数字滤波弱约束四维变分同化在台风初始化中的应用Ⅰ.个例试验

传统四维变分(4D-Var)同化台风初始化中,bogus资料的同化将不可避免引起初始时刻风场和质量场之间的非平衡,在预报时段产生高频振荡噪音,进而影响数值模拟效果.如何有效滤除bogus资料引入产生的高频噪音非常重要.本文为第Ⅰ部分,基于数字滤波弱约束4D-Var方法,开展台风初始化个例试验研究.2010年Chaba台风两个不同时刻数值试验结果表明:bogus资料4D-Var同化台风初始化中,呈现出与暴雨个例明显不同的3 h地面气压倾向特征,不同数字滤波权重试验在初始时刻差异小,在预报1—4 h差异明显;实施数字滤波弱约束使得初始时刻700 hPa垂直速度场和850 hPa散度场在台风区域减小,bogus资料为引起高频噪音的一个重要因素;数字滤波权重选择是影响台风同化和数值模拟效果的重要因素,合理的数字滤波权重选择试验可明显改善台风路径和强度预报,相对于台风强度预报,路径预报改善更加明显;同时,某些个例中,数字滤波权重的选择直接决定着台风数值模拟的成功与否.不同台风个例相对最佳数字滤波权重并不相同,如何选择数字滤波权重非常重要.第Ⅱ部分中将针对bogus资料同化台风初始化,提出一种数字滤波权重优选方案.     

数字滤波弱约束四维变分同化在台风初始化中的应用(Ⅱ):数字滤波权重选择

数字滤波权重的选择是影响台风路径和强度预报的重要因素,某些个例中直接决定着台风数值模拟的成功与否,合适的数字滤波权重非常重要.本文利用2010年3个台风7个不同初始时刻个例,开展数字滤波弱约束四维变分同化(4D-Var)台风初始化试验,分析不同数字滤波权重对台风路径和强度预报的影响;基于上述台风路径和强度预报后验统计信息,以及台风中心区域800 km范围和整个模拟区域3 h平均地面气压倾向信息,提出一种bogus资料同化台风初始化中数字滤波权重优选方案,并进行理论解释说明;同时,指出bogus资料同化台风初始化中,常规数字滤波权重为1000更合理,明显有别于常规天气和暴雨天气权重设置.     

以全球谱模式为约束的四维变分资料同化系统YH4DVAR的设计和实现

四维变分资料同化仍将是未来相当长时间内业务数值天气预报中所使用的主流同化方法．针对全球数值天气预报业务系统对气象资料同化技术的需求,在WRFDA软件框架结构的基础上,发展了一个与全球谱模式配套使用的四维变分资料同化系统YH4DVAR．系统将背景场、观测处理、重力波控制和偏差订正进行综合考虑,设计了一体化目标函数,引入了小波背景场误差协方差模型,实现了增量方法以及卫星遥感资料的直接同化．单点试验表明YH4DVAR的背景场误差模型具有各向异性、垂直相关和水平相关不可分离性、以及与位置的相关等特性．从2009年7月到2010年6月的分析预报试验结果表明,由YH4DVAR和全球谱模式组成的分析预报系统在北半球和亚洲地区的可用预报时效可以达到8天以上．     

大亚湾核电站周围大气流场监测数据同化分析

在大亚湾核电站周围大气流场分布模拟计算中,引入了一种简化的同化技术,用以解决观测数据在时间、空间上分布不均匀导致的流场预报误差.测试并分析了该同化技术对计算模式和初始场的影响,结果表明该同化方法对模式误差和初始场误差的抑制是显著的.     

海水温度垂直分布预报数据同化的离散伴随算子法

以一维水温模型为例,利用伴随算子法进行海洋观测数据同化,以便给水温的数值预报提供一个较准确的初始场。讨论了离散伴随算子法的思想,最优化过程,计算水温方程和伴随方程的差分格式,并图示描述水温场的初始猜测和同化后的分布。     

模式误差弱约束四维变分同化研究

经典四维变分同化(four dimensional variational data assimilation,简称4dvar)中认为模式是完美的, 而实际数值模式中存在的模式误差将对变分同化效果产生直接影响, 弱约束4dvar的提出为4dvar中考虑模式误差的影响提供了一种可行途径. 研究了基于模式误差控制变量的弱约束4dvar基本理论, 并在一维浅水方程组中对其同化效果及估计模式误差的有效性进行相关数值实验研究. 结果表明,在模式误差不可忽略的情况下,模式误差弱约束4dvar在整个区间 (包括同化区间和预报区间)上预报误差均比传统4dvar小, 在模式误差相对较大时改进效果更明显,具有明显优越性; 并且,模式误差弱约束4dvar能有效估计模式误差大小及分布形式.     

