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1.
根据Bayes准则下的多元线性判别分析和时间序列的线性自回归模式,本文提出一种时间序列的判别预报模式。该模式采用两种不同的变量筛选方案,对于气象时间序列的数量记录,由过去的记录判别未来记录的趋势(如正负距平、旱涝等)。在一定的自相关结构下,其判别效果较好。文献[1—4]曾论述用(0,1)两值时间序列建立AR(p)模式,但AR(p)模式有其局限性。将时间序列与多元判别分析结合,建立时间序列基础上的判别模式,用以往各时刻变量作为线性判别因子对未来各时刻的变量取值类型作出判别,既可保留时间序列线性模式的优点,又可利用多元逐步判别筛选因子的计算方法。从气象状况演变的物理机制来看,考虑前期状态演变比单纯考虑前期某一时刻的状态更有意义。 相似文献
2.
C. Michailidou P. Maheras A. Arseni-Papadimititriou F. Kolyva-Machera C. Anagnostopoulou 《Theoretical and Applied Climatology》2009,97(1-2):179-194
A discriminant analysis is applied in order to determine the relationships between circulation types in the middle troposphere and prevailing weather types over two major Greek cities, Athens and Thessaloniki. In order to describe the synoptic conditions, an automatic classification scheme for the Greek region is used. For each circulation type identified (14 in total), several meteorological parameters at the 500 hPa level are calculated such as geopotential heights and their anomalies, temperature and relative vorticity. Weather group-types that reflect the conditions at the surface, were previously defined using a two-step cluster analysis. These types result from a combination of five meteorological parameters—maximum temperature, precipitation amount, relative humidity, wind velocity and sunshine duration. The study period is 43 years long (1958–2000) and is restricted to the cold and wet period of the year, from December until March. For Athens, six weather types are developed, whereas for Thessaloniki five are produced. By means of a stepwise discriminant analysis (DA) model, the most important variables from the 500 hPa level are found and are used to generate the necessary functions that can discriminate weather types over the two stations. The aim of the present study is first to discriminate weather types effectively and to identify the most important discriminating variables, and second, to connect these weather types to elements of the prevailing synoptic pattern, through mathematical functions provided by DA. The results of the evaluation of the aforementioned procedure are considered to be very satisfactory. 相似文献
3.
The method of quadratic discrimination through orthogonal transformation is introduced to optimize quadratic discriminant function, which has been proved more effective than the method of stepwise multiple discrimination. It is noteworthy that, in the case of a large number of predictors, it is better to make a preliminary choice by using stepwise multiple discrimination or stepwise regression so that the calculation can be made more stable and the effectiveness of discrimination can be improved. 相似文献
4.
Baroclinic Instability in the Generalized Phillips’ Model Part II: Three-layer Model 总被引：2，自引：0，他引：2
The nonlinear stability of the three-layer generalized Phillips model, for which the velocity in each layer is constant and the top and bottom surfaces are either rigid or free, is studied by employing Arnol’d’s variational principle and a prior estimate method. The nonlinear stability criteria are established. For com-parison, the linear instability criteria are also obtained by using normal mode method, and the influences of the free parameter, β parameter and curvature in vertical profile of the horizontal velocity on the linear in-stability are discussed by use of the growth rate curves.The comparison between the nonlinear stability criterion and the linear one is made. It is shown that in some cases the two criteria are exactly the same in form, but in other cases, they are different. This phenom-enon, which reveals the nonlinear property of the linear instability features, is explained by the explosive resonant interaction (ERI). When there exists the ERI, i.e., the nonlinear mechanisms play a leading role in the dynamical system, the nonlinear stability criterion is different from the linear one; on the other hand, when there does not exist the ERI, the nonlinear stability criterion is the same as the linear one in form. 相似文献
5.
Application of Nonlinear Balance Scheme in Three-Dimensional Variational Data Assimilation 总被引：1，自引：1，他引：1 
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下载免费PDF全文 The dynamical constrains in three-dimensional variational data assimilation are discussed when consid- ering the impact of stream divergence and convergence on the pressure and wind fields.For the analysis of severe tropical cyclone,frontal structures,and other rapidly changing structures,the geostrophic balance and linear balance cannot properly represent the relationship between wind and pressure fields.However,the nonlinear balance incremental equation takes into account the information of flow-dependent background, and makes response to the flow-dependent background covariance in the 3D-Var system.Results indicate that the application of the nonlinear balance equation to 3D-Var system improves the quality of severe trop- ical cyclone assimilation system,which has some positive effects on intensity prediction of tropical cyclones. 相似文献
6.
