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1.
在基于条件非线性最优扰动(CNOP)的台风适应性观测研究中,针对预报模式的湿物理参数化产生的“on-off”开关导致传统伴随方法不能为最优化过程提供正确梯度这一现象,将模式含有“on-off”开关时求解CNOP的问题视为非光滑最优化问题,引入遗传算法,在给出详细的算法流程后,以一个在强迫项中含“on-off”开关的理想模式,分析了“on-off”开关对求解CNOP的影响,三个数值试验检验了模式含有“on-off”开关时遗传算法求解CNOP的有效性,并分析了不同初始种群对最优化结果的影响。结果显示,所采用的含有“on-off”开关的理想模式下,遗传算法能有效求解CNOP,最后对遗传算法求解CNOP的优缺点进行了详细讨论。 相似文献
2.
求解条件非线性最优扰动(Conditional Nonlinear Optimal Perturbation,CNOP)属约束最优化问题,一般采用基于伴随模式提供梯度信息的约束优化算法(简称ADJ)进行求解。当优化问题涉及不连续的"开关"过程时,传统优化算法的寻优能力会受到较大的影响。近年来遗传算法(Genetic Algorithm,GA)因其在非光滑优化问题中的鲁棒性备受关注,但GA的性能不仅与优化问题有关,还取决于遗传算子的配置。本文将一种新的约束GA(GA1)用于求解CNOP,并对GA1,ADJ及具有不同遗传算子配置的约束GA(GA2)求解含"开关"过程的CNOP时的性能进行了比较。数值试验结果显示,GA1和GA2的全局寻优能力明显优于ADJ,后者易于陷入局部最优;对于不同的初猜值(不同的初始种群),GA1求解的CNOP能够保持一个较为一致的空间结构,ADJ求解的CNOP呈现了明显的两种结构,一种代表的是全局CNOP,一种是局部CNOP。通过验证不同遗传策略对优化结果的影响发现,对不同的优化问题,采用合适的遗传策略以及合适的参数设置是获取更好优化结果的一种有效途径。 相似文献
3.
综述用非线性优化方法研究厄尔尼诺(El Ni(n)o)-南方涛动(ENSO)事件可预报性的进展.针对ENSO可预报性研究中的热点问题--"前期征兆"、"春季可预报性障碍",以及如何量化研究ENSO可预报性和ENSO的不对称性问题,作者在近年来的工作中先后用理论模式和中等复杂程度ENSO模式研究了ENSO可预报性的动力学,揭示了ENSO的若干重要非线性特征.主要结果如下:(1)条件非线性最优扰动(CNOP)(局部CNOP)比线性奇异向量更易发展成ENSO事件,扮演了ENSO的最优前期征兆.这些ENSO事件关于气候平均态是不对称的.理论分析表明,非线性温度平流过程是造成这种不对称性的重要原因.1980～2002年的海洋再分析资料验证了上述理论结果.(2)ENSO事件CNOP型初始误差的发展有明显的季节依赖性,该误差导致了ENSO事件最显著的春季可预报性障碍(SPB)现象.ENSO事件SPB的发生不仅依赖于气候平均态,而且依赖于ENSO事件本身及其初始误差模态,是三者综合作用的结果.(3)建立了关于ENSO可预报性的最大可预报时间下界、最大预报误差上界和最大允许初始误差下界的三类可预报性问题,分别从三个方面揭示了ENSO事件的春季可预报性障碍现象,比较有效地量化了其可预报性.(4)通过CNOP方法,揭示了非线性温度平流在年代际尺度ENSO不对称性研究中的重要作用,解释了ENSO不对称性的年代际变化,基于所用ENSO模式给出了ENSO不对称性年代际变化的机制.最后,展望了非线性优化方法在ENSO可预报性中应用的前景,并期望该方法能拓展到ENSO第二类可预报性问题的研究中. 相似文献
4.