优化模式物理参数的扩展四维变分同化方法

数值模拟的一个重要误差来源是模式物理参数,为提高模拟准确率,如何改进模式物理参数是亟需解决的问题.本文对经典四维变分同化技术进行了改进,提出了一种新的利用观测资料来同时优化模式初始场和物理参数的扩展四维变分同化方法,并以Ekman边界层模式和Lorenz模式为例进行了数值试验.结果表明,利用本文提出的新方法,通过对观测资料的变分同化,可以在实现对模式初始场进行优化的同时,纠正了模式物理参数中的误差,从而有效提高了模式的模拟准确率.该方法对于改进数值模式物理参数有着重要的促进意义.     

GPS掩星一维弯曲角算子在四维变分资料同化系统中的实现方法研究

GPS无线电掩星资料的良好特性使其作为十分重要的观测资料在欧洲 及美国数值天气预报中效果明显. 本文设计了一维弯曲角算子在四维变分资料同化系统中的实现方法和质量控制策略. 详细阐述了一维弯曲角正演算子及其切线性/伴随算子在全球四维变分资料同化系统中的实现步骤和过程, 其中折射率计算中采用Rüeger的折射率最佳平均因子的三项表达式, 计算出更加精确的折射率值. 对2012年8月份的GPS无线电掩星资料进行了观测误差统计, 引入了更加符合四维变分资料同化系统实际情况的一维弯曲角观测误差模型和质量控制策略. 同时, 对比了观测误差统计试验中同化GPS无线电掩星资料的模式背景场和未同化GPS无线电掩星资料的背景场的观测误差统计结果, 发现同化GPS无线电掩星资料之后的观测误差统计结果在全球区域正作用十分明显, 尤其是在中低纬度地区, 高度在10 km 以上的区域观测误差减小十分明显. 关键词： GPS无线电掩星 一维弯曲角算子 四维变分同化系统 质量控制     

变分同化中水平误差函数的正交小波模拟新方法

背景误差协方差是变分资料同化系统中的一个重要组成部分,能将观测信息从观测点传播到周围的模式格点和垂直层上.为了模拟背景误差协方差中水平误差函数的非均匀性和各向异性,提出了一种用正交小波变换模拟水平误差函数的新方法.试验结果表明：新方法能模拟出水平误差函数中固有的非均匀性和各向异性,极好地表示了背景误差协方差中固有的结构和特征. 关键词： 变分资料同化 背景误差协方差 二维正交小波 水平误差函数     

强对流降水过程动力因子分析和预报研究

利用对流涡度矢量垂直分量,湿热力平流参数和波作用密度等动力因子对2009年7月8日发生在我国华北南部地区的强对流降水过程进行诊断分析,结果表明,这些动力因子能够综合描述降水系统水平风场垂直切变,涡度扰动以及位温平流和广义位温(及其扰动)的经向梯度等动力学和热力学特征,在降水区对流层中低层表现为强信号,在非降水区表现为弱信号.利用美国NCEP/NCAR 0.5度GFS的6 h,12 h,18 h和24 h预报资料计算分析2009年6月2日至10月1日动力因子对观测降水的指示预测作用,结果表明,动力因子对观测 关键词： 对流涡度矢量垂直分量 湿热力平流参数 波作用密度     

Data assimilation for the Navier–Stokes- equations

The well-posedness of the data assimilation problem for the Navier–Stokes-α equations on a bounded three-dimensional domain is investigated. The data assimilation procedures under consideration are the adjoint method of variational data assimilation (4D-Var) and the method of continuous data assimilation. Concerning the adjoint method the existence of optimal initial conditions with respect to an observation-dependent cost functional is proven, the optimizers are characterized by a first-order necessary condition involving the adjoint linearized Navier–Stokes-α equations and conditions for the uniqueness of the initial conditions are given. Well-posedness of the continuous data assimilation problem is proven and convergence rates in terms of observational resolution are provided.     

Impact of GNSS radio occultation bending angle data assimilation in YH4DVAR system

Global Navigation Satellite System （GNSS） radio occultation measurements have been assimilated into the four- dimensional variational data assimilation system （YH4DVAR） using a one-dimensional bending angle operator （GBAO） as a new type of observation. For the sake of verifying the impact of GNSS radio occultation （RO） measurements to the data assimilation system, three experiments have been conducted. The statistical results of the analysis error experiment and forecast skill experiment show that the GNSS RO measurements have an impact on the analysis system. The typhoon forecast experiment shows the impact on the important weather process. They all have a positive impact on the weather forecast. Lastly, we look forward to future work on the observation system simulation experiment （OSSE） to investigate the impact of GNSS RO measurements as a function of observation number, which is an effective method to estimate the saturation of the observation number.     

The long pulse effect in FEL's

The paper, by means of Green's function and Fourier spectral theory, investigates spontaneous radiation of a single pulse electron bean in free electron lasers with a helically periodical magnetic field. It is found that there are fast and slow electroagnetic waves as well as normal in the interaction region, and that the energy spectrum of spontaneous emission is not only dependent on the imposed helical field, but also modulated sensitively by the electron beam pulse. Under some conditions, the spectrum may have main peaks more than one, showing oscillations, that is, the long pulse effect.     