利用2008年1月—2013年12月以及2017年1—11月全球天气预报系统（GFS）预报场资料,采用自适应线性最小二乘回归（LS）和自适应递推卡尔曼（Kalman）滤波两种动态时变参数方法,建立了河套周边地区0~168 h预报时效的总云量精细化预报,并与GFS模式直接输出的总云量、线性预报模型逐步回归预报方法得到的总云量以及非线性预报模型BP神经网络和最小二乘支持向量机回归方法（LSSVM）得到的总云量进行了对比,结果如下:（1）相比GFS模式直接输出的总云量,LS、BP神经网络、LSSVM得到的总云量与实况值的平均绝对误差均明显减小。LS方法误差最小,LS方法的年MAE均在20%~25%,且随着预报时效的延长,改进效果越大。LS方法、多元逐步回归方法、BP神经网络、LSSVM四种方法在6—8月的改进效果最大。（2）LS方法预报的总云量与实况云量的相关性最好,即使168 h预报时效的相关系数依然在0.64以上,远高于其他几种模型的预报结果。（3）LS方法能够明显地提高少云和多云天空状况下预报的击中率,且最优（少云击中率平均提高24 %,多云击中率平均提高34 %）。（4）自适应递推Kalman滤波方法存在预报滞后现象,改进效果不明显。 相似文献
7.
论最优预报因子与最优预报方程 总被引：9，自引：3，他引：9
本文根据预报量与预报因子关系的实际情况,提出了预报因子的四种类型,并且利用最优化方法原理选取最优的预报因子,同时还利用强迫引进重要因子的办法选取最优预报方程。从实例计算证明,最优预报因子基本上是非线性的,所以用逐步回归或逐步判别方法建立的预报方程也是非线性的,并且还证明,非线性预报方程比线性预报方程效果有明显的提高。本文最后还指出,此种非线性预报方法可推广到各个领域有关多元分析的定量和定性预报工作中去。 相似文献
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在中尺度WRF-EnSRF系统中最新引入的采样误差订正局地化方法不仅考虑了回归系数偏差,而且计算量较小。该方法基于状态变量和对应观测值的相关系数的分布关系,根据离线蒙特卡洛技术制作的关于集合数和样本相关系数的查找表格确定局地化系数因子,进而订正由集合数选取有限造成的背景误差协方差被低估引起的采样误差。本文利用风暴过程的雷达观测资料做了一系列风暴尺度的资料同化理想试验,探讨了采样误差订正局地化方法在风暴尺度集合卡尔曼滤波同化中的技术特点和同化效果。结果表明:相比于经验局地化方法,采样误差订正局地化方法能够有效地改善集合同化的效果,对距离的敏感度更低,尤其在天气系统发展变化较快的阶段,新方法优势更大。并且,对不同观测变量以及在风暴发展的不同阶段使用不同的局地化方法,所得的结果都存在一定的差异,因此需要根据同化对象合理地选择局地化方法。 相似文献
10.
Summary A new method of four-dimensional data assimilation based on Singular Value Decomposition (SVD) is proposed. In it, a set of
atmospheric states is obtained by integrating a numerical weather prediction model and simulated observations are taken and
calculated from the model variables. Then the SVD technique is used to create the base vectors from this coupled data set.
Finally, the analysis is obtained by projecting actual observation data into a space spanned by the base vectors. Using this
approach, the four-dimensional data assimilation becomes a simple linear inverse problem the linearization of the nonlinear
forward model is avoided, and the developments of the adjoint and background error covariance matrix are no longer needed.
Since the SVD technique is used here, the method is simply called 4DSVD. 相似文献
11.
Antony A. Hill 《Dynamics of Atmospheres and Oceans》2004,38(1):441
A model for convection induced by the selective absorption of radiation in a fluid layer is investigated. The concentration based internal heat source is modelled quadratically. A linear analysis and conditional nonlinear analysis are performed. Due to the presence of significantly large regions of potential subcritical instabilities, the results indicate that linear theory may only be accurate enough to predict the onset of convective motion when the model for the internal heat source is predominantly linear. This lends much credence to the use of a linearly modelled internal heat source as used by Krishnamurti [Dyn. Atmos. Oceans 27 (1997) 367], within the constraints of the parameter ranges. 相似文献
12.
