共查询到19条相似文献，搜索用时 109 毫秒
1.
应用一个最大简化的６变量非线性模式,引入胞映射全局分析方法,研究初值误差范围内不同扰动对预报结果的影响。在获得对其全局认识的基础上,再通过理论研究与预报实践证明,集合预报方法确能改进预报。理论研究表明,可以求出最佳集合样本数,而且由模式预报试验得到的最佳集合样本数与理论结果是一致的。另外,观测精度、预报精度以及模式状态变量的个数对最佳集合样本数有着不同的影响。 相似文献
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天气可预报性的时空分布 总被引：10，自引：1，他引：9
为了能从非线件误差增长动力学的角度研究大气的可预报性问题,文章引入了可预报性研究的新方法--非线性局部Lyapunov指数.非线性局部Lyapunov指数及其相关统计量能够被用来定量地确定混沌系统可预报性的大小,真正地实现对可预报性的定量化研究.为了把非线性局部Lyapunov指数方法应用到实际的大气可预报性研究中,给出了一种利用大气的实际观测资料估计非线性局部Lyapunov指数的计算方法.存非线性局部Lyapunov指数方法的基础上,文中利用NCEP/NCAR再分析资料,对大气位势高度场、温度场、纬向风场、经向风场等要素场可预报性的时空分布进行了研究,结果表明:(1)在500 hPa高度层上,对于不同的要素场,其可预报期限的大小以及时空分布规律都不一样;全球大部分地区位势高度场可预报期限最大,温度场和纬向风场次之,而经向风场的可预报期限最小.(2)在500 hPa高度层七,位势高度场和温度场的纬向平均可预报期限基本上表现为一定的南北纬向带状分布,热带地区和南极地区的可预报期限最大,北极地区次之,南北半球中高纬度地区可预报期限相对较小.纬向风场可预报期限在热带地区最高,但是南北极地区可预报期限与邻近的中高纬度地区差别不大.经向风场可预报期限在南北两极地区最高,南北半球的中纬度和赤道附近地区可预报期限最小.(3)在垂直方向上,纬向平均高度场、温度场以及纬向风场可预报期限基本上都是随高度升高而增加,高层的可预报期限明显大于低层;经向风场可预报期限随高度的变化比较复杂,不同的纬度有所不同.(4)可预报性有明显的季节变化,不同要素场可预报期限高低值区的位置和强度随季节鄙有明显变化,对于全球大部分地区来说,冬季可预报性都大于夏季的. 相似文献
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初值信息在气候预测中的作用 总被引：1，自引：0，他引：1
应用胞映射全局分析方法,研究初值信息在气候预测中的作用。针对人们认识气候的实际过程,分别提出4种初始条件,研究一个最大简化的大气模式做逐日级别预报和量值预报以及月平均级别预报和量值预报的能力。结果表明,初值信息掌握得越多,预报误差就越小,预报的相对准确率就越高。 相似文献
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根据非线性局部Lyapunov指数的方法, 以Logistic映射和Lorenz系统的试验数据序列为例, 研究了在初始误差存在的情况下, 随机误差对混沌系统可预报性的影响。结果表明: 初始误差和随机误差对可预报期限影响所起的作用大小主要取决于两者的相对大小。当初始误差远大于随机误差时, 系统的可预报期限主要由初始误差决定, 可以不考虑随机误差对预报模式可预报性的影响; 反之, 当随机误差远大于初始误差时, 系统的可预报期限主要由随机误差决定; 当初始误差和随机误差量级相当时, 两者都对系统的可预报期限起重要作用。在后两种情况下, 在考虑初始误差对可预报性影响的同时还必须考虑随机误差的作用。此外, 我们在已知系统精确的控制方程和误差演化方程的条件下, 研究了随机误差对可预报性的影响, 理论所得结果与试验数据所得结果相似。这表明在随机误差较小的情况下, 对系统可预报期限的估计相对准确, 但在随机误差较大的情况下, 可预报期限的估计误差也较大。本文利用三种不同的滤波方法对序列进行了试验, 结果表明, Lanczos高通滤波得到的高频序列与原始加入的噪声序列无论是在强度上还是在演变趋势上都表现得相当一致, 其能有效地去除高频噪音继而提高对系统的可预报期限的估计, 这对实际气象观测资料如何有效地去除噪音具有一定的启发意义。 相似文献
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气候系统的非线性特征及其预测理论 总被引：1，自引：0，他引：1
本文针对气候问题的特殊性,对气候的定义、气候系统的非线性特征、演变机制、可预报性问题以及气候状态的预报方法等做了较全面的研究,这些内容初步形成了气候系统的准动力—准随机理论,是气候动力学的新发展。 相似文献
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本文介绍了信息理论在气候可预报性方面的应用,特别用到了熵和转移信息的概念。通过一个简单的随机模式,说明它们在气候系统的可预报性和气候系统预报方面的应用。同时也讨论了其他方面的应用性。 相似文献
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利用全国518个站1960—2011年逐日气温观测资料和160个站1983—2012年月尺度气温客观预测数据,基于非线性局部Lyapunov指数和非线性误差增长理论,研究中国区域月尺度气温可预报性期限对资料序列长度的依赖性。结果表明:气温可预报性期限对资料序列的长度有一定程度的依赖性,在西北、东北及华中地区尤为明显。平均而言,45年的资料序列长度才能够得到稳定合理的可预报性期限。为了验证气温可预报期限计算结果的可信度,将月尺度气温的可预报性期限与客观气候预测方法的预报评分技巧进行对比,发现两者结果非常一致。其中,由观测资料得到的1月气温的可预报性期限明显低于7月,1月客观气候预测方法的预报评分技巧也明显低于7月,且1月 (7月) 预报评分的空间分布型与1月 (7月) 气温可预报性期限的空间分布型较为一致。因此,利用非线性局部Lyapunov指数和台站逐日观测资料分析气温的可预报性期限结果是可信的。 相似文献
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非线性误差增长理论在大气可预报性中的应用 总被引：10，自引：1，他引：9
为了能从非线性误差增长动力学的角度来研究大气的可预报性问题,在非线性动力系统的理论和方法基础上,文中引入了可预报性研究的新方法--非线性局部Lyapunov指数.非线性局部Lyapunov指数及其相关统计量能够用来定量地确定混沌系统可预报性的大小,真正地实现了对可预报性的定量化研究.首先给出了利用大气单个变量的实际观测资料获得其可预报期限估计的计算方法,因而解决了将非线性误差增长理论应用到大气实际的可预报性研究中的问题.然后,以位势高度场为例,详细讨论了逐日时间尺度上全球可预报性的时空分布,得到的主要结论为:(1)在水平方向上,全球位势高度场可预报性表现为一定的南北纬向带状分布,赤道地区和南极地区的可预报期限最长,可以达到两周左右;北极地区次之,可预报期限大约为9-12 d;北半球中高纬度地区可预报期限相对较短,可预报期限大约为6-9 d;而在南半球的中纬度地区最短,可预报期限仅为4-6 d.此外,500 hPa位势高度场可预报性分市随季节有明显变化,季节不同一些可预报期限的高值区和低值区所在的纬度和经度也会不同,总体来说,全球大部分地区的可预报性冬季都大于夏季,尤其在南极地区、热带印度洋以及北太平洋地区.(2)在垂直方向上,位势高度场可预报期限随高度升商而增加,可预报期限从对流层下层的两周以下增加到平流层下层的1个月左右,对流层和平流层天气尺度运动的可预报期限与其时间尺度是十分一致的. 相似文献
