STOCHASTIC SIMULATION OF DAILY PRECIPITATION, TEMPERATURE, AND SOLAR-RADIATION
STOCHASTIC SIMULATION OF DAILY PRECIPITATION, TEMPERATURE, AND SOLAR-RADIATION
复制标题
DOI：
10.1029/wr017i001p00182
复制
发表时间：
1981-01-01
影响因子：
5.4
通讯作者：
RICHARDSON, CW
中科院分区：
文献类型：
--
作者：
RICHARDSON, CW
经常需要长时间的天气数据样本来评估拟议水文变化的长期影响。评估通常使用确定性数学模型进行，需要每天的天气数据作为输入。随机生成所需的天气数据提供了一个有吸引力的替代使用观测到的天气记录。本文提出了一种方法，可用于生成长样本的日降水量，最高温度，最低温度和太阳辐射。通过使用马尔可夫链-指数模型，独立于其他变量生成降水量。其他三个变量是通过使用多变量模型生成的，其中变量的平均值和标准差取决于由降水模型确定的一天的潮湿或干燥状态。使用这种方法生成的每日天气样本保留了每个变量的季节和统计特征以及观测数据中存在的四个变量之间的相互关系。
Long samples of weather data are frequently needed to evaluate the long‐term effects of proposed hydrologic changes. The evaluations are often undertaken using deterministic mathematical models that require daily weather data as input. Stochastic generation of the required weather data offers an attractive alternative to the use of observed weather records. This paper presents an approach that may be used to generate long samples of daily precipitation, maximum temperature, minimum temperature, and solar radiation. Precipitation is generated independently of the other variables by using a Markov chain‐exponential model. The other three variables are generated by using a multivariate model with the means and standard deviations of the variables conditioned on the wet or dry status of the day as determined by the precipitation model. Daily weather samples that are generated with this approach preserve the seasonal and statistical characteristics of each variable and the interrelations among the four variables that exist in the observed data.