综述用非线性优化方法研究厄尔尼诺(El Ni~no)南方涛动(ENSO)事件可预报性的进展。针对ENSO可预报性研究中的热点问题———“前期征兆”、“春季可预报性障碍”,以及如何量化研究ENSO可预报性和ENSO的不对称性问题,作者在近年来的工作中先后用理论模式和中等复杂程度ENSO模式研究了ENSO可预报性的动力学,揭示了ENSO的若干重要非线性特征。主要结果如下:(1)条件非线性最优扰动(CNOP)(局部CNOP)比线性奇异向量更易发展成ENSO事件,扮演了ENSO的最优前期征兆。这些ENSO事件关于气候平均态是不对称的。理论分析表明,非线性温度平流过程是造成这种不对称性的重要原因。1980~2002年的海洋再分析资料验证了上述理论结果。(2)ENSO事件CNOP型初始误差的发展有明显的季节依赖性,该误差导致了ENSO事件最显著的春季可预报性障碍(SPB)现象。ENSO事件SPB的发生不仅依赖于气候平均态,而且依赖于ENSO事件本身及其初始误差模态,是三者综合作用的结果。(3)建立了关于ENSO可预报性的最大可预报时间下界、最大预报误差上界和最大允许初始误差下界的三类可预报性问题,分别从三个方面揭示了ENSO事件的春季可预报性障碍现象,比较有效地量化了其可预报性。(4)通过CNOP方法,揭示了非线性温度平流在年代际尺度ENSO不对称性研究中的重要作用,解释了ENSO不对称性的年代际变化,基于所用ENSO模式给出了ENSO不对称性年代际变化的机制。最后,展望了非线性优化方法在ENSO可预报性中应用的前景,并期望该方法能拓展到ENSO第二类可预报性问题的研究中。 相似文献
5.
用非线性最优化方法研究El Niño可预报性的进展与前瞻 总被引：2，自引：4，他引：2
综述用非线性优化方法研究厄尔尼诺(El Ni(n)o)-南方涛动(ENSO)事件可预报性的进展.针对ENSO可预报性研究中的热点问题--"前期征兆"、"春季可预报性障碍",以及如何量化研究ENSO可预报性和ENSO的不对称性问题,作者在近年来的工作中先后用理论模式和中等复杂程度ENSO模式研究了ENSO可预报性的动力学,揭示了ENSO的若干重要非线性特征.主要结果如下:(1)条件非线性最优扰动(CNOP)(局部CNOP)比线性奇异向量更易发展成ENSO事件,扮演了ENSO的最优前期征兆.这些ENSO事件关于气候平均态是不对称的.理论分析表明,非线性温度平流过程是造成这种不对称性的重要原因.1980～2002年的海洋再分析资料验证了上述理论结果.(2)ENSO事件CNOP型初始误差的发展有明显的季节依赖性,该误差导致了ENSO事件最显著的春季可预报性障碍(SPB)现象.ENSO事件SPB的发生不仅依赖于气候平均态,而且依赖于ENSO事件本身及其初始误差模态,是三者综合作用的结果.(3)建立了关于ENSO可预报性的最大可预报时间下界、最大预报误差上界和最大允许初始误差下界的三类可预报性问题,分别从三个方面揭示了ENSO事件的春季可预报性障碍现象,比较有效地量化了其可预报性.(4)通过CNOP方法,揭示了非线性温度平流在年代际尺度ENSO不对称性研究中的重要作用,解释了ENSO不对称性的年代际变化,基于所用ENSO模式给出了ENSO不对称性年代际变化的机制.最后,展望了非线性优化方法在ENSO可预报性中应用的前景,并期望该方法能拓展到ENSO第二类可预报性问题的研究中. 相似文献
6.
一种求解条件非线性最优扰动的快速算法及其在台风目标观测中的初步检验 总被引：2，自引：0，他引：2
条件非线性最优扰动(CNOP)是Mu等2003年提出的一个新的理论方法,它是线性奇异向量在非线性情形的推广,克服了线性奇异向量不能代表非线性系统最快发展扰动的缺陷,成为非线性系统可预报性和敏感性等研究新的有效工具.然而,由于以往CNOP的求解需要采用伴随技术,计算量相当巨大,限制了该方法的推广应用.为了克服这一困难,本文基于经验正交分解(EOF),提出了一种求解CNOP的快速算法,利用GRAPES区域业务预报模式实现了CNOP快速计算,并在台风"麦莎"的目标观测研究中得到初步检验,通过观测系统模拟实验(OSSE)检验了该方法确定敏感性区域(瞄准区)的有效性和可行性.试验结果表明,用快速算法求解的CNOP,其净能量随时间快速地发展,而且发展呈非线性.在台风"麦莎"个例的目标观测试验中,用快速算法得到的预报时间为24 h的CNOP可以有效地识别瞄准区,并通过瞄准区内初值的改善,可明显减少目标区域(检验区)内24 h累计降水预报误差.尤其,累计降水预报的这种改进效果能够延伸到更长时间(如72 h),尽管检验时间是设在第24小时.进一步分析发现,24 h累计降水预报误差的减少是通过利用瞄准区内改善的初值改进初始时刻台风暖心结构、高空相对涡度以及水汽条件等而得以实现的. 相似文献
7.