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年代际气候预测计划（DCPP）是第六次国际耦合模式比较计划（CMIP6）的子计划之一,其目标是利用多模式开展气候系统年代际预测、可预测性和变率机制研究。DCPP设计了3组试验,即年代际回报试验、预报试验以及理解年代际变率机制和可预测性的敏感性试验。目前有21个模式拟参与DCPP计划,其中包括5个来自中国的模式。DCPP将推动解决气候系统从年际到年代际尺度预测相关的多项科学问题,评估当前气候预测系统预报技巧,挖掘潜在可预报性,研究长时间尺度气候变率形成机制,提供对科学和社会有用的预测产品。 相似文献
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Recent Advances in Predictability Studies in China （1999-2002） 总被引：10，自引：2，他引：8
Since the last International Union of Geodesy and Geophysics (IUGG) General Assembly (1999), the predictability studies in China have made further progress during the period of 1999-2002. Firstly, three predictability sub-problems in numerical weather and climate prediction are classified, which are concerned with the maximum predictability time, the maximum prediction error, and the maximum allowable initial error, and then they are reduced into three nonlinear optimization problems. Secondly, the concepts of the nonlinear singular vector (NSV) and conditional nonlinear optimal perturbation (CNOP) are proposed,which have been utilized to study the predictability of numerical weather and climate prediction. The results suggest that the nonlinear characteristics of the motions of atmosphere and oceans can be revealedby NSV and CNOP. Thirdly, attention has also been paid to the relations between the predictability and spatial-temporal scale, and between the model predictability and the machine precision, of which the investigations disclose the importance of the spatial-temporal scale and machine precision in the study of predictability. Also the cell-to-cell mapping is adopted to analyze globally the predictability of climate,which could provide a new subject to the research workers. Furthermore, the predictability of the summer rainfall in China is investigated by using the method of correlation coefficients. The results demonstrate that the predictability of summer rainfall is different in different areas of China. Analysis of variance, which is one of the statistical methods applicable to the study of predictability, is also used to study the potential predictability of monthly mean temperature in China, of which the conclusion is that the monthly mean temperature over China is potentially predictable at a statistical significance level of 0.10. In addition,in the analysis of the predictability of the T106 objective analysis/forecasting field, the variance and the correlation coefficient are calculated to explore the distribution characteristics of the mean-square errors.Finally, the predictability of short-term climate prediction is investigated by using statistical methods or numerical simulation methods. It is demonstrated that the predictability of short-term climate in China depends not only on the region of China being investigated, but also on the time scale and the atmospheric internal dynamical process. 相似文献
