集合最优插值(EnOI)方法对北印度洋海浪进行同化数值实验研究。在集合样本选取方案上,针对不同的实验分别选取有效波高(SWH)的历史后报场(样本A)、24h变化(样本B)以及以同一时刻72h预报时效和24h预报时效的差异(样本C)用于估计背景误差协方差。样本A和样本B是为海浪模拟而设计,样本C是为海浪预报而设计;通过与由高度计数据确定的模式背景误差进行比较,认为样本B优于样本A。采用样本B对2011年北印度洋海浪场进行同化模拟,结果表明2011-03-11相对误差改进都在5%及以上,其中7月份改进效果最佳。采用样本C对2013-07的有效波高进行0~72h预报,发现同化使0~24h预报改进最明显:均方根误差改进0.12m,相对误差改进5%。浮标检验结果支持上述结论。     

Envisat ASAR海浪谱资料的最优插值同化试验

基于第三代海浪数值模式LAGFD-WAM,分别利用4种不同形式的各向同性背景误差相关函数进行了Envisat高级合成孔径雷达(ASAR)海浪谱资料的最优插值同化试验.与4个浮标实测数据的比较验证表明,ASAR海浪谱资料的最优插值能够有效地改进海浪模式有效波高的模拟.4种不同形式的各向同性背景误差相关函数的同化结果相差不大,决定同化效果好坏的关键是对相关距离尺度的选取.针对自回归形式的背景误差相关函数的试验结果表明,相关距离尺度的量级在400-500km时同化效果最好,此时同化后的模式有效波高均方根误差比未同化时相对减小了26%.     

北印度洋海浪背景误差分析和同化预报初步评估

基于背景误差分析中的观测法,利用Jason-1卫星高度计沿轨有效波高数据并结合Wave Watch Ⅲ海浪模式预报结果,进行北印度洋海域海浪背景误差分析,得到海浪场背景误差方差和各向同性假设下背景误差相关长度的时空分布特征。按经验函数拟合该海域有效波高背景误差协方差时总残差平方和最小的原则给出了更为适用于该海域的描述公式。在上述工作基础上,采用最优插值同化方法将Jason-1和Jason-2卫星高度计有效波高数据连续同化到海浪模式Wave Watch Ⅲ,按业务化标准对2013年1月北印度洋海域的海浪场进行了同化预报试验,经浮标数据检验发现同化可使海浪24 h预报得到明显改进。     

JASON-2卫星高度计波高数据同化对中国渤黄海海浪数值预报影响研究

利用原国家海洋局北海分局浮标所测有效波高数据对Jason-2卫星高度计所测有效波高数据进行验证,采用50km空间窗和0.5h时间窗,得到219个时空配准点。对配准结果进行统计分析表明,Jason-2卫星高度计测得有效波高与浮标测量结果存在-0.277m的偏差,均方根误差为0.372m。利用最小二乘回归(OLR)对Jason-2有效波高数据进行校正可使其均方根误差下降至0.247m,减少34.5%。基于第三代海浪模式WAVEWATCH III对Jason-2有效波高数据进行最优插值同化试验,其中背景误差相关函数取为指数形式,相关距离尺度选为500km。与浮标观测数据比较表明,同化后模式有效波高均方根误差比未同化时减少11.56%,,能够有效地改善模式精度。以此为初始场进行为期3d的数值预报实验。与未同化实验相比,卫星高度计有效波高数据同化对模式0～72 h预报有不同程度的改善,改善程度随预报时间的增加而降低。     

基于海浪谱分解与重构的资料同化试验

针对有效波高资料提出一种海浪谱分解与重构的资料同化方案:利用历史时段内的有效波高观测资料和模式计算波高场,采用最优插值方法得到分析波高场;在WAVEWATCH-Ⅲ模式的波浪能量密度谱和有效波高分析值之间引入一个变异系数矩阵,描述模式的误差,以此为状态向量构建卡尔曼滤波系统,对分解过的海浪谱进行修正和重构,得到同化后的海浪谱初始场。利用美国阿拉斯加湾北部海域的7个浮标站进行同化和72 h预报试验,对连续1个月的预报结果进行统计表明:采用该同化方案后24 h预报结果的有效波高均方根误差比未同化的结果降低了0.13 m;同化方案对预报效果的影响可持续36 h左右,随着预报时效延长,同化的效果减弱。     

HY-2卫星高度计波高资料在集合最优插值同化中的应用研究——以台风“Lipee”为例

基于海浪模式SWAN（Simulating Waves Nearshore）,以台风“Lipee”为例,开展了集合最优插值（EnOI）同化HY-2卫星高度计有效波高（SWH）资料的台风浪数值预报影响研究。结果表明,利用HY-2卫星高度计波高资料结合EnOI方法进行同化,可有效改善海浪初始场质量,同化对绝对误差的改进可达15%,均方根误差改进14%。同化对预报误差、均方根误差都有一定程度的改进,其中在0~24 h预报时效内的改进最为明显,绝对误差可改进12%,均方根误差改进13%。研究结果不仅可为海洋预报、同化提供参考,而且可为进一步加强HY-2卫星高度计资料的应用提供技术支持。     

WAVEWATCH Ⅲ模式在全球海域的数值模拟试验及结果分析

基于第三代海浪数值预报模式WAVEWATCH III(v3.14),构建了全球区域海浪数值预报系统,采用1999年9月—2009年7月的Quik SCAT/NCEP混合风场作为驱动场,对模式进行了10a的积分。利用NDBC浮标数据及Jason-1卫星高度计资料对模式模拟结果进行了检验,结果表明:模式对全球海浪模拟效果较好。通过对模式误差的分析,为后续开展全球海浪同化工作中背景误差协方差矩阵的构建及集合样本的选取提供了依据。     

