基于统计方法的天气要素随机模拟成为许多地表过程模型的重要组成部分，但是对湿度的研究较少。国内对风向风速的研究较多，但研究区域范围较小，多局限在一省之内，而且很少有细分为16个方向12个月份的情形。国外对风向风速研究较细，但是风的区域特性很显著,不一定适用于我国。本文分析了我国相对湿度、水汽压、露点温度等湿度指标和风向风速的特征，确定合适的概率分布型及其统计参数的估算办法，建立了可以生成日相对湿度、日水汽压、日露点温度和日风速风向的随机模型。利用我国716个气象站点1954-2008年的逐日气温和相对湿度资料，以及北方地区244个气象站点1957-2010年的逐日风向风速的资料，研究了湿度的三个参量和风向风速的概率分布特征，进行了随机模拟和效果检验。主要结论如下：(1)湿度指标中，露点温度符合正态分布，水汽压符合对数正态分布，而且二者均在长江中下游、甘肃青海、新疆南部一带，具有较高的假设检验通过率，但在两广地区、黄土高原地区的通过率较低。相对湿度符合威布尔分布。在环渤海一带的假设检验通过率较高。三种湿度参量相比较，露点温度正态分布的假设检验的通过率最高，适用的区域最广；水汽压对数正态分布效果次之，相对湿度威布尔分布的效果不太理想。模拟序列均值和标准差与实测序列相比，相对湿度的相对误差最小，水汽压次之，露点温度最大。12个月里，相对湿度均值的平均相对误差0.37%-0.64%，水汽压为0.32%-0.95%，露点温度为 0.70%- 6.94%。(2)12个月平均出现频率最大的三个风向依次是西，西北和西西北；平均出现频率最小的三个风向依次是北东北，东东南和南东南；平均出现频率最大的风向西风，其频率为7.1%；平均出现频率最低的风向为北东北，其频率为4.0%。静风的出现频率在3月-6月较低，4月份最低，为13.52%；在10月-1月较高，12月份最高，为25.39%。全年中，3月至5月这三个月平均风速最大。6月至8月这三个月平均风速最小。全年风速最大的风向始终为偏西西北，风速最小的风向一般为偏东。(3)将风向视作离散变量进行随机模拟。17个风向状态出现频率的模拟序列与实测序列的平均相对误差为3.70%-4.35%。12个月静风出现频率的模拟序列的相对误差为1.58%- 2.34%。威布尔分布和伽马分布都较为符合风速的分布。概率权重矩法得到的威布尔分布序列0.01显著性水平KS检验的16个风向的平均通过率介于0.282-0.542。经典矩法得到的威布尔分布序列0.01显著性水平KS检验的16个风向的平均通过率介于0.283-0.525。在全年中，6、7、8月和9月的通过率较低，2、3、4月和5月的通过率相对较高。在16个风向中，西西南、西西北和北西北风向的通过率较高。通过率在季节的差异显著大于风向上的差异。1、4、7、10月概率权重矩法和经典矩法得到的威布尔分布的风速序列的均值与实测序列的平均相对误差均为2%上下。经典矩法得到的序列的标准差的平均相对误差约为3%，而概率权重矩法得到的序列的标准差的平均相对误差约为6%。经典矩法的模拟精度要高于概率权重矩法，且经典矩法计算简便。

The stochastic weather generation method has been a necessary component for surface process models, but the research about humidity distribution is not enough.There are lots of discussion on the research about wind velocity in China, at the same time, the scope of the discussion is local and not divided into 16 types of direction and into months. Wind of 16 types of direction and months has been reserched penetratingly abroad .Because character of wind is regional, conclusion on wind abroad may be adapt to China. This study explored the character of dew temperature, water vapor pressure, relative humidity, wind direction and wind velocity, found appropriate types of distribution, provided the compute methods of statistics parameters, for founding several stochastic models of daily dew temperature,water vapor pressure , relative humidity, wind direction and wind velocity. In this study, the daily relative humidity and air temperature data from 1954 to 2008 for 716 weather stations in China was used to analyze the distribution character of dew temperature, wat -er vapor pressure and relative humidity, and to simulate sequence effectivly. In addition, the win -d data from 1957 to 2010 for 244 weather stations in North China so did for wind direction and wind velocity. The mainly results of this reaserch are listed as below:(1) Dew temprature(DEW) is adapted to Normal distribution, water vapor pressure(VAP) is to logarithmic normal distribution, and the rate of weather station passed hypotheses test, in the middle and lower reaches of the Yangtze River, Gansu, Qinghai and north of Xinjiang keeps high, and keeps low in Guangdong, Guangxi and the Loess Plateau. Relative humidity(RH) is adaped to Weibull distribution. The rate of weather station passed hypotheses test keep high in area around Bohai Bay. Compared this three varible which describs air humidity, the rate of weather station passed hypotheses test of Normal distribution for DEW is the highest, and the scope of station adapted to Normal distribution for DEW is the widest. VAP takes the second place. RH takes the worst place. Compared relative error of mean value and standard deviation of simulate sequence and observed sequence, the relative error of RH is lowest, VAP takes the second place, and DEW takes the worst place. In twleve months, the value of average relative error of RH is 0.37%-0.64%, of VAP is 0.32%-0.95%, and of DEW is 0.70%-6.94%.(2)In 12 months,the average of frequecy of 17 directors wind, at such directors as W, NW, a -nd WNW is the highest; at such directors as NNE, ESE, and SSE is lowest. The average of freq -uecy at director E is highest, and it is 7.1%; The average of frequecy at director NNE is lowest, and it is 4.0%. The average of frequecy of calm wind in March, April, May and June is lowest, a -nd in October, November and December is highest. In 12 months, average wind velocity of all weather station in March,April and May is highest, in June, July and August is lowest. Average wind velocity at director WNW and its approximation is highest; Average wind velocity at dire -ctor E and its approximation is highest. (3) Regarded wind director as discrete variable to simulate wind director. The mean value of the relative error of frequency of 17 directors wind,are 3.70%-4.35%. The relative error of fr -equency of calm wind in 12 months is 1.58%-2.34%. Weibull distribution and Normal distribu -tion are suitable to wind velocity. The rate of weather station which passed KS test with signifi -cance at p=0.01 for Weibull distribution sequence derived by classical moment method at 16 wi -nd director, is 0.282-0.542,and which derived by Probability weight moment method is 0.283 - 0.525. The number of weather station which passed KS test with significance at p=0.01 at odd order director is less than 15 at even order director. The rate of weather station which passed KS test with significance at p=0.01 for Weibull distribution simulated sequence, in June, July, Augu -st and September is low, and in February, March, April and May is high.At all wind directors, the rate at director WSW, WNW and NNW is high. The differrence of rate in months is higher th -an in directors. The average ralative error of mean value of obeserved sequence and simulated sequence derived by Probability weight moment method and classical moment method is about 2%. The average ralative error of standard deviation of obeserved sequence and simulated sequ -ence derived by Probability weight moment method is about 3%, which derived by classical mo -ment method is about 6%. The accuracy of simulating method of classical moment is higher th -an Probability weight moment,and classical moment is concise.