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A New Strategy for Solving a Class of Constrained Nonlinear Optimization Problems Related to Weather and Climate Predictability 总被引：2，自引：0，他引：2
There are three common types of predictability problems
in weather and climate, which each involve different constrained nonlinear
optimization problems: the lower bound of maximum predictable time, the
upper bound of maximum prediction error, and the lower bound of maximum
allowable initial error and parameter error. Highly efficient algorithms
have been developed to solve the second optimization problem. And this
optimization problem can be used in realistic models for weather and climate
to study the upper bound of the maximum prediction error. Although a
filtering strategy has been adopted to solve the other two problems, direct
solutions are very time-consuming even for a very simple model, which
therefore limits the applicability of these two predictability problems in
realistic models. In this paper, a new strategy is designed to solve these
problems, involving the use of the existing highly efficient algorithms for
the second predictability problem in particular. Furthermore, a series of
comparisons between the older filtering strategy and the new method are
performed. It is demonstrated that the new strategy not only outputs the
same results as the old one, but is also more computationally efficient.
This would suggest that it is possible to study the predictability problems
associated with these two nonlinear optimization problems in realistic
forecast models of weather or climate. 相似文献
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Summary An atmosphere-land coupled simple climate model is constructed and its climatic properties are analyzed by introducing a global analysis method, cell mapping. The simple model is a nonlinear six order simplified climate model featured with chaotic dynamics, dissipation, and forcing source, which are the main features of the real climate system. The cell mapping method is applied with this coupled system. Numerical experiments are carried out for investigating the interactions between the fast-changing atmospheric variables and slow-changing underlying surface variables. The predictability of the system is also investigated via the global analysis, with which the evolution of the system is translated to the evolution of probability transition on a Markov Chain. An effective scheme is proposed for computing the probability transition matrix for the coupled system. Predictions can be made based on the combination of dynamics and statistics. The importance of constructing the coupled model is shown by globally analyzing the predictability of the coupled system. The coupling mechanism prolongs the memorization of initial information, and then the predictability as well. 相似文献
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该文系统地介绍了国内外评估月、季尺度短期气候预测结果的方法 ,比较了相关系数(R)、预报技巧分 (S)和准确率 (P)的特点 ,并对当前国内外气候预测业务水平进行了分析 ,着重对大气环流、气温、降水及 ENSO的预测水平进行了评估 ,指出国内外月、季尺度的降水预报的水平目前在 55%～ 60 %左右 ,对 ENSO的发生、结束和强度的预报水平有限 .文中探讨了短期气候预测的可预报性问题 ,提出月、季尺度气候预测的可预报性的理论上限可能为 6～ 1 2月 ,准确率在 80 %～ 85%之间 . 相似文献
15.