通过在Zebiak Cane数值模式中引入参数化MJO随机外强迫,着重从Nio 3指数的演变发展探讨了MJO不确定性对ENSO可预报性的影响。结果表明,对Zebiak Cane模式而言,MJO不确定性对由条件非线性最优扰动(CNOP)导致的ENSO事件最大预报误差影响较小;与初始误差相比,由MJO不确定性产生的模式误差在ENSO预报不确定性的产生中具有较小作用,对ENSO可预报性的影响不显著。该结果强调了初始误差在ENSO预报不确定性中的主要作用,从而为ENSO预测的资料同化提供了理论基础。 相似文献
8.
《气象科学进展》2016,(6)
介绍了条件非线性最优扰动(Conditional Nonlinear Optimal Perturbation,CNOP)的定义及其在大气和海洋等可预报性研究中的应用。根据研究对象不同,CNOP分为与初始扰动有关的CNOP(CNOP-I)方法、与模式参数扰动有关的CNOP(CNOP-P)方法和同时考虑初始扰动和模式参数扰动的CNOP方法。目前,CNOP-I方法已经应用于ENSO、黑潮和阻塞可预报性以及热盐环流和草原生态系统稳定性的研究。此外,CNOP-I方法也被应用于探讨台风目标观测的研究,利用CNOP-I方法能够识别出台风预报的初值敏感区,通过观测系统模拟试验表明在初值敏感区增加观测能够有效改进台风的预报技巧。CNOP-P方法也在ENSO和黑潮可预报性以及热盐环流和草原生态系统稳定性研究中得到了应用。为了将CNOP方法应用于更多的领域,本文利用一个简单的Burgers方程,介绍了如何通过建立Burgers方程的切线性模式和伴随模式,从而利用非线性最优化算法计算获得CNOP。这一数值试验为将CNOP方法应用于更多的领域提供了借鉴。 相似文献
9.
用Zebiak-Cane模式和季节内振荡(Madden-Julian Oscillation,MJO)的参数化表述以及条件非线性最优扰动(Conditional Nonlinear Optimal Perturbation,CNOP)方法,分析了以ENSO事件为基态的CNOP型初始误差的空间结构增长规律。结果表明,参数化的MJO对CNOP型初始误差的发展影响较小,其影响主要是使中东太平洋的海表面温度异常增大。CNOP型初始误差比由MJO不确定性产生的模式误差的影响大,前者可能是造成ENSO事件预报不确定性的主要误差来源。由于CNOP型初始误差的局地性,本结论可用来指导ENSO的目标观测和适应性资料同化。 相似文献
10.
通过在Zebiak Cane数值模式中引入参数化MJO随机外强迫,着重从Nio 3指数的演变发展探讨了MJO不确定性对ENSO可预报性的影响。结果表明,对Zebiak Cane模式而言,MJO不确定性对由条件非线性最优扰动(CNOP)导致的ENSO事件最大预报误差影响较小;与初始误差相比,由MJO不确定性产生的模式误差在ENSO预报不确定性的产生中具有较小作用,对ENSO可预报性的影响不显著。该结果强调了初始误差在ENSO预报不确定性中的主要作用,从而为ENSO预测的资料同化提供了理论基础。 相似文献
11.
In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the on-off switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedur... 相似文献
12.
On the Application of a Genetic Algorithm to the Predictability Problems Involving "On-Off" Switches 总被引：2，自引：0，他引：2
The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation (CNOP) method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on-off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm (GA) to the CNOP method, named GA-CNOP, to solve the predictability problems involving on-off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on-off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured. 相似文献
13.
The conditional nonlinear optimal perturbation (CNOP), which is a nonlinear generalization
of the linear singular vector (LSV), is applied in important problems of atmospheric and oceanic
sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study,
we investigate the computational cost of obtaining the CNOP by several methods. Differences and
similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among
the sequential quadratic programming (SQP) algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno
(L-BFGS) algorithm, and the spectral projected gradients (SPG2) algorithm. A theoretical grassland
ecosystem model and the classical Lorenz model are used as examples.
