马尔科夫链在年降水量预测中的应用

Application of the Markov chain in the forecast of annual precipitation

年降水量是目前水文和气象预测中非常重要的一项工作,是指导工农业生产以及经济发展策略的主要影响因素,其预测结果能够为气象、水文研究以及各种生产活动中决策的制定提供重要的数据支持。马尔科夫链是随机过程中的一种,在已知条件下对某个对象的未来进行预测,但是其未来产生的结果和过去所产生的结果之间相互独立,二者互补影响。这种随机过程的特征跟每年中降水量的特征完全相符,所以本文拟采用马尔科夫链,通过对降水量模型的构建,探讨了马尔科夫链在某个地区年降水量预测中的作用。

The annual precipitation is a very important work in the current hydrological and meteorological forecast, which is the main factor affecting the production of industry and agriculture as well as the guidance of economic development strategy. The prediction results can provide important data support for making decision in the meteorological, hydrological research and various production activities. Markov chain is a random process to forecast an object in the future in one known condition. But its future and past result is independent of each other with complementary effect. The precipitation feature of this stochastic process with the characteristics in each year is fully consistent. Through constructing the model of precipitation, this paper uses Markov chain to discuss its function in one area of annual precipitation forecast.