苏联长期天气预报的物理统计综合方法

1964年以来,苏联地球物理观象总台动力气象室深入研究了长期预报的物理统计方法,并从1970年起作出业务预报。它的工作代表了苏联物理统计预报方法的现状和动向,现综述如下。一、基本原理和信息来源物理统计方法是统计预报方法的发展。近十几年尤金等人在统计预报的基础上致力于加强其物理内容,并引进现代数理统计理论,形成了物理统计综合方法。它的基本原理是:从表征大气物理现象相互联系的信息系统中,挑选出符合一定要求的良好的预报关系,对因子和预报量的自然正交分解系数按统计方法建立预报方程并求解,再对预报结果检验评定以改进方法。     

因子交叉影响对降水预报的作用

本文针对预报量(降水)与预报因子之间的非线性特点,增添了幂函数型和交叉乘积型预报因子,同时采用“两段筛选”方法建立MOS判别方程。业务预报试验结果表明:它的击中率达87.5％,Hedij技巧得分为0.34,特别是对大—暴雨的预报能力明显提高。这对一般统计与MOS天气预报具有普遍意义。     

用完全预报法制作春季全盟性寒潮

完全预报是应用实际观测资料建立预报量实测值和预报因子实测值之间的同时关系,得出统计预报方程,然后在假定数值预报是完全正确的情况下,将数值预报产品结合其他因子代入预报方程,作出天气要素预报。对于接收数值预报产品年限较短,而历史资料较多的阿盟,采用完全预报法较为适宜。     

用PC——1500袖珍计算机筛选统计预报因子的几种方案

无论是经典统计预报方法还是动力一统计预报(MOS,PPM等)方法,筛选预报因子都是一个重要步骤。下面是用PC-1500袖珍计算机筛选预报因子的几种方案。这几种方案,在输入预报量的观测值及筛选因子的标准后,只要将待选的各个可能预报因子的观测值依次输入,即可初选出符合指定标准的预报因子,并可给出供进一步筛选因子和建立预报方程的有关统计结果。     

统计预测方法的试验比较

通过相关系数选出预报因子,用逐步回归、最优子集回归、EOF降维逐步回归、神经网络等统计预报方法对预报对象进行逐年交叉检验以及实际预报检验,检测预报因子对原预报序列的预报能力。同时通过计算每种方法的平均绝对误差和实际预报检验的平均误差,客观地对各种预报方法进行既有纵向也有横向的较为全面的比较。     

MOS预报中的几个问题

1998年3月26～28日,美国气象专家美籍华人严巍先生在银川就美国气象统计预报系统的现状与未来及MOS预报的基本方法作了学术讲座,严巍先生在美一直从事MOS预报的工作,达15a左右,作了大量的工作,根据有关方面材料及记录进行整理,就MOS预报中的几个问题作一简单介绍,供大家在数值预报产品的解释应用中参考。1MOS预报的优点由于MOS预报采用数量统计方法建模,从若干待选因子中选取与预报量相关性较好的因子,作为MOS预报的因子,不仅与预报量有良好的统计相关关系,也要求因子与预报量间具有明确的物理学、天气动力学意义．因此…     

挑选预报因子的极差分析法

如何挑选有效的预报因子是统计预报的一个关键问题。目前挑选因子的方法很多,对同一个预报量,用不同的方法往往挑选出不同的预报因子,所得出的预报结果也不尽相同,每种方法的计算量也不相同。因此,寻找一种简便而且预报效果较好的挑选因子方法是有重要意义的。极差分析法就是这方面的一个初步尝试。根据我们体会,此方法与量级计算法配合进行中长期预报效果较好。     

逐步回归双重分析在长期预报中的应用

前言逐步回归双重分析方法运用了逐步回归方法,同时进行因子的筛选和周期分析,在制作预报时,既考虑了前期因子对预报对象的支配作用,又兼顾了预报对象本身周期变化的影响。解决了多元回归和周期分析两种统计方法的不足之处,预报效果较为稳定,预报精度明显提高。一、统计模型的建立气象要素的变化不仅受到前期因子的支配,同时要素本身也有其演变规律,只是在不同的情况、不同的条件下起的作用不同而已。本方法的统计模型为     

统计预测方法的试验比较

通过相关系数选出预报因子后,用逐步回归、最优子集回归、EOF降维逐步回归、神经网络等统计预报方法对预报对象进行逐年交叉检验以及实际预报检验,检测预报因子对原预报序列的预报能力。同时通过计算每种方法的平均绝对误差和实际预报检验的平均误差,客观地对各种预报方法进行既有纵向也有横向的较为全面的比较。     

湖州市汛期旱涝预报系统

我们选择了优良可靠的推理统计模型来制作湖州市汛期旱涝长期预报.同时对预报量、因子场及因子普查方法等采取了一系列技术处理.所建立的长期预报系统稳定可靠,预报准确率比以往有明显的提高,特别对异常年份的预报能力较强.     

A numerical method of statistical pattern recognition

A numerical method of statistical pattern recognition is proposed in this paper. Different from the discriminatory analysis method currently used in the mathematic statistics, it is unnecessary to assume that the predictand should be subject to a certain distribution. On the contrary, the statistical relationship between predictand and predictor has been obtained directly with computer according to actual distribution to recognize the category of patterns. Result of forecast has been improved as compared with the usual analytic discriminatory method. The influence of predictor on predictand can be seen clearly from this method and the transparency is good. Therefore, it is better to use the method in very short range forecast for which causality is more obvious.     

