Stochastic Simulation of Daily Climatic Data for Agronomic Models1
Stochastic Simulation of Daily Climatic Data for Agronomic Models1
复制标题
农艺模型的日常气候数据的随机模拟1
DOI：
--
复制
发表时间：
1982
期刊：
影响因子：
--
通讯作者：
R. B. Pense
中科院分区：
文献类型：
--
作者：
G. Larsen;R. B. Pense
许多农学模型需要输入每日气候数据。当无法获得或不方便获得长系列历史数据时，或需要未来数据时，可使用模拟气候数据。本文建立了一个随机天气模拟模式，并对一系列气候条件进行了验证.该模式产生可能的降水量，最高和最低气温，以及全年在地球表面的总太阳辐射的日序列。用一阶二状态马尔可夫链模拟了干湿日的发生。概率是用来模拟发生的微量降水量在潮湿的日子。由前一天的降水状况条件的两参数伽玛分布用于产生大于痕量的量。用两个以当日降水状况为条件的二元正态分布来模拟当前气温与长期平均气温曲线的偏差。一个双参数伽玛分布模拟当前太阳辐射与计算出的最大晴天辐射在干燥天的偏差。在潮湿的日子里，偏差是用两个参数的beta来模拟的。
Many agronomic models require the input of daily climatic data. Simulated climatic data may be used when long series of historic data are not available or convenient, or when future data are needed. A stochastic weather simulation model was developed and validated for a ~;de range of climates. The model produces possible daily sequences of precipitation amount, maximum and minimum air temperature, and total solar radiation at the earth's surface for the entire year. A first-order, two-stat~ Markov chain is used to simulate the occurrence of wet and dry days. Probabilities are used to simulate the occurrence of trace precipitation amounts on wet days. A two-pa· rameter gamma distribution conditioned by the precipitation status on the previous day is used to generate greater than trace amounts. Two bivariate normal distributions conditioned by the precipitation status on the current day are used to simulate current temperature de"iations from long.term average temperature curves. A two-parameter gamma distribution simulates current solar radiation deviations from the calculated maximum clear day radiation on dry days. On wet days, the deviations are simulated with a two-parameter beta