Incremental 4D-VAR assimilation scheme based on Lorenz model

Four-dimensional variational(4D-VAR) data assimilation method is a perfect data assimilation solution in theory, but the compu- tational issue is quite difficult in operational implementation. The incremental 4D-VAR assimilation scheme is set up in order to re- duce the computational cost. It is shown that the accuracy of the observations, the length of the assimilation window and the choice of the first guess have an important influence on the assimilation outcome through the contrast experiment. Compared with the standard 4D-VAR assimilation scheme, the incremental 4D-VAR assimilation scheme shows its advantage in the computation speed through an assimilation experiment.     

基于CESM气候模式的同化模拟实验

利用nudging方法将GODAS次表层海温资料同化到"通用地球系统模式"(CESM)中,并分析了同化后的海温、降水、海表面气压等海洋、气象要素。结果表明,同化后的模拟结果与GODAS、NCEP、GPCP等资料的结果较为一致,较好的再现了中低纬海洋和大气的平均特征和随时间演变的规律,NINO3指数的相关系数达到0.90。但模拟结果仍存在一些问题,如大西洋西边界流偏强,赤道辐合带降水偏多等。     

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.     

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%。     

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.     

拉格朗日资料同化的研究进展

拉格朗日资料对了解海洋环流和大气环流的动力学过程起到很重要的作用,同化拉格朗日资料有很大的必要性.拉格朗日观测工具主要提供的是水团或空气微团的位置和速度等信息,因为拉格朗日资料提供的是移动框架的数据而不是欧拉格点上的模式变量,所以随着拉格朗日观测覆盖大部分海洋和全球海洋环流模式的实现,采用先进的同化方法充分利用这些资料来提高分析预报准确性势在必行.在同化方法不断的发展中最优插值、变分同化、卡尔曼滤波这几种方法都有很好的应用.     

A method of oceanographic data assimilation based on the theory of optimal control

Fundamental points of the computational procedure for the assimilation of observations by a model for a large-scale ocean circulation are discussed.Translated by Vladimir A. Puchkin.     

Cycling the representer algorithm for variational data assimilation with a nonlinear reduced gravity ocean model

The representer method was used by [Ngodock, H.E., Jacobs, G.A., Chen, M., 2006. The representer method, the ensemble Kalman filter and the ensemble Kalman smoother: a comparison study using a nonlinear reduced gravity ocean model. Ocean Modelling 12, 378–400] in a comparison study with the ensemble Kalman filter and smoother involving a 1.5 nonlinear reduced gravity idealized ocean model simulating the Loop Current (LC) and the Loop Current eddies (LCE) in the Gulf of Mexico. It was reported that the representer method was more accurate than its ensemble counterparts, yet it had difficulties fitting the data in the last month of the 4-month assimilation window when the data density was significantly decreased. The authors attributed this failure to increased advective nonlinearities in the presence of an eddy shedding causing the tangent linear model (TLM) to become inaccurate. In a separate study [Ngodock, H.E., Smith, S.R., Jacobs, G.A., 2007. Cycling the representer algorithm for variational data assimilation with the Lorenz attractor. Monthly Weather Review 135 (2), 373–386] applied the cycling representer algorithm to the Lorenz attractor and demonstrated that the cycling solution was able to accurately fit the data within each cycle and beyond the range of accuracy of the TLM, once adjustments were made in the early cycles, thus overcoming the difficulties of the non-cycling solution. The cycling algorithm is used here in assimilation experiments with the nonlinear reduced gravity model. It is shown that the cycling solution overcomes the difficulties encountered by the non-cycling solution due to a limited time range of accuracy of the TLM. Thus, for variational assimilation applications where the TLM accuracy is limited in time, the cycling representer becomes a very powerful and attractive alternative, given that its computational cost is significantly lower than that of the non-cycling algorithm.     