Mixed-mode oscillations and cluster patterns in an electrochemical relaxation oscillator under galvanostatic control

We studied the dynamics of a prototypical electrochemical model, the electro-oxidation of hydrogen in the presence of poisons, under galvanostatic conditions. The lumped system exhibits relaxation oscillations, which develop mixed-mode oscillations (MMOs) for low preset currents. A fast-slow analysis of the homogeneous dynamics reveals that the MMOs arise from a fast oscillating subsystem and a one-dimensional slow manifold. In the spatially extended system, the galvanostatic constraint imposes a synchronizing global coupling that drives the system into cluster patterns. The properties of the cluster patterns (CPs) result from an intricate interplay of the nature of the local oscillators, the global constraint, and a nonlocal coupling through the electrolyte. In particular, we find that the global constraint suppresses small-amplitude oscillations of MMOs and prevents domains oscillating out of phase from occupying equal regions in phase space. The nonlocal coupling causes each individual clustered region to oscillate on a different limit cycle. Typically multistability of CPs is found. Coexisting patterns possess different oscillation periods and a different total fraction in space that occupies the in-phase or out-of-phase state, respectively.     

Predicting air quality: Improvements through advanced methods to integrate models and measurements

Air quality prediction plays an important role in the management of our environment. Computational power and efficiencies have advanced to the point where chemical transport models can predict pollution in an urban air shed with spatial resolution less than a kilometer, and cover the globe with a horizontal resolution of less than 50 km. Predicting air quality remains a challenge due to the complexity of the governing processes and the strong coupling across scales. While air quality prediction is closely aligned with weather prediction, there are important differences, including the role of pollution emissions and their associated large uncertainties. Improvements in air quality prediction require a close integration of observations. As more atmospheric chemical observations become available chemical data assimilation is expected to play an essential role in air quality forecasting. In this paper advances in air quality forecasting are discussed with an emphasis on data assimilation. Applications of the four-dimensional variational method (4D-Var) and the ensemble Kalman filter (EnKF) approach are presented and the computation challenges are discussed.     

Data assimilation using Ensemble Transform Kalman Filter (ETKF) in ROMS model for Indian Ocean

Study of Oceans dynamics and forecast is crucial as it influences the regional climate and other marine activities. Forecasting oceanographic states like sea surface currents, Sea surface temperature (SST) and mixed layer depth at different time scales is extremely important for these activities. These forecasts are generated by various ocean general circulation models (OGCM). One such model is the Regional Ocean Modelling System (ROMS). Though ROMS can simulate several features of ocean, it cannot reproduce the thermocline of the ocean properly. Solution to this problem is to incorporates data assimilation (DA) in the model. DA system using Ensemble Transform Kalman Filter (ETKF) has been developed for ROMS model to improve the accuracy of the model forecast. To assimilate data temperature and salinity from ARGO data has been used as observation. Assimilated temperature and salinity without localization shows oscillations compared to the model run without assimilation for India Ocean. Same was also found for u and v-velocity fields. With localization we found that the state variables are diverging within the localization scale.     

自然循环流动不稳定性的多目标优化极限学习机预测方法

极限学习机是近年来提出的一种前向单隐层神经网络训练算法,具有训练速度快、不会陷入局部最优等优点,但其性能会受到随机选取的输入权值和阈值的影响.针对这一问题,提出一种基于多目标优化的改进极限学习机,将训练误差和输出层权值的均方最小化同时作为优化目标,采用带精英策略的快速非支配排序遗传算法对极限学习机的输入层到隐层的权值和阈值进行优化.将该算法应用于摇摆工况下自然循环系统不规则复合型流量脉动的多步滚动预测,分析了训练误差和输出层权值对不同步长预测效果的影响.仿真结果表明,优化极限学习机预测误差可以用较小的网络规模获得很好的泛化能力.为流动不稳定性的实时预测提供了一种准确度较高的途径,其预测结果可以作为核动力系统操作员的参考.     

New data assimilation system DNDAS for high-dimensional models

The tangent linear(TL) models and adjoint(AD) models have brought great difficulties for the development of variational data assimilation system. It might be impossible to develop them perfectly without great efforts, either by hand, or by automatic differentiation tools. In order to break these limitations, a new data assimilation system, dual-number data assimilation system(DNDAS), is designed based on the dual-number automatic differentiation principles. We investigate the performance of DNDAS with two different optimization schemes and subsequently give a discussion on whether DNDAS is appropriate for high-dimensional forecast models. The new data assimilation system can avoid the complicated reverse integration of the adjoint model, and it only needs the forward integration in the dual-number space to obtain the cost function and its gradient vector concurrently. To verify the correctness and effectiveness of DNDAS, we implemented DNDAS on a simple ordinary differential model and the Lorenz-63 model with different optimization methods. We then concentrate on the adaptability of DNDAS to the Lorenz-96 model with high-dimensional state variables. The results indicate that whether the system is simple or nonlinear, DNDAS can accurately reconstruct the initial condition for the forecast model and has a strong anti-noise characteristic. Given adequate computing resource, the quasi-Newton optimization method performs better than the conjugate gradient method in DNDAS.