Numerical results demonstrate that the computational error is acceptable with all three algorithms.
The computational cost to obtain the CNOP is reduced by using the SQP algorithm. The experimental
results also reveal that the L-BFGS algorithm is the most effective algorithm among the three
optimization algorithms for obtaining the CNOP. The numerical results suggest a new approach and
algorithm for obtaining the CNOP for a large-scale optimization problem. 相似文献
14.
Some intelligent algorithms (IAs) proposed by us, including swarm IAs and single individual IAs, have been applied to the Zebiak-Cane (ZC) model to solve conditional nonlinear optimal perturbation (CNOP) for studying El Ni?o – Southern Oscillation (ENSO) predictability. Compared to the adjoint-based method (the ADJ-method), which is referred to as a benchmark, these IAs can achieve approximate CNOP results in terms of magnitudes and patterns. Using IAs to solve CNOP can avoid the use of an adjoint model and widen the application of CNOP in numerical climate and weather modeling. Of the proposed swarm IAs, PCA-based particle swarm optimization (PPSO) obtains CNOPs with the best patterns and the best stability. Of the proposed single individual IAs, continuous tabu search algorithm with sine maps and staged strategy (CTS-SS) has the highest efficiency. In this paper, we compare the validity, stability and efficiency of parallel PPSO and CTS-SS using these two IAs to solve CNOP in the ZC model for studying ENSO predictability. The experimental results show that CTS-SS outperforms parallel PPSO except with respect to stability. At the same time, we are also concerned with whether these two IAs can effectively solve CNOP when applied to more complicated models. Taking the sensitive areas identification of tropical cyclone adaptive observations as an example and using the fifth-generation mesoscale model (MM5), we design some experiments. The experimental results demonstrate that each of these two IAs can effectively solve CNOP and that parallel PPSO has a higher efficiency than CTS-SS. We also provide some suggestions on how to choose a suitable IA to solve CNOP for different models. 相似文献
15.
A Preliminary Application of the Differential Evolution Algorithm to Calculate the CNOP 总被引：1，自引：0，他引：1 
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下载免费PDF全文 A projected skill is adopted by use of the differential evolution （DE） algorithm to calculate a conditional nonlinear optimal perturbation （CNOP）. The CNOP is the maximal value of a constrained optimization problem with a constraint condition, such as a ball constraint. The success of the DE algorithm lies in its ability to handle a non-differentiable and nonlinear cost function. In this study, the DE algorithm and the traditional optimization algorithms used to obtain the CNOPs are compared by analyzing a theoretical grassland ecosystem model and a dynamic global vegetation model. This study shows that the CNOPs generated by the DE algorithm are similar to those by the sequential quadratic programming （SQP） algorithm and the spectral projected gradients （SPG2） algorithm. If the cost function is non-differentiable, the CNOPs could also be caught with the DE algorithm. The numerical results suggest the DE algorithm can be employed to calculate the CNOP, especially when the cost function is non-differentiable. 相似文献
16.
This paper proposes a hybrid method, called CNOP–4 DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation(CNOP) and four-dimensional variational assimilation(4 DVar) methods. The proposed CNOP–4 DVar method is capable of capturing the most sensitive initial perturbation(IP), which causes the greatest perturbation growth at the time of verification; it can also identify sensitive areas by evaluating their assimilation effects for eliminating the most sensitive IP. To alleviate the dependence of the CNOP–4 DVar method on the adjoint model, which is inherited from the adjoint-based approach, we utilized two adjointfree methods, NLS-CNOP and NLS-4 DVar, to solve the CNOP and 4 DVar sub-problems, respectively. A comprehensive performance evaluation for the proposed CNOP–4 DVar method and its comparison with the CNOP and CNOP–ensemble transform Kalman filter(ETKF) methods based on 10 000 observing system simulation experiments on the shallow-water equation model are also provided. The experimental results show that the proposed CNOP–4 DVar method performs better than the CNOP–ETKF method and substantially better than the CNOP method. 相似文献
17.