Cycling the representer algorithm for variational data assimilation with a nonlinear reduced gravity ocean model

The representer method was used by [Ngodock, H.E., Jacobs, G.A., Chen, M., 2006. The representer method, the ensemble Kalman filter and the ensemble Kalman smoother: a comparison study using a nonlinear reduced gravity ocean model. Ocean Modelling 12, 378–400] in a comparison study with the ensemble Kalman filter and smoother involving a 1.5 nonlinear reduced gravity idealized ocean model simulating the Loop Current (LC) and the Loop Current eddies (LCE) in the Gulf of Mexico. It was reported that the representer method was more accurate than its ensemble counterparts, yet it had difficulties fitting the data in the last month of the 4-month assimilation window when the data density was significantly decreased. The authors attributed this failure to increased advective nonlinearities in the presence of an eddy shedding causing the tangent linear model (TLM) to become inaccurate. In a separate study [Ngodock, H.E., Smith, S.R., Jacobs, G.A., 2007. Cycling the representer algorithm for variational data assimilation with the Lorenz attractor. Monthly Weather Review 135 (2), 373–386] applied the cycling representer algorithm to the Lorenz attractor and demonstrated that the cycling solution was able to accurately fit the data within each cycle and beyond the range of accuracy of the TLM, once adjustments were made in the early cycles, thus overcoming the difficulties of the non-cycling solution. The cycling algorithm is used here in assimilation experiments with the nonlinear reduced gravity model. It is shown that the cycling solution overcomes the difficulties encountered by the non-cycling solution due to a limited time range of accuracy of the TLM. Thus, for variational assimilation applications where the TLM accuracy is limited in time, the cycling representer becomes a very powerful and attractive alternative, given that its computational cost is significantly lower than that of the non-cycling algorithm.     

台风过程研究中四维资料同化方法的探讨及应用

与把任意分布的观测值通过空间和时间插值分析到网格点的方法相比较,结合动力关系分析大气场问题的方法,其优势十分明显.Charney等[1]建议在一个能提供时间连续和动力耦合的模式预报方程中,有机地结合现在和过去的资料.后来这个概念被发展为众所周知的四维资料同化(FDDA).起初,四维资料同化主要应用于全球尺度,现在,无论是动力初始化还是作为一个分析或者研究工具,四维资料同化都有效地应用在中尺度模式上[2,3].     

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.     

Optimization of the data assimilation algorithm in a model for synoptic dynamics of the velocity field

The results of numerical experiments involving data assimilation by a non-linear model for synoptic dynamics of the ocean are given. The results of application of simplified optimal filtration algorithms (with the correlation error function being parametrized for both linear and non-linear variants) and of the optimal interpolation are intercompared. The use of a non-linear equation to predict average values and a linear one to predict dispersion in the filtration algorithm is suggested as most appropriate for the cases, when anisotropy of the correlation function is to be included. In other cases, it is considered worth-while to apply optimal interpolation.Translated by V. Puchkin.     

Dynamic-stochastic model for assimilation of remotely-sensed sea surface temperature data

We consider the possibility of assimilating sea surface temperature data by a combined ocean dynamics model incorporating a three-dimensional hydrodynamic model for synoptic variability for the considered ocean area and an integral model block describing the upper mixed layer. Model numerical simulations and experiments involving satellite-derived data are analysed.Translated by V. Puchkin.     

Analysis of the response of a non-linear data assimilation algorithm in a one-dimensional model of the upper ocean

A one-dimensional (1D) integral dynamico-stochastic model of the upper ocean with a non-linear assimilation algorithm is considered. The accuracy of computing the characteristics of the upper layer depends essentially on the values of the empirical coefficients. A numerical experiment was carried out which verified the efficiency of the model's adaptive mechanism operation when different values of the empirical coefficients and their variances were preset. Recommendations on assigment of the model's initial parameters are derived.Translated by Mikhail M. Trufanov.     

海洋环流模式中卫星遥感资料同化的应用进展

物理海洋研究长久以来一直受到观测资料不足的制约,然而这一状况随着现代观测技术的迅猛发展得到了很大的改善。卫星遥感技术的发展提供了覆盖全球的、连续、实时的卫星观测数据,这是其他任何资料都无法比拟的。这些数据大部分难以直接运用来改善气候预测或数值模拟分析,然而资料同化技术的出现和发展改善了这一情况。     

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%.     

Monitoring of the large-scale dynamics of the ocean based on the assimilation of satellite data

An examle of satellite monitoring of the large-scale dynamics of the eastern sector of the tropical Atlantic Ocean in April 1990 is considered. As the satellite-derived data, a series of averaged 5-day maps of the remote data on the upper ocean temperature field obtained from satellites of the NOAA series was used. The profundity of the effect of the satellite data is estimated. Recommendations are provided. for selecting the regions most appropriate for rapid analysis of the circulation using satellite data.Translated by Mikhail M. Trufanov.