Scientific design and preliminary results of three-dimensional variational data assimilation system of GRAPES

The scientific design and preliminary results of the data assimilation component of the Global-Regional Prediction and Assimilation System （GRAPES） recently developed in China Meteorological Administration （CMA） are presented in this paper. This is a three-dimensional variational （3DVar） assimilation system set up on global and regional grid meshes favorable for direct assimilation of the space-based remote sensing data and matching the frame work of the prediction model GRAPES. The state variables are assumed to decompose balanced and unbalanced components. By introducing a simple transformation from the state variables to the control variables with a recursive or spectral filter, the convergence rate of iteration for minimization of the cost function in 3DVar is greatly accelerated. The definition of dynamical balance depends on the characteristic scale of the circulation considered. The ratio of the balanced to the unbalanced parts is controlled by the prescribed statistics of background errors. Idealized trials produce the same results as the analytic solution. The results of real data case studies show the capability of the system to improve analysis compared to the traditional schemes, Finally, further development of the system is discussed.     

SST data assimilation experiments using an adaptive variational method

An adaptive variational data assimilation method is proposed by Zhu and Kamachi^[1]. This method can adaptively adjust the model state without knowing explicitly the model error covariance matrix. The method enables very flexible ways to form some reduced order problems. A proper reduced order problem not only reduces computational burden but also leads to corrections that are more consistent with the model dynamics that trends to produce better forecast. These features make the adaptive variational method a good candidate for SST data assimilation because the model error of an ocean model is usually difficult to estimate. We applied this method to an SST data assimilation problem using the LOTUS data sets and an ocean mixed layer model (Mellor-Yamada level 2.5). Results of assimilation experiments showed good skill of improvement subsurface temperatures by assimilating surface observation alone.     

Effect of four-dimensional variational data assimilation in case of nonlinear instability

The effect of four-dimensional variational data assimilation on the reduction of the forecast errors is investigated for both stable and unstable flows. Numerical results show that the effect is generally positive. Particularly,its effect is much more significant in the presence of nonlinear instability     

Three-dimensional variational data assimilation implemented in numerical modeling for Wuhan torrential rain in July 1998

On July 20～21, 1998, a torrential rain with 24-hour rainfall of 286 mm occurred in Wuhan, Hubei Province of China. In this study, a three-dimensional variational data assimilation system developed at the Fifth-Generation Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model (MM5) along with conventional rawinsonde sounding and surface observation is used for modeling the heavy rain. It successfully incorporates observational data of temperature, humidity and wind, and provides an optimal analysis being in favor of the formation of heavy rain. The 24-hour rainfall predicted by the new scheme is 109 mm rather than 2.5 mm forecasted by control experiment. Furthermore, the forecasted accuracy of location and time-variation of hourly rainfall reproduced by this model is also improved. Keywords：variational data assimilation, torrential rain, numerical modeling.     

海洋变分同化背景场误差协方差矩阵构造技术

为改进背景场误差协方差矩阵构造技术,提高海洋资料变分同化效果,首先将背景场误差协方差矩阵分解为背景误差标准偏差矩阵、非平衡变量空间相关算子以及多变量间平衡算子3部分,然后利用广义扩散方程模拟空间相关算子,联合引入温盐约束关系、线性状态方程、海表高度诊断方程、静力平衡、地转平衡关系模拟多变量平衡,最后给出了一种适用于海洋资料变分同化的背景场误差协方差矩阵构造方案。单点海表温度观测模拟同化试验表明,该方案可实现观测信息在三维空间、多变量间的传播,充分体现了背景场误差协方差矩阵在同化中的作用。     

Forming proper ensemble forecast initial members with four-dimensional variational data assimilation method

A method has been presented to improve ensemble forecast by utilizing these initial members generated by four-dimensional variational data assimilation (4-D VDA), to conquer limitation of those initial members generated by Monte Carlo forecast (MCF) or lagged average forecast (LAF). This method possesses significant statistical characteristic of MCF, and by virtue of LAP that contains multi-time information and its initial members are harmonic with the dynamic model, six groups of numerical control and contrast experiments were performed with T42 spectral 4-D VDA system. The results show that in dekad range ensemble forecast with initial members generated by this method is prior to that by LAP. The anomaly correlation coefficient (ACC) of 500 hPa height in 10-d average by employing this method is 0.01-0.04 larger than by LAP, and root mean square error (RMSE) less than 0.2-0.4 dagpm.     

