降水预测的模糊权马尔可夫模型及应用

Research on fuzzy Markov chain model with weights and its application in predicting the precipitation state

中长期降水量的预测是气象科学的一个难点问题,论文首先基于降水过程存在大量不确定性、不精确性的特点,应用模糊有序聚类的方法建立降水丰枯状况的分级标准;然后针对降水量为相依随机变量的特点,采取以规范化的各阶自相关系数为权重,用加权的马尔可夫链来预测未来降水的丰枯变化状况;在此基础上,根据模糊集理论中的级别特征值,计算出具体的降水量;最后以山西省河曲水文站近50年的降水资料为实例,对该方法进行了具体的应用,收到了较为满意的结果,从而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 future precipitation is a very difficult problem of meteorologic science, this paper firstly applied fuzzy sequential cluster method to set up the classification standard of precipitation state based on the fact that there are much uncertainty and imprecise characteristics in the precipitation course; then this paper presented a method which is called fuzzy Markov chain with weights to predicted the future precipitation state by regarding the standardized selfcoefficients as weights based on the special characteristics of precipitation being a dependent stochastic variable; on this base, a concrete precipitation was obtained by using the level characteristics value of fuzzy sets; and applied this method to a real hydrological observation station(Hequ) with nearly 50 years precipitation information in Shanxi Province at last, an ideal result was obtained, hence a practicable method for predicting future precipitation was put forward. 