摘要
中长期降水量的预测是气象科学的一个难点问题,也是水文学中的一个重要问题。根据降水过程存在大量不确定性的特点,通过聚类分析建立降水序列的分级标准,采用规范化的各阶自相关系数为权重,用滑动平均的马尔可夫链模型,通过状态转移概率矩阵预测未来时段的降水状态,并根据模糊集理论中的级别特征值计算具体的降水量,最后以隆德县水文站54年的降水资料为实例,对该方法进行了具体的应用,预测精度较高,为提高中长期降水量预报的精度提供了一条值得探索的途径。
The prediction of the medium-and-long-range precipitation is a difficult problem of meteorology. And it is also an important one in hydrology. According to the uncertain characteristic in precipitation procession,the graduation standard of the precipitation serial was set through cluster analysis, the normalized different autocorrelation coefficient was used as weight,the moving average-Markov chain was used to predicit precipitation state in a future period with state transition probability matrix,then the paticular precipitation was calculated according to the class designated value in unresolved set theory. At last,the precipitation material in 54 years of the hydrologial station in Longde county was used as a model and found out that the precipitation accuracy was satisfied. So the moving average-Markov chain can be used in the prediction of the medium-and-long-range precipitation,which provides a channel to be searched.
出处
《水土保持研究》
CSCD
北大核心
2005年第6期196-198,205,共4页
Research of Soil and Water Conservation
基金
国家"十五"重大科技攻关项目"宁夏河东沙地退化草场植被恢复与风蚀沙化防治技术示范区(盐池)(2002BA517A)"资助
关键词
降水
聚类分析
滑动平均
马尔可夫链
预测
precipitation
cluster analysis
moving average
Markov chain
prediction
作者简介
李娟（1982-）．女、在读硕士研究生．研究方向：水文与水资源．曾获宁夏回族自治区科学技术进步三等奖。