Assimilation analysis of Rammasun typhoon structure over Northwest Pacific using satellite data

The kinetic and thermodynamic structure of typhoon Rammasun (No. 0205) over the Northwest Pacific has been analyzed, using NOAA-16 polar orbiting Advanced Microwave Sounding Unit (AMSU) data collected on 2 July, 2002. The three-dimensional variational (3DVAR) assimilation technology is used to assimilate the satellite observation. The results show that the characteristics of the 3D typhoon structure can be more reasonably described from the assimilated data. The warm-cored structure of the typhoon is enhanced in the analyzed field, which corresponds to strong typhoon. The typhoon cyclonic circulation in the middle and lower layers is apparently strengthened, and the strong anticyclonic circulation appears at the top of the typhoon. The water vapor and its supply in the typhoon are enhanced. The microwave assimilation data may be used to supply the lack of the conventional observation data over the tropical ocean.     

利用改进的并行粒子群算法对变分资料同化的研究

近年来,为了提高同化精度和减少同化时间,粒子群算法(PSO)被引入到数值天气预报资料同化中来.粒子群算法虽然令同化精度有所提高,但同化时间仍然存在较大缺陷.基于此,首先设计了一种改进的并行粒子群算法(P2PSO),然后应用于含不连续"开关"过程的变分资料同化中,与时变双重压缩因子粒子群算法(PSOTVCF)和动态权重粒...     

基于并行粒子群优化算法的变分资料同化

为了提升粒子群优化算法(PSO)应用到实际大气和海洋模式的资料同化时的计算时效性,针对一种最新提出的基于方向的粒子群优化算法(PSOBD),使用消息传递接口(MPI)和整体同步并行(BSP)计算模型,设计PSOBD的并行算法,较好地实现了BSP结构下PSOBD的全局通信操作。为检验并行化后的PSOBD的计算时效,将其用于潜水方程的资料同化并与基于串行的PSOBD的同化数值试验结果比较。大量的孪生同化试验结果的统计分析显示,并行化后的PSOBD与串行PSOBD一样能产生高质量的同化结果,且计算时效相对串行PSOBD提高了13倍以上。这一结果为PSOBD算法用于实际模式的四维变分资料同化(4D-Var)提供了依据。     

具有“on-off”过程的变分资料同化

为了验证伴随方法的有效性,利用一个含有"on-off"振荡的概念模式,研究了含物理参数化不连续性的变分资料同化中,代价函数关于初值梯度的存在性及伴随方法的可行性.结果表明:在解析情形中,尽管控制方程中出现"on-off" 振荡,代价函数关于初值的梯度除个别情形外总是存在的,且可由伴随模式的反向积分来表示.     

大亚湾海域流场数据同化的数值试验

用有限差分隐式方向交替(alternatingdirectionimplicit,ADI)法求解潮波方程,并进行数据同化的数值试验,与未经同化的ADI法计算值相比有较好的效果.结果表明,采用数据同化系数1 12×10-4s-1为最佳选择.     

海洋数值模拟中的伴随数据同化方法

从伴随方程的推导、伴随数值模型的构造以及伴随数据同化中的最优化方法3个方面对伴随数据同化基本理论的研究现状进行了较为系统的归纳和评述;从优化初始状态、优化模式中不确定参数和优化开边界条件等3方面,综述了伴随法在海洋数值模拟中的应用.最后讨论了伴随法在海洋数值模拟应用中存在的问题,指出,对于伴随法用于数值模型的参数优化,只能对其在物理意义范围内进行适当调整和估计,而不能背离物理背景使目标函数最优.     

改进的粒子群算法在太阳能光伏发电资料同化中的应用研究

资料同化是目前太阳能光伏发电预测研究的一个关键和难点.近年来,遗传算法和粒子群算法等智能优化算法被引入到四维变分同化中.针对基于分子运动论的粒子群算法(MPSO)在处理大量数据时速度慢的不足,该文提出了并行分子运动论粒子群算法(PMPSO),并行计算的基本思想是将粒子群分成N个子集,每个子集交给一个线程控制,同时进行粒...     