奇异向量(singular vectors,SVs)和条件非线性最优扰动(conditional nonlinear optimal perturbation,CNOP)已广泛应用于研究大气—海洋系统的不稳定性以及与其相关的可预报性、集合预报和目标观测问题研究。本文首先回顾了SVs和CNOP的发展历史,并简单描述了它们的基本原理;然后针对二维正压准地转模式,使用不同的范数组合,分析了第一线性奇异向量(first singular vector,FSV)和CNOP之间的异同。结果表明,当优化时间较短时,度量SVs和CNOP大小的范数不同也将导致FSV和CNOP相差很大,而当度量SVs和CNOP大小的范数相同时,FSV和CNOP之间的差别则主要是由非线性物理过程作用的结果。因此,针对不同的物理问题,应该选取合适的度量范数研究FSV和CNOP以及其所引起的大气或海洋动力学的异同,从而揭示非线性物理过程的影响机理。 相似文献
18.
Application of a Derivative-Free Method with Projection Skill to Solve an Optimization Problem 下载免费PDF全文
下载免费PDF全文 Improving numerical forecasting skill in the atmospheric and oceanic sciences by solving optimization problems is an important issue. One such method is to compute the conditional nonlinear optimal perturbation（CNOP）, which has been applied widely in predictability studies. In this study, the Differential Evolution（DE） algorithm, which is a derivative-free algorithm and has been applied to obtain CNOPs for exploring the uncertainty of terrestrial ecosystem processes, was employed to obtain the CNOPs for finite-dimensional optimization problems with ball constraint conditions using Burgers＇ equation. The aim was first to test if the CNOP calculated by the DE algorithm is similar to that computed by traditional optimization algorithms, such as the Spectral Projected Gradient（SPG2） algorithm. The second motive was to supply a possible route through which the CNOP approach can be applied in predictability studies in the atmospheric and oceanic sciences without obtaining a model adjoint system, or for optimization problems with non-differentiable cost functions. A projection skill was first explanted to the DE algorithm to calculate the CNOPs. To validate the algorithm, the SPG2 algorithm was also applied to obtain the CNOPs for the same optimization problems. The results showed that the CNOPs obtained by the DE algorithm were nearly the same as those obtained by the SPG2 algorithm in terms of their spatial distributions and nonlinear evolutions. The implication is that the DE algorithm could be employed to calculate the optimal values of optimization problems, especially for non-differentiable and nonlinear optimization problems associated with the atmospheric and oceanic sciences. 相似文献
19.
In this paper,a nonlinear optimization method is used to explore the finite-time instability of the atmospheric circulation with a three-level quasigeostrophic model under the framework of the conditional nonlinear optimal perturbation (CNOP).As a natural generalization of linear singular vector (SV),CNOP is defined as an initial perturbation that makes the cost function the maximum at a prescribed forecast time under certain physical constraint conditions.Special attentions are paid to the different structures and energy evolutions of the optimal perturbations.The results show that the most instable region of the global atmospheric circulation lies in the midlatitude Eurasian continent.More specially,SV and CNOP in the total energy norm with an optimization time of 2 days both present localness:they are mainly located in the midlatitude Asian continent and its east coast.With extension of the optimization time,SVs are more upstream and less localized in the zonal direction,and CNOPs differ essentially from SVs with broader zonal and meridional coverages; as a result,CNOPs acquire larger kinetic and available potential energy amplifications than SVs in the nonlinear model at the corresponding optimization time.For the climatological wintertime flow,it is seen that the baroclinic terms remain small over the entire time evolution,and the energy production comes essentially from the eddy kinetic energy,which is induced by the horizontal shear of the basic flow.In addition,the effects of SVs and CNOPs on the Eurasian atmospheric circulation are explored.The results show that the weather systems over the Eurasian continent in the perturbed fields by CNOPs are stronger than those by SVs at the optimization time.This reveals that the CNOP method is better in evaluating the instability of the atmospheric circulation while the SV method underestimates the possibility of extreme weather events. 相似文献
20.
In this paper, a nonlinear optimization method is used to explore the finite-time instability of the atmospheric circulation
with a three-level quasigeostrophic model under the framework of the conditional nonlinear optimal perturbation (CNOP). As
a natural generalization of linear singular vector (SV), CNOP is defined as an initial perturbation that makes the cost function
the maximum at a prescribed forecast time under certain physical constraint conditions. Special attentions are paid to the
different structures and energy evolutions of the optimal perturbations. 相似文献