各向异性背景场误差协方差构建及在“凡亚比”台风的应用

利用相临过去时段预报结果中同一时刻不同时效的模式预报场差异,计算预报误差协方差,并基于集合-变分混合同化系统将其与静态背景场误差协方差结合,从而在同化系统中构建了具有各向异性和一定流依赖特征的背景场误差协方差。单点观测理想试验显示本方案改善了静态模型化背景场误差协方差的各向同性和流依赖性问题。“凡亚比”台风的一系列同化及模拟试验表明,从台风路径、强度等方面本文方案的效果都要优于三维变分法。本文方案在不需要集合预报,计算量与三维变分法相当的情况下,给同化系统引入了各向异性、一定流依赖特征的背景误差协方差,因此本方案适于在计算资源较为紧缺情况下,对时效要求较高的预报业务中应用。     

A 4D-variational approach applied to an eddy-permitting North Atlantic configuration: Synthetic and real data assimilation of altimeter observations

The increasing number of oceanic observations calls for the use of synthetic methods to provide consistent analyses of the oceanic variability that will support a better understanding of the underlying mechanisms. In this study, a 1/3° eddy-permitting model of the North Atlantic (from 20°S to 70°N) is combined with a 4D-variational method to estimate the oceanic state from altimeter observations. This resolution allows a better extraction of the physical content of altimeter data since the model spatial scales are more consistent with the data than coarser assimilation exercises because of a lower error in model representativity. Several strategies for the assimilation window are tested through twin experiments carried out under the following conditions: different window lengths and either a quasi-static (also known as progressive) variational assimilation with progressive extension of the window, or a simpler direct method without prior assimilation. From our set of experiments, the most efficient strategy is the use of both a simple direct assimilation method and a 90-day window. The assimilation of synthetic altimeter data constrains the model-temperature, -salinity and -velocity fields mainly over the first 1300 m where the error is the largest. Improvements occur not only in quiescent regions, but also in more energetic meso-scale regimes. Despite the existence of model- and surface forcing-errors as well as large errors in the first guess, the assimilation of real altimeter data proves to be consistent with our twin experiments. Indeed, the analyses show a better detachment of the Gulf Stream, weaker regional biases and more accurate positions for meso-scale structures. Independent hydrographic data (Argo floats and CTD cruises) and transports estimates along the OVIDE 2002 cruise show an improvement of the analysed oceanic state with respect to the assimilation-free case though water mass properties are still incorrectly represented. After assimilation, the North Atlantic heat transport in the model is in good agreement with independent estimates based on hydrographic data.     

海浪同化预报模型理论基础

将共轭变分同化方法应用于 LAGFD- WAM海浪数值模式 ,导出了海浪谱能量平衡方程的共轭方程以及风输入、破碎、底摩擦、波波非线性相互作用和波流相互作用的相应共轭源函数 ,建立了海浪同化模型 ,数值计算仍采用特征线嵌入计算格式 ,为合成孔径雷达波谱反演资料和卫星高度计有效波高资料同化奠定理论基础     

Data assimilation of partitioned HF radar wave data into Wavewatch III

In this study the assimilation of HF radar data into a high resolution, coastal Wavewatch III model is investigated. An optimal interpolation scheme is used to assimilate the data and the design of a background error covariance matrix which reflects the local conditions and difficulties associated with a coastal domain is discussed. Two assimilation schemes are trialled; a scheme which assimilates mean parameters from the HF radar data and a scheme which assimilates partitioned spectral HF radar data. This study demonstrates the feasibility of assimilating partitioned wave data into a coastal domain. The results show that the assimilation schemes provide satisfactory improvements to significant wave heights but more mixed results for mean periods. The best improvements are seen during a stormy period with turning winds. During this period the model is deficient at capturing the change in wave directions and the peak in the waveheights, while the high sea state ensures good quality HF radar data for assimilation. The study also suggests that there are both physical and practical advantages to assimilating partitioned wave data compared to assimilating mean parameters for the whole spectrum.     

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.     

Analysis of SARAL/AltiKa Wind and Wave over Indian Ocean and its Real-time Application in Wave Forecasting System at ISRO

The current study aims to analyze the wind and wave parameters over Indian Ocean region obtained from first Ka –band altimeter AltiKa onboard SARAL, a collaborative mission of Indian Space Research Organization (ISRO) and Centre National d'Etudes Spatiales (CNES), France. It also demonstrates a real time application of SARAL data by assimilating the wave height in a wave model operational at the Space Applications Centre, ISRO. State-of–the art coastal wave model Simulating Wave Near shore (SWAN) is used for this purpose. The well-tested optimal interpolation technique is adopted for assimilation. Before proceeding to the assimilation per se, SARAL/AltiKa Wind and Significant Wave Height (SWH) have been validated using in- situ observations and WAVEWATCH III model. Apart from assessment of wind and wave data quality, this also served the purpose of providing error covariance to be used in assimilation. Supremacy of the assimilation run over parallel control run without assimilation has been judged by comparing the results with buoy observations at Indian National Centre for Ocean Information System (INCOIS). The statistics of validation of the assimilation run has been found to be extremely encouraging and interesting.     

Variational data analysis with control of the forecast bias

We propose a methodology for the treatment of the systematic model error in variational data assimilation. The principle of the method is to add a systematic error correction term in the model equations and to include it in the variational assimilation control vector.
This method is applied to a simplified ocean circulation model in an identical twin experiment framework. It shows a noticeable improvement compared to the result of a classical variational assimilation scheme in which the systematic error is not corrected. The estimated systematic error correction term is sufficiently consistent with that needed by the model that it allows improvements not just to the analysis, but also during the forecast phase.