THE SENSITIVITY INFLUENCE OF NUMERICAL MODEL INITIAL VALUES ON FOUR-DIMENSIONAL ASSIMILATION—STUDY BASED ON LORENZ SYSTEM

Numerical experiments of adjoint variational assimilation have been performed using the knownLorenz system.With the increase of sensitivity of model's initial values,it is more and more difficultto use the adjoint method to get the initial values which are consistent with the dynamics of the fore-cast model.Under some circumstances the algorithm completely fails.This shows that four-dimen-sional assimilation is related to the limit of predictability.On the other hand.with the increase ofmodel equation's error,the result of variational assimilation may become worse and worse so that theprediction has no meaning.But if the model parameters are corrected when variational assimilation ismade,the forecast results can be greatly improved based on Lorenz model.     

数值模式初值的敏感性程度对四维同化的影响

用著名的Ｌｏｒｅｎｚ系统作了共轭变分同化的数值试验。发现随着模式对初值敏感性程度的增加，用这种方法得到和模式相协调的初始场愈来愈困难，直到某些情况下的完全失败。这表明四维同化和可预报期限是联系在一起的。另一方面，随着方程不精确程度的增加，变分同化的效果愈来愈差，直到所做的预报无任何意义可言。如果在做变分同化的同时对模式参数也进行反演，就可使得基于Ｌｏｒｅｎｚ系统所做的预报效果大大提高。     

变分同化方法在Lorenz系统中的简单应用研究

利用Lorenz模式作变分同化数值试验，通过对一个简单系统的讨论，介绍四维变分同化方法。对初值敏感性和观测点的个数及观测值作了对比试验，发现随着模式对初值敏感性的增加，同化效果会越来越差；观测点越少，观测值误差越大，这些都会影响同化效果，甚至导致同化失败。     

用一种新的同化方法同化降水量资料

Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard four- dimensional variational data assimilation at a much lower computational cost.     

FOUR-DIMENSIONAL DATA QUALITY CONTROL THROUGH THE ADJOINT APPROACH

Using the variational four-dimensional (4-D) data assimilation technique (the adjointmethod), we defined the error/mean-error ratios (EMER) of observational data. The plausibilityof marking the gross error using the EMER is investigated theoretically under some assumptions.Idealized numerical experiments are carried out using the simple Lorenz model to test the qualitycontrol scheme.     

FOUR-DIMENSIONAL DATA QUALITY CONTROL THROUGH THE ADJOINT APPROACH*

Using the variational four-dimensional (4-D) data assimilation technique (the adjoint method), we defined the error/mean-error ratios (EMER) of observational data. The plausibility of marking the gross error using the EMER is investigated theoretically under some assumptions.Idealized numerical experiments are carried out using the simple Lorenz model to test the quality control scheme.     

Variational data assimilation experiments of mei-yu front rainstorms in China

The numerical forecasts of mei-yu front rainstorms in China has been an important issue. The intensity and pattern of the frontal rainfall are greatly influenced by the initial fields of the numerical model. The 4-dimensional variational data assimilation technology (4DVAR) can effectively assimilate all kinds of observed data, including rainfall data at the observed stations, so that the initial fields and the precipitation forecast can both be greatly improved. The non-hydrostatic meso-scale model (MM5) and its adjoint model are used to study the development of the mei-yu front rainstorm from 1200 UTC 25 June to 0600 UTC 26 June 1999. By numerical simulation experiments and assimilation experiments, the T106 data and the observed 6-hour rainfall data are assimilated. The influences of many factors, such as the choice of the assimilated variables and the weighting coefficient, on the precipitation forecast results are studied. The numerical results show that 4DVAR is valuable and important to mei-yu front rainfall prediction.     

The REM Adjoint System and Its 4DVar Data Assimilation Experiments

The Regional Eta-coordinate Model(REM) has performed well in forecasting heavy rainfalls in China in recent years.A four-dimensional variational assimilation system(4DVar) is developed to improve the forecast skill of the REM.The tangent linear model and adjoint model codes are written according to thecode to coderule,and the establishment of the REM adjoint modeling system is introduced in detail in this paper.The tangent linear and adjoint models of the REM are validated against the observational data,...     

