排序方式： 共有15条查询结果，搜索用时 15 毫秒
1.
利用条件非线性最优扰动（conditional nonlinear optimal perturbation,CNOP）可以实现最大预报误差的上界估计。CNOP通常由基于梯度信息的约束优化算法进行求解,且其中的梯度信息由伴随模式提供。然而当非线性模式中含不连续＂开关＂时,传统伴随方法不能为优化过程提供正确的梯度方向,从而导致优化失败。为此,采用自适应变异和混合交叉的遗传算法,联赛选择机制和小生境技术的约束处理方法来求解最大预报误差上界。为检验新方法的有效性,以修改的Lorenz模型作为预报模式,对3个初始态分别用新方法和传统伴随方法进行比较,数值试验结果显示新方法求解出的最大预报误差的上界更加精确。 相似文献
2.
非线性滤波在含“开关”过程的资料同化中的应用研究 总被引：2，自引：0，他引：2
利用一个描述实际数值天气预报模式中比湿在单格线上随时间发展的偏微分方程作为控制方程,研究分析了非线性滤波方法在含开关过程的资料同化中的有效性和可行性。首先在贝叶斯理论框架下,讨论了一般情形的非线性滤波方法,然后对基于粒子滤波(PF)和基于集合卡尔曼滤波(EnKF)的两种同化方法进行对比,由于EnKF是通过对集合成员的统计分析得到的误差分布的一阶矩和二阶矩来近似真实误差分布的,所以当用高斯分布近似真实误差分布所产生的误差较大时,基于EnKF的同化方法得到的结果也会有较大的误差。最后分别从观测算子为线性和非线性、观测误差为高斯型和非高斯型4种情形进行数值试验,结果显示当观测误差为高斯型时,无论观测算子为线性还是非线性,基于PF和基于EnKF的同化方法都能克服由开关过程给资料同化带来的困难,给出满意的同化结果;而当观测误差为非高斯型时,EnKF出现滤波不稳定,产生了非理想的同化结果,但PF方法仍然能够有效地发挥作用,给出满意的同化结果。 相似文献
3.
粒子群优化算法(Particle Swarm Optimization,PSO)由于其编码简单,易于实施而发展迅速,PSO的性能取决于两个重要因素:惯性权重和学习因子.通过合理设计这两个参数,将动态权重的PSO应用于含有不连续“开关”过程的变分资料同化.为检验算法的有效性,针对一个简化的含不连续开关过程的偏微分方程,操作了3种比较同化数值试验,即基于传统伴随方法,遗传算法(GA)和动态权重的PSO的变分同化.结果显示,当控制方程含有开关时,使用PSO的变分同化结果的质量上明显优于其他两种方法,且PSO的性能更加稳定.对观测误差及模式误差的敏感性试验结果显示PSO方法具有更强的鲁棒性.PSO同化的效果与算法中参数的选取有关,采用好的参数设置能获得更好的同化结果. 相似文献
4.
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
5.
Some variational data assimilation (VDA) problems of time- and space-discrete models with
on/off parameterizations can be regarded as non-smooth optimization problems. Same as the sub-gradient
type method, intelligent optimization algorithms, which are widely used in engineering optimization,
can also be adopted in VDA in virtue of their no requirement of cost functions gradient (or sub-gradient)
and their capability of global convergence. Two typical intelligent optimization algorithms, genetic
algorithm (GA) and particle swarm optimization (PSO), are introduced to VDA of modified Lorenz equations
with on-off parameterizations, then two VDA schemes are proposed, that is, GA based VDA (GA-VDA) and PSO
based VDA (PSO-VDA). After revealing the advantage of GA and PSO over conventional adjoint methods in the
ability of global searching at the existence of cost functions discontinuity induced by on-off switches,
sensitivities of GA-VDA and PSO-VDA to population size, observational noise, model error and observational
density are detailedly analyzed.
Its shown that, in the context of modified Lorenz equations, with proper population size, GA-VDA and PSO-VDA
can effectively estimate the global optimal solution, while PSO-VDA consumes much less computational time than
GA-VDA with the same population size, and requires a much lower population size with nearly the same results,
both methods are not very sensitive to observation noise and model error, while PSO-VDA shows a better
performance with observational noise than GA-VDA. It is encouraging that both methods are not sensitive to
observational density, especially PSO-VDA, using which almost the same perfect assimilation results can be
obtained with comparatively sparse observations. 相似文献
6.
采用实验生态学方法研究了除草剂百草枯对褶皱臂尾轮虫(Brachionus plicatilis)的摄食和种群动态的影响。结果表明,百草枯对褶皱臂尾轮虫的24hLC50为9.17mg/L,对褶皱臂尾轮虫的摄食具有明显的抑制作用。实验浓度下褶皱臂尾轮虫对小球藻的滤水率和摄食率随百草枯浓度的增加先下降后升高。百草枯对褶皱臂尾轮虫种群动态影响显著,使轮虫的胚胎发育时间、繁殖前期和繁殖后期延长,繁殖期和平均寿命缩短,产卵量和繁殖率降低。轮虫的净生殖率和世代时间均低于对照,不同百草枯浓度下轮虫能够维持一定的种群增长。 相似文献
7.
数值模式误差对降水四维变分资料同化及预报的影响 总被引：3，自引：1，他引：2
利用一个无量纲的水汽发展方程,针对同化时间窗口内出现和不出现降水两种情况,分析了不同模式误差和初始误差对降水四维变分资料同化预报效果的影响。结果表明,应用四维变分资料同化方法进行降水预报前,应该充分考虑数值模式中的误差,才能得到比较满意的同化及预报结果。假定同化窗口内获得的比湿观测场是准确的,当不存在模式误差时,四维变分资料同化方法可以有效地消除初始场误差,找到比湿真实初始场;而存在模式误差时,四维变分资料同化后的模式初始场会偏离真实的比湿初始场,并且模式误差越大,偏离程度越严重。在一些模式误差情况下,由于模式误差在同化窗口及延伸预报时段的作用不同,进行四维变分资料同化处理后,尽管累积降水量的预报结果在同化时间窗口内优于同化前的预报,而在最终预报时刻反而差于同化前。 相似文献
8.
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
9.
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
10.
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