Development of a middle and low latitude theoretical ionospheric model and an observation system data assimilation experiment

On the basis of previous work, we develop a middle and low latitude theoretical ionospheric model in this paper, named Theoretical Ionospheric Model of the Earth in the Institute of Geology and Geophysics, Chinese Academy of Sciences （TIME-IGGCAS）. TIME-IGGCAS solves the equations of mass continuity, motion and energy of electron and ions self-consistently and uses an eccentric dipole field approximation to the Earth＇s magnetic field. We combine the Eulerian and Lagrangian approaches in the model and take account of the plasma ExB drift velocity. Calculation results reveal that the model is steady and credible and can reproduce most large-scale features of ionosphere. By using TIME-IGGCAS, we carried out an observation system data assimilation experiment. Assimilation results show that the ExB drift velocity can be accurately estimated by ingesting the observed foF2 and hmF2 into the model applying nonlinear least-square fit method. We suggest that this work is of great significance in the development of ionospheric data assimilation model to give better nowcast and forecast of ionosphere.     

北斗二代卫星导航系统在海洋资料浮标监控与管理中的应用

为有效地实现大规模、多类型海洋资料浮标的运行监控与管理,依托北斗二代卫星导航系统的定位和短信通信功能,分别实现浮标位置定位和数据传送,将北斗定位与GPS定位相结合以弥补位置数据丢失、错误等问题。通过数据分包技术克服北斗通讯数据长度的限制,并设计了数据包缓冲接收和数据包动态拼接的方法,有效地实现大规模的浮标数据接收处理。实验结果表明,该方式定位精度高、通讯稳定性好、数据接收率高,能有效满足海洋资料浮标用户对于浮标的管理需求。     

STMAS中雷达反射率数据三维变分同化方案

介绍了在时空多尺度分析系统(STMAS)中以雨水含量作为控制变量,利用Z-qr关系,实现对区域中多部雷达回波强度资料三维变分同化方案,和水汽惩罚项模块方案。方案避免了利用Z-qr关系直接以回波强度或雨水混合比为控制变量会造成一阶导数不连续的问题。实例分析同化后初始场表明,从同化后的雨水混合比反算出来的回波强度与观测结果保持了较好的一致性。以2009年8月的MORAKOT台风个例为例,设计实验来分析本文提出的雷达回波强度资料同化和水汽惩罚项方案效果。结果表明:雷达回波强度资料的同化方案(STMAS方案),提高了降水位置和强度预报的准确性; 加入惩罚项和对雷达回波强度资料的同化模块后(HYDRO方案),削弱了水汽影响并加强了其他物理量的变化,相对STMAS方案,台风总降水增强了,但在强降水区域的降水强度减弱; 相对控制试验,2种方案均改善了降水位置和强度的预报。     

环境大气中数据同化技术方法及应用

 从数据同化的角度,对卡尔曼滤波技术、元启发式算法、贝叶斯推导及逆扩散/轨迹模拟技术、非参数化回归等技术在污染环境大气模拟技术中的应用情况进行了总结分析。分析结果表明,经多种优化方法试用于解决环境监测数据的同化问题中,元启发式算法结合传统优化技术在有效解决环境监测的数据同化问题中具有良好应用前景。     

Hilbert空间中的广义非线性混合拟变分包含

本文在H ilbert空间考虑了一类广义混合拟变分包含,利用预解式概念,得到了这类广义非线性混合拟变分包含在H ilbert空间的解,证明了由算法生成的迭代序列的收敛性。     

Development of a Chinese land data assimilation system: its progress and prospects

The objective of land data assimilation is to merge multi-source observations into the dynamics of land surface model for improving the estimation of land surface states. We have developed a land data assimilation system for China’s land territory. In this system, the Common Land Model and Simple Biosphere Model 2 are used to simulate land surface processes. The radiative transfer models of thawed and frozen soil, snow, lake, and vegetation are used as observation operators to transfer model predictions into estimated brightness temperatures. A Monte-Carlo based sequential filter, the ensemble Kalman filter, is implemented as data assimilation method to integrate modeling and observation. The system is capable of assimilating passive microwave remotely sensed data such as special sensor microwave/imager (SSM/I), TRMM microwave imager (TMI), and advanced microwave scanning radiometer enhanced for EOS (AMSR-E) and the conventional in situ measurements of soil and snow. A spatiotemporally consistent assimilated dataset for soil moisture, soil temperature, snow and frozen soil, with a spatial resolution of 0.25 degree and temporal resolution of one hour, has been produced. This paper introduces the development of Chinese land data assimilation system and the progress made on data assimilation algorithms, land surface modeling, microwave remote sensing of land surface hydrological variables, and the preparation of atmospheric forcing data. The distinct characteristics and challenges of developing land data assimilation system and the perspectives for future development are also discussed.