针对目前采用的统计方法存在的不足, 即在选择预报因子时没有考虑预报因子之间的相关性, 挑选的预报因子由于非正交, 使回归计算的结果不稳定, 给计算带来一定的误差。该文提出把一元线性回归分析、自然正交函数 (EOF) 和逐步回归方法结合起来, 从而得到一种新的建立统计预报模型的方法。以西安市采暖期和夏季SO2日均浓度为预报对象, 使用该方法建立预报模型。拟合及预报试验表明, 这些预报模型不但可以很好地拟合变化趋势, 而且还能作出较准确的预报, 采暖期预报的级别命中率为72.5 %, 夏季级别预报命中率为100%。通过对比试验, 此方法优于目前常用的逐步回归方法, 具有很好的应用前景。 相似文献
13.
主要研究了具有Lipschiz-type非线性多智能体系统的分布式优化问题.在多智能体网络中,每个个体都拥有一个代价函数,整个多智能体网络的好坏由这些代价函数的和来进行评判.在整个过程中,每个目标仅知道局部的交互信息和其自身代价函数的梯度.为了实现协同优化的目标,提出了一个新的分布式优化算法,运用李雅普诺夫稳定性分析的方法可以证明该算法能够保证所有智能体实现协同优化.最后进行数值仿真,成功地验证了该算法的正确性和可行性. 相似文献
14.
Comparative analysis of regression and artificial neural network models for wind speed prediction 总被引：1，自引：1，他引：0
In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods. 相似文献
15.
Shuoben Bi Shengjie Bi Xuan Chen Han Ji Ying Lu 《Asia-Pacific Journal of Atmospheric Sciences》2018,54(4):611-622
Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction. 相似文献
16.
The effects of stochastic forcing on a one-dimensional, energy balance climate model are considered. A linear, stochastic model is reviewed in analogy with the Brownian motion problem from classical statistical mechanics. An analogous nonlinear model is studied and shows different behavior from the linear model. The source of the nonlinearity is the dynamical heat transport. The role of nonlinearity in coupling different temporal and spatial scales of the atmosphere is examined. The Fokker-Planck equation from statistical mechanics is used to obtain a time evolution equation for the probability density function for the climate, and the climatic potential function is calculated. Analytical solutions to the steady-state Fokker-Planck equation are obtained, while the time-dependent solution is obtained numerically. The spread of the energy produced by a stochastic forcing element is found to be characterized by movement mainly from smaller to larger scales. Forced and free variations of climate are also explicitly considered. 相似文献
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18.
集合变分混合同化背景误差协方差流依赖性分析 总被引：4，自引：2，他引：2
通过单点观测试验的方法,对集合变分混合同化背景误差协方差的流依赖特征、流依赖性影响因子、产生原因,以及集合预报方法对流依赖性的影响进行了研究。结果表明:由于引入了集合信息,集合变分混合同化的分析增量与天气系统的分布有关,具有非均匀、各向异性的特征;这种流依赖特征对混合系数敏感,当集合协方差所占权重很小时,分析增量仍呈现出均匀、各向同性特征;混合同化背景误差协方差的流依赖特征不仅与集合样本有关,还与构造集合协方差的ETKF方法有关,只引入与环流形势密切相关的集合样本并不能使分析增量表现出显著的流依赖性,集合样本和ETKF方法共同作用才能将流依赖信息引入到混合协方差中,使分析增量出现流依赖特征;不同集合预报方法对混合协方差的流依赖特征有显著影响,考虑初值和物理过程的超级集合,以及在超级集合样本上再进行ETKF更新扰动后样本构造的混合协方差流依赖特征更加显著。 相似文献
19.
An Adaptive Estimation of Forecast Error Covariance
Parameters for Kalman Filtering Data Assimilation 总被引：1，自引：0，他引：1
Xiaogu ZHENG 《大气科学进展》2009,26(1):154-160
An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering
data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of
perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are
adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed
approach could be applied to Kalman filtering data assimilation with imperfect models when the model
error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate
the efficacy of the proposed approach. 相似文献