变分伴随数据同化方法在断面海温数值计算中的应用研究

以二维断面海温分布模型为例,利用海温实际观测数据,将变分伴随方法应用于断面海温初始场的优化。讨论了变分伴随方法的基本思想,分别从模型方程的连续和离散形式出发推导伴随模型系统,并对这两种途径建立的伴随系统之间的相互关系进行了分析。数值试验的结果表明了变分伴随数据同化方法在海温数值计算和数值预报业务中的良好的应用前景。     

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。     

最优插值法在海温数据同化中的应用研究

海洋声场环境是决定海军作战行动成败的重要组成要素之一,海洋的垂直温度结构作为影响声速场的最重要因素,为了更精确更为实际的反映其分布特点,对其各种数据进行同化研究是十分必要的。文中针对海温数据来源的特点,提出了一种海温数据同化的最优插值方法,较好地完成了各种观测数据与背景数据的同化,并用各种数据进行了检验,其结果表明,利用最优插值法对海温数据进行同化处理是可行的,且同化效果比较理想。     

融合法及其在数据同化中的应用研究

根据预报值具有最小方差这一要求,详细推导了融合法在观测数据为一维、多维和维数不同的情况下的具体同化表达形式,同时还给出了不同情况下与同化表达式相对应的预报误差公式.利用这些公式,可以用融合法处理常见的海洋观测数据的同化问题.在陆架海模式HAMSOM基础上,以4月份的渤海海表温度为例,我们验证了同化公式的正确性,并给出了同化后较好的同化结果。最后将融合法的同化结果与卡尔曼滤波同化结果进行了对比.比较表明,融合法使用起来更简单,且能有效地处理常见的海洋观测数据.     

Kalman滤波技术在海表温度预测中的应用

以EOF分解方法为基础,把AR模型和Kalman滤波方法相结合,建立了海表温度的预报模型。首先对历史时间序列资料进行EOF分解,在此基础上,利用时间权重系数建立AR(2)模型,并对此模型参数进行了改进,作为Kalman滤波的状态方程。然后用Kalman滤波方法对时间权重系数进行了滤波预测,并引入集合预报的思想对SST预测结果进行了重构,并与实况资料进行了相关性分析。以太平洋、印度洋、大西洋三大洋的热带海域为个例进行了预测试验。试验结果表明,预测效果较好,相关系数平均达到了98%以上,而残差方差在0.5以内。     

白噪声Kalman滤波模型在GPS单向授时中的应用

全球定位系统(GPS)的应用越来越广泛,尤其是高精度的星载原子钟使得授时的精度得到了很大的提高。针对GPS授时过程中接收机受到各种噪声的影响,利用Kalman滤波原理,建立状态方程和观测方程,对噪声进行分类并加以讨论。运用Kalman滤波原理对接收机钟差数据进行分析,计算结果表明Kalman滤波可提高GPS单向授时精度。     

海温数值预报资料同化试验Ⅰ.客观分析的最优插值法试验

利用最优插值法发展了一个海温短期数据预报的资料同化系统，并进行了一些客观分析试验。这个同化系统具有下述几个特点：（１）由于海温的保守性，在对海个时次的船舶报ＳＳＴ资料进行同化时，加入了前后相差６ｈ的相邻资料作为同时资料，增加了资料密度，较好地解决了海洋资料相对稀疏引起的问题；（２）利用模式的一系列后报试验结果来估计模式预报误差的标准差，这比常用主观猜测的方法更加客观；（３）对观测资料进行检误的质量     

修正Kalman滤波技术在复杂环境DVL标定中的应用研究

声学多普勒计程仪的安装偏角误差和比例因子误差是影响水下自主导航精度的主要误差源,需要进行精确误差标定.然而任何声呐性能均与海洋环境密切相关,如何实现复杂环境精确标定是计程仪工程应用中面临的实际问题.为此,在分析真实测速数据统计分布基础上,提出了一种基于修正Kalman滤波预处理的误差校准方法,并与标准Kalman滤波预...     

