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.     

Direct assimilation of satellite radiance data in GRAPES variational assimilation system

Variational method is capable of dealing with observations that have a complicated nonlinear relation with model variables representative of the atmospheric state, and so make it possible to directly assimilate such measured variables as satellite radiance, which have a nonlinear relation with the model variables. Assimilation of any type of observations requires a corresponding observation operator, which establishes a specific mapping from the space of the model state to the space of observation. This paper presents in detail how the direct assimilation of real satellite radiance data is implemented in the GRAPES-3DVar analysis system. It focuses on all the components of the observation operator for direct assimilation of real satellite radiance data, including a spatial interpolation operator that transforms variables from model grid points to observation locations, a physical transformation from model variables to observed elements with different choices of model variables, and a data quality control. Assimilation experiments, using satellite radiances such as NOAA17 AMSU-A and AMSU-B （Advanced Microwave Sounding Unit）, are carried out with two different schemes. The results from these experiments can be physically understood and clearly reflect a rational effect of direct assimilation of satellite radiance data in GRAPES-3DVar analysis system.     

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.     

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.     

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.     

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...     

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。     

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.     

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.     

考虑材料应变软化的非线性有限元的改进变Kp法

针对变Kp法进行非线性有限元求解时，存在求解精度低和应变软化难以模拟的缺点，分析了变Kp法与增量法内在联系与区别，将初应力迭代法的基本思想引进到变Kp法，提出了改进变Kp法。不仅保持了原方法求解效率高的优点，而且可改善原方法求解精度，使该方法适用于材料应变软化的数值模拟。     

求解一类新的非线性变分不等式的神经网络

考虑了一类新的非线性变分不等式,提出了求解的一个神经网络模型．在映射弱强制条件下,严格证明了该网络是Lyapunov稳定的,并且渐进收敛于原问题的一个精确解．此外,在适当的条件下证明了该模型的指数稳定性．数值实例表明该模型可行且有效．     

掩星折射率和弯角同化对台风预报影响的对比

为研究无线电掩星探测(GPS RO)弯角和折射率同化对数值预报的影响,以2012年17号台风"杰拉华"为例,利用美国国家环境预报中心(NCEP)的业务同化系统(GSI),以NCEP预报场资料为初始场,分别使用一维弯角观测算子和一维折射率观测算子同化弯角和折射率数据,并与观测数据对比,分析比较了同化掩星弯角和折射率资料对台风路径、强度及大气状态变量的影响。结果表明,在完成掩星弯角和折射率资料同化后,台风路径的预报水平得到明显改进,其中同化弯角资料可以使路径误差减少32%,而同化折射率资料只能减少13%,同化2种掩星资料对台风强度预报也有一定的影响,其中同化弯角资料后在前2天预报的台风中心气压更接近于实际情况,而同化折射率资料对预报有负影响。     

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.     

等离子体双流不稳定性中一类非线性方程的数值方法

研制了一个称为修正ＡＢＳ方法的计算机Ｃｏｄｅ，利用该Ｃｏｄｅ数值求解了等离子体双流不稳定性激发条件中含有积分项的一类非线性方程组，获得了满意的结果，修正ＡＢＳ方法的收敛速度为平方了介，理论和实践都证明采用该方法求解非线性方程组是颇为有效的。     

非线性数据分析中的算法优选

运用模糊优选理论建立非线性数据分析方案的优选模型，进行非线性数据分析方案选择，能有效地减少主观因素的影响，优选结果准确可靠。结合实例介绍了模型的使用方法。     

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 sys- tem,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 bright- ness temperatures.A Monte-Carlo based sequential filter,the ensemble Kalman filter,is implemented as data assimilation method to inte- grate modeling and observation.The system is capable of assimilating passive microwave remotely sensed data such as special sensor mi- crowave/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 sur- face modeling,microwave remote sensing of land surface hydrological variables,and the preparation of atmospheric forcing data.The dis- tinct characteristics and challenges of developing land data assimilation system and the perspectives for future development are also dis- cussed.     

On reduced variational equations of coupled systems

A coupled system of three nonlinear oscillators with symmetric couplings is studied. It turns out that there exists a reduced variational equation to the in-phase periodic solution of such a coupled system. By the symmetry one establishes a structural property for the monodromy matrix of the reduced variational equation, which simplifies the computation of multipliers to a great extent. As an application of the above results, a coupled system of three Poincaré oscillators is discussed as well. Supported by the National Natural Science Foundation of China Zeng Xianwu: born in Dec. 1938, Professor     

耦合遗传算法的数据同化系统误差处理方法

针对数据同化系统中的误差估计与处理问题,介绍了集合滤波数据同化系统中各种误差来源及特征;侧重于在集合数据同化中为防止滤波发散的乘数放大法、附加放大法和松弛先验法等模型误差处理方案,利用经典的非线性模型-Lorenz模型开展了数值试验.在此基础上,提出了一种耦合遗传寻优算法的数据同化系统,来解决以往的误差调节因子由反复实...     

非线性约束条件的污染数据的L2-估计

讨论非线性回归模型yi=f(xi,β)+ei,i=1,2,...,n,其中Eei=0,Eei2=α21,假设yi受到另一独立同分布随机变量序列ui的污染,仅能观察到污染数据y*i=(1-v)yi+vui,0≤v《1,v为未知参数.估计问题带有等式和不等式约束,约束是非线性的.首先利用矩估计方法给出污染参数的估计,最后利用最优化方法讨论了其非线性约束的L2-估计渐近问题,并给出估计量是概率有界的等相关结果.