巴州北部山区汛期降水趋势的时间序列预测法

用时间序列法分析了巴音布鲁克、巴仑台两地汛期各阶段的降水规律，并做出预报方程、进行历史检验，结果表明拟合效果较好，说明这种预测方法有一定的实用性。     

Statistical downscaling of regional daily precipitation over southeast Australia based on self-organizing maps

This paper presents a novel statistical downscaling method based on a non-linear classification technique known as self-organizing maps (SOMs) and has therefore been named SOM-SD. The relationship between large-scale atmospheric circulation and local-scale surface variable was constructed in a relatively simple and transparent manner. For a specific atmospheric state, an ensemble of possible values was generated for the predictand following the Monte Carlo method. Such a stochastic simulation is essential to explore the uncertainties of climate change in the future through a series of random re-sampling experiments. The novel downscaling method was evaluated by downscaling daily precipitation over Southeast Australia. The large-scale predictors were extracted from the daily NCAR/NCEP reanalysis data, while the predictand was high-resolution gridded daily observed precipitation (1958?C2008) from the Australian Bureau of Meteorology. The results showed that the method works reasonably well across a variety of climatic zones in the study area. Overall, there was no particular zone that stands out as a climatic entity where the downscaling skill in reproducing all statistical indices was consistently lower or higher across seasons than the other zones. The method displayed a high skill in reproducing not only the climatologic statistical properties of the observed precipitation, but also the characteristics of the extreme precipitation events. Furthermore, the model was able to reproduce, to a certain extent, the inter-annual variability of precipitation characteristics.     

中国日降水量与其占有面积的函数关系

根据中国近40年逐日降水观测资料,分析了大范围空间域上不同等级日降水量与其占有面积的函数关系。研究表明,几乎每一年任何一天中国区域的不同等级日降水量场的统计分布都呈明显的左偏态型。利用负指数分布和Weibull分布的概率密度函数(PDF)拟合实测降水资料的结果表明,后者拟合效果优于前者。负指数分布型可能仅适用于同一天气系统控制下的一次降水过程或较小的区域范围,在更大的区域内(如中国区域),由于构成降水的多种天气系统在同一日相互叠加,可能用Weibull分布更适合。     

论最优预报因子与最优预报方程

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。     

多元门限回归模型的一种建模方法

本文根据门限自回归模型的基本思想[1]，提出一种多元门限回归模型的建模方法。其特点是充分考虑了预报系统中某些特殊预报因子突变点对预报关系的改变作用。数值实例表明，该模型在模拟和预报精度上比一般线性逐步回归模型有一定程度的提高。     

一种神经网络的云图短时预测方法

依据6hT213数值预报产品的资料,采用EOF展开和人工神经网络等方法,对卫星云图短时预报方法进行研究。首先对卫星云图灰度值样本序列进行EOF展开,将提取出来的时间系数作为建模的预报量,以数值预报产品的物理量场作为预报因子,建立人工神经网络预测模型。将预报得到的时间系数与空间特征向量进行时空反演,实现对未来6h云图的预测。预报方法的独立样本试验证明,预测结果与实际云图的主要特征基本吻合,尤其在预测云图的大体分布和发展趋势上得到了较好效果。     

最优化因子处理及加权多重回归模型

因子的优劣是回归分析的关键。故在建立回归方程时,一般除对预报因子进行直线相关普查外,还要进行非线性相关普查。如将原因子x用x~(-1)、x~(1/2)、x~2 、e~x、In x等函数形式进行变换。但由于这些函数形式有限,故不一定能找到最优的表达形式;为此冯耀煌等仅给出了x~a和e~(ax)两种通式,其中a为待     

An intercomparison of observational precipitation data sets over Northwest India during winter

Development of downscaling models for each calendar month using the data of predictors specifically selected for each calendar month may assists in better capturing the time-varying nature of the predictor-predictand relationships. Such approach will not allow the explicit modelling of the persistence of the predictand (e.g. lag-1 correlation). However, downscaling at an annual time step and subsequent disaggregation to monthly values can explicitly consider the modelling of the persistence of the predictand. This study investigated the potential of annual downscaling of a predictand and subsequent disaggregation of annual values to monthly values, in comparison to the potential of downscaling models separately developed for each calendar month. In the case study, annual and monthly downscaling models were developed for precipitation and evaporation at two stations located in Victoria, Australia. The output of the annual downscaling models was then disaggregated into monthly values using four different methods based on the method of fragments. It was found that the annual to monthly disaggregation methods and monthly downscaling models are able to reproduce the average of monthly observations with relatively higher accuracy in comparison to their ability in reproducing standard deviation, skewness and lag-1 serial correlation. Downscaling models separately developed for each calendar month were able to show relatively smaller root mean square errors for their time series indicating better overall agreement with observations in comparison to their counterpart annual to monthly disaggregation methods. Furthermore, it was found that not only the bias in the output of an annual downscaling model but also the presence of annual totals in the records of observations of a predictand that are very similar in magnitude, but having significantly different sets of fragments, can largely contribute to the poor performance of an annual to monthly disaggregation method.