基于干湿期的随机天气发生器

为了按不同的应用需求生成可信的任意长序列逐日天气数据,为作物天气系统研究提供数据支持,该文描述了一个以干湿期随机模型为基础,组合了日降水量、温度和辐射变量随机模型的逐日天气发生器WGDWS（Weather Generator based on Dry and Wet Spells）。它分为两部分：以干湿期为独立随机变量的干湿期模型部分,和依赖第一种模型生成其余天气变量的模型部分;其天气要素的生成主要分2个步骤,即首先根据月经验分布值产生一个干期或湿期长度,然后生成干期或湿期的逐日值。利用代表中国不同地理区域的9个站点1973-2003年的逐日气象资料对天气发生器WGDWS进行了检验,并与基于干...

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Bibliographic Details
Published in农业工程学报 Vol. 30; no. 11; pp. 118 - 125
Main Author 李世娟 诸叶平
Format Journal Article
LanguageChinese
Published 中国农业科学院农业信息研究所农业部农业信息服务技术重点实验室,北京,100081 2014
Subjects
天气发生器
干期
模型
气温
湿期
降水
models
precipitation
wet spell
dry spell
降水
干期
模型
气温
天气发生器
temperature
weather simulator
湿期
Online AccessGet full text
ISSN1002-6819
DOI10.3969/j.issn.1002-6819.2014.11.015

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Summary:为了按不同的应用需求生成可信的任意长序列逐日天气数据,为作物天气系统研究提供数据支持,该文描述了一个以干湿期随机模型为基础,组合了日降水量、温度和辐射变量随机模型的逐日天气发生器WGDWS（Weather Generator based on Dry and Wet Spells）。它分为两部分：以干湿期为独立随机变量的干湿期模型部分,和依赖第一种模型生成其余天气变量的模型部分;其天气要素的生成主要分2个步骤,即首先根据月经验分布值产生一个干期或湿期长度,然后生成干期或湿期的逐日值。利用代表中国不同地理区域的9个站点1973-2003年的逐日气象资料对天气发生器WGDWS进行了检验,并与基于干湿日开发的DWSS天气发生器进行了比较。结果表明两者性能基本相近,并且WGDWS模拟干湿期的效果更好。因此,WGDWS天气发生器用于生成逐日天气序列是可靠的,同时作为一个JAVA组件,还可以方便地嵌入作物模型系统。
Bibliography:11-2047/S
models;temperature;precipitation;dry spell;wet spell;weather simulator
Climate change is important for agriculture and the environment. Changing rainfall amounts have positively or negatively impact on plant growth. The reduction in solar radiation can potentially reduce the photosynthesis, growth of plants and potential evaporation. Stochastic weather generators can generate a long series of weather variable statistics, which usually are used as the input of system models to analyze and evaluate the effect of climate on systems. This paper described a stochastic weather generator WGDWS which consisted of dry and wet spell, daily precipitation, solar radiation, and maximum and minimum temperature models. It included two types of models. The first one was a dry and wet spell model in which dry and wet spell lengths were defined as an independent stochastic variable respectively, and it was the principal model. The second one referred to other weather variables whose modeling was dependent on the first
ISSN:1002-6819
DOI:10.3969/j.issn.1002-6819.2014.11.015