四维变分同化技术在优化Z-C预测模式初始场中的应用试验

文中使用四维变分同化技术将海温观测资料同化到Zebiak-Cane模式中,通过优化模式的初始场提高了模式的预报技巧.通过用理想场进行检验,说明所建立的同化伴随模式是正确的 .用文中建立的四维变分同化模式以1997年1月为初始场所做的预报结果与实况相比,结果较好.这对今后ENSO预报打下了良好的基础.     

伴随系统及非线性优化方法在REM模式可预报性研究中的实际个例应用

利用REM模式的伴随系统和非线性优化方法，通过三个实际天气个例，对REM模式的可预报性问题进行了研究。结果表明，REM模式在给定的实际应用中可接受的预报误差范围内，对三个天气个例都具有预报能力。对于个例一，利用现有的常规报文初始观测场，进行简单的插值处理（最优插值等），REM数值模式就可以得到比较满意的预报结果； 对于个例二和个例三，对现有的报文初始观测场进行处理（如四维变分资料同化）后，REM模式在给定的误差允许范围内，对这两个天气个例仍得到满意的预报。研究结果不仅对改进数值模式具有一定的指导意义，而且对如何改进数值模式的初值问题，特别是在中尺度天气预报中如何改进具有一定的参考价值。     

不同初始场条件对GRAPES模式数值预报的影响

利用中国新一代数值预报模式GRAPES(Global/Regional Assimilation and Prediction Enhanced System),分别使用T213 L31及NCEP FNL 6 h间隔的资料作为初始和边界条件,对2005年7月9—10日淮河流域一次致洪暴雨过程进行了个例试验,初步探讨了GRAPES模式的数值预报产品对不同初始场的敏感程度,以及三维变分同化对数值预报结果的可能影响。结果表明,T213和NCEP初始场中存在着差异较大的次天气尺度特征,并由此造成了此后GRAPES模式预报场的差异,且此差异不会随时间消失;同化对GRAPES模式积分结果的影响主要表现在最初的24 h内;模式对此次致洪暴雨过程的暴雨区分布、强度均有一定的模拟能力,但模拟的强暴雨区与实况仍存在着较大差异。由此可知,GRAPES模式的数值预报能力对不同的初始场和侧边界条件存在不同程度的依赖性,初始场的差异决定了模拟结果的差异。     

梅雨锋暴雨研究中的四维变分同化试验

作者利用PSU/NCAR的MM5数值预报模式及其伴随模式, 以中国1999年6月23日～24日的一次梅雨锋暴雨过程为个例, 根据气象要素与同化窗口之间的配置差异, 作了3组变分同化试验.试验结果表明: 4DVAR方法在提高梅雨锋暴雨的数值预报水平上具有重要的作用.     

REM模式伴随系统的建立及其四维变分资料同化初步试验

Regional-Eta-Coordinate-Model(REM)中尺度模式对中国区域性降水显示出公认的较高预报能力,建立其四维变分资料同化系统是完善该模式,进一步提高其预报效果的重要工作。本研究编写了REM模式的切线性模式和伴随模式,介绍了建立REM模式伴随系统的过程,并利用实际天气个例资料,分别对REM模式的切线性模式、伴随模式及定义的目标函数梯度进行了正确性检验,检验结果表明对REM模式的切线性模式及伴随模式编写是成功的。利用REM模式的伴随系统,对1998年06月08日00时到09日00时和2000年08月01日00时到02日00时两个实际天气个例进行了四维变分资料同化试验。从数值试验的结果分析可以看到,进行四维变分资料同化后,两个天气个例在预报结束时刻其预报结果对风场和湿度场的预报都有明显改善,对温度场和高度场的预报也有所改善。对于累积降水的预报,两个个例利用四维变分资料同化后得到的初始场进行的预报结果则有较大不同,在个例1中,变分同化后对降水中心的位置和降水强度的预报都有明显改善,预报结果更接近于观测场;个例2中,变分同化后对降水中心位置和强度的预报则没有改善,产生这种现象的原因可能是由于定义的目标函数中没有加进背景场项,也可能是由于采用的观测资料时次比较少,还需要进一步进行研究和试验。     

Numerical experiments for Typhoon Dan incorporating AMSU-A retrieved data with 3DVM

Two sets of assimilation experiments on a landfalling typhoon—Typhoon Dan(1999)over the western North Pacific were designed to compare the performances of two kinds of variational data assimilation schemes that are the 3-Dimensional Variational data assimilation of Mapped observation(3DVM)and the 4-dimensional variational data assimilation(4DVar).Results show that:(1)both the 3DVM and 4DVar successfully improved the simulations of typhoon intensity and track incorporating the satellite AMSU-A retrieved temperature and wind data into the initial conditions,and the 3DVM more significantly due to the flow-dependent of background error covariance matrix and observation error covariance matrix like 3-dimensional variational data assimilation(3DVar)circle;(2)inclusions of extra model integration iterations at each observation time in the 3DVM make it more consistent with prediction model;(3)the 3DVM is much more time-saving due to the exclusion of the adjoint technique in it.     