Torben Koenigk Christof K?nig Beatty Mihaela Caian Ralf D?scher Klaus Wyser 《Climate Dynamics》2012,38(11-12):2389-2408
Decadal prediction is one focus of the upcoming 5th IPCC Assessment report. To be able to interpret the results and to further improve the decadal predictions it is important to investigate the potential predictability in the participating climate models. This study analyzes the upper limit of climate predictability on decadal time scales and its dependency on sea ice albedo parameterization by performing two perfect ensemble experiments with the global coupled climate model EC-Earth. In the first experiment, the standard albedo formulation of EC-Earth is used, in the second experiment sea ice albedo is reduced. The potential prognostic predictability is analyzed for a set of oceanic and atmospheric parameters. The decadal predictability of the atmospheric circulation is small. The highest potential predictability was found in air temperature at 2?m height over the northern North Atlantic and the southern South Atlantic. Over land, only a few areas are significantly predictable. The predictability for continental size averages of air temperature is relatively good in all northern hemisphere regions. Sea ice thickness is highly predictable along the ice edges in the North Atlantic Arctic Sector. The meridional overturning circulation is highly predictable in both experiments and governs most of the decadal climate predictability in the northern hemisphere. The experiments using reduced sea ice albedo show some important differences like a generally higher predictability of atmospheric variables in the Arctic or higher predictability of air temperature in Europe. Furthermore, decadal variations are substantially smaller in the simulations with reduced ice albedo, which can be explained by reduced sea ice thickness in these simulations. 相似文献
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气候预测中的集合方法初探 总被引：11，自引：2，他引：9
介绍了气候预测中的集合方法。该文作者曾在1996年论证了在西太平洋暖池区海温异常与东亚夏季风的共同作用下存在一个可预测的气候异常区,部分地改变了气候不可预测的论断。如何从与大量不可预测结果混杂在一起的结果中提炼出可预测部分是集合方法的重要目的之一。文中也讨论了由于大气运动固有的动力学特性,其集合预测与经典的数学考虑有所区别,天气与气候预测有不同的特点,其集合方法、目的也应有所不同,由此对集合方法提出了一些新的建议。文中同时介绍了首次在气候预测中发现的多平衡态现象,建议了如何判定多平衡的出现,以及如何利用多平衡态来改善对不同区域的预测。 相似文献
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数值天气预报和气候预测的可预报性问题 总被引：29，自引：7，他引：29
考察由初始状态误差和模式中参数误差所引起的预报结果的不确定性。提出了数值天气预报与气候预测中三类可预报性问题，即，最大可预报时间，最大预报误差，初值与参数的最大允许误差。然后将这三类问题化成了对应的非线性优化问题，给出了处理此类非线性优化问题的思路，并且有数值方法对Lorenz模型研究了这三类问题。 相似文献
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Case studies in interannual to decadal climate predictability 总被引：1，自引：0，他引：1
The predictability of ocean and climate variables is investigated, using a perfect model-based case study approach that recognises that predictability is dependent on the initial climate state. In line with previous studies, large scale ocean variables show predictability for several years or more; by contrast, the predictability of climate variables is generally limited to 2 years at most. That predictability shows high sensitivity to the initial state is demonstrated by predictable climate signals arising in different regions, variables and seasons for different initial conditions. The predictability of climate variables in the second year is of particular interest, because this is beyond the timescale that is usually considered to be the limit of seasonal predictability. For different initial conditions, second year predictability is found in: temperatures in southeastern North America (winter) and western Europe (winter and summer), and precipitation in India (summer monsoon) and in the tropical South Atlantic. Second year predictability arises either from persistence of large-scale sea surface temperature (SST) and related ocean heat content anomalies, particularly in regions such as the North Atlantic and Southern Ocean, or from mechanisms that involve El Niño Southern Oscillation (ENSO) dynamics. 相似文献