海温数值预报资料同化试验：Ⅰ.客观分析的最优插值法试验

利用最优插值法发展了一个海温短期数据预报的资料同化系统，并进行了一些客观分析试验。这个同化系统具有下述几个特点：（１）由于海温的保守性，在对海个时次的船舶报ＳＳＴ资料进行同化时，加入了前后相差６ｈ的相邻资料作为同时资料，增加了资料密度，较好地解决了海洋资料相对稀疏引起的问题；（２）利用模式的一系列后报试验结果来估计模式预报误差的标准差，这比常用主观猜测的方法更加客观；（３）对观测资料进行检误的质量     

海洋卫星遥感数据同化在海洋声场研究中的应用

卫星遥感数据同化技术已成为海洋学研究的一个有效手段。本文选取了日本以东黑潮流域作为研究海区,介绍了最优插值同化方法对海洋温度场和盐度场的分析,在此基础上对实验海区声场进行了研究。文中为了说明数据同化的优越性,利用WOA数据与其进行比较,体现了同化数据对真实场描述的可靠性。     

Assimilation of TOPEX/POSEIDON Altimeter Data with a Reduced Gravity Model of the Japan Sea

Sea level data measured by TOPEX/POSEIDON over the Japan Sea from 1993 to 1994 is analyzed by assimilation using an approximate Kalman filter with a 1.5 layer (reduced-gravity) shallow water model. The study aims to extract signals associated with the first baroclinic mode and to determine the extent of its significance. The assimilation dramatically improves the model south of the Polar Front where as much as 20 cm² of the observed sea level variance can be accounted for. In comparison, little variability in the northern cold water region is found consistent with the model dynamics, possibly due to significant differences in stratification.     

卡尔曼滤波在GPS潮位数据处理中的应用

利用RTK GPS技术测量潮位数据时,在把具体的潮位数据提取出来以后还会存在很多的异常点或不确定点,因此我们要在提取出具体波动的过程中实时的修正,才能得到有效的资料。文中简要介绍了卡尔曼滤波的基本原理,以及如何应用于GPS潮位数据的处理,并通过试验讨论卡尔曼滤波在数据处理中的效果。     

Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter

The Localized Weighted Ensemble Kalman Filter(LWEnKF) is a new nonlinear/non-Gaussian data assimilation(DA) method that can effectively alleviate the filter degradation problem faced by particle filtering, and it has great prospects for applications in geophysical models. In terms of operational applications, along-track sea surface height(AT-SSH), swath sea surface temperature(S-SST) and in-situ temperature and salinity(T/S) profiles are assimilated using the LWEnKF in the northern South China ...     

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.     

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.     

Approximate Variational Method for Ocean Data Assimilation

An approximate variational method is proposed to assimilate an oceanographic data set with a numerical ocean model. In the approximate method, the adjoint equation to a governing equation is derived and then converted to a finite difference form, in contrast to the ordinary, exact variational method which is composed of a finite difference equation adjoint to the finite difference governing equation. A cumbersome derivation of the adjoint equation is avoided, and finite difference schemes used for the original governing equation are easily utilized for the adjoint equation. This method has been verified with twin experiments. The flow field in the twin experiments is composed of dipole eddies in a two-layer quasi-geostrophic model. Initial and boundary conditions are control variables. The descent converges towards the exact field within 50 iterations, showing that the fundamental problem of the method (an unstable descent with a large number of iterations) does not appear. The approximate method is promising and should be tried with real data. This revised version was published online in July 2006 with corrections to the Cover Date.     

Ocean Data Assimilation in the Tropical Pacific: A Short Survey

This short survey presents several research and operational developments of ocean data assimilation in the tropical Pacific, primarily for climate-scale phenomena. Aspects of theoretical error estimations, diagnostics and practical reduced space techniques are also briefly reported. This revised version was published online in August 2006 with corrections to the Cover Date.     

Short-Range Prediction Experiments with Operational Data Assimilation System for the Kuroshio South of Japan

The short-range (one month) variability of the Kuroshio path was predicted in 84 experiments (90-day predictions) using a model in an operational data assimilation system based on data from 1993 to 1999. The predictions started from an initial condition or members of a set of initial conditions, obtained in a reanalysis experiment. The predictions represent the transition from straight to meander of the Kuroshio path, and the results have been analyzed according to previously proposed mechanisms of the transition with eddy propagation and interaction acting as a trigger of the meander and self-sustained oscillation. The reanalysis shows that the meander evolves due to eddy activity. Simulation (no assimilation) shows no meander state, even with the same atmospheric forcing as the prediction. It is suggested therefore that the initial condition contains information on the meander and the system can represent the evolution. Mean (standard deviation) values of the axis error for all 84 cases are 13, 17, and 20 (10, 10, and 12) km, in 138.5°E, in the 30-, 60-, and 90-day predictions respectively. The observed mean deviation from seasonal variation is 30 km. The predictive limit of the system is thus about 80 days. The time scale of the limit depends on which stage in the transition is adopted as the initial condition. The gradual decrease of the amplitude in a stage from meander to straight paths is also predicted. The predictive limit is about 20 days, which is shorter than the prediction of the opposite transition. This revised version was published online in July 2006 with corrections to the Cover Date.     

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.