中尺度数值模式的资料同化系统——(一)伴随模式的设计

以一个有限区域的中尺度模式为基础,采用伴随模式技术进行有限区域气象资料的同化。伴随模式的方法是以数值天气预报的动力模式作为约束条件的变分方法,比传统的变分方法有了很大的改进。本文初步探讨了伴随模式系统的设计方法,特别是伴随模式的构造问题;用共轭码的方法导出伴随模式;初步试验表明该系统有较强的同化能力。     

中尺度数值模式的资料同化系统：（一）伴随模式的设计

以一个有限区域的中尺度模式为基础,采用伴随模式技术进行有限区域气象资料的同化。伴随模式的方法是以数值天气预报的动力模式作为约束条件的变分方法,比传统的变分方法有了很大的改进。本文初步探讨了伴随模式系统的设计方法,特别是伴随模式的构造问题;用共轭码地方法导出伴随模式;初步试验表明该系统有较强的同化能力。     

GRAPES伴随模式底层数据栈优化

GRAPES伴随模式是其四维变分同化系统的核心组成部分。由于其计算过程复杂,临时数据较多,实现中采用断点存储策略可以有效减少伴随模式的计算时间和存储空间。极限断点存储策略是在单积分步内以全存储策略实现为基础,将其中部分基态以计算代替的一种类断点存储策略。在该策略的支持下,需要一种新的数据管理结构,来保证程序的正确运行。文章提出了在已有栈基础上优化的新数据存储管理方式——嵌套多链栈,这种结构可有效满足使用极限断点存储技术实现GRAPES伴随模式的初态管理需求。试验表明:相比断点存储技术,在总内存增加不超过30%的情况下可使GRAPES的运行效率提高1倍。     

UKF滤波算法在数据同化中的应用

介绍了一种新的数据同化算法（UKF,Unscented Kalman Filter）,该算法不需要计算伴随矩阵,就能够解决模式的非线性问题。以Lorenz系统为例,进行了数据同化的数值试验。结果表明：基于UKF的同化方案与背景场的初始值无关,它能有效地抑制状态变量误差的增长,同化结果精度高。     

Variational Data Assimilation for a Lorenz Model Usinga Non-Standard Genetic Algorithm

Summary  Data assimilation in meteorology and oceanography for strongly nonlinear dynamical systems is challenging. The dynamical system studied here is the classical three-variable Lorenz model. In this context data assimilation with weak-constraint variational methods performs better than other methods like strong-constraint variational methods or Kalman filters. The difficulty in tracking the chaotic Lorenz orbit by assimilation of noisy observations results from the inherent instability in the system. In variational methods a cost function has to be minimized. It is known, that in the Lorenz case the structure of the cost function becomes more and more complex with increasing length of the assimilation time interval and with reduction of the observational data quality. This paper proposes a non-standard implementation of a genetic algorithm for searching the global minimum in case of a weak-constraint formulation. The good performance of this non-local search is shown, but the algorithm is computationally demanding due to a very large number of control parameters within the weak-constraint formulation and, thus, the algorithm is applicable for simple systems only. Received December 12, 1998 Revised May 11, 1999     

卫星云图反演资料质量控制方案在暴雨模拟中的对比研究

本文以2003年7月9～10日发生在江淮流域的暴雨过程为例,应用FY-2B卫星红外云图灰度资料,利用统计回归的反演方法求出高分辨率的温度和湿度的反演场用于改进预报的初始场。设计和对比了几种质量控制方案,再将控制后的反演场与常规资料进行同化,最后用MM5模式进行了模拟对比试验。结果表明：在模式的初始场中引入卫星云图反演场能反映出更为细致的中尺度结构,有效地增强了对降水有重要影响的高湿区;锋生函数质量控制方案改善模式的初始场,从而改善了MM5模式降水预报的落区和强度,提高了降水预报准确性及Ts评分。