摘要
针对当前实时气温质量控制存在的问题,提出了一种基于k-means聚类的动态控制算法。算法首先用k-means方法将区域内各测温点划分为若干气温相似的聚类,然后分别对各聚类内的点进行离群率和离群速度的判别,以确定各点的质量。与传统气温质量控制方法相比,该算法采用单点气温与整体气温相比较的思想,不需要预先设置气温参考极值,因而更具有实用性和科学性。而且,算法的复杂度较低,适合较大气温输入数据集的计算。
Aiming at some current problems of quality control in real-time temperature measurements, a dynamic method based on k-means clustering algorithm is proposed. The algorithm first divides the temperature sample points in the region into a number of clusters according to their similar temperatures by k-means, and then for each sample point in the clusters the algorithm checks its outlier ratio and outlier speed in order to determine the final quality of the point. Compared with conventional temperature quality control methods, the algorithm uses an idea of the comparison of the single-point temperature with the overall temperature, and it does not need to pre-set the reference temperature value, thus it is a more real- time and scientific temperature quality control method. Also, the complexity of the algorithm is low, and it is proper for the calculation of large temperature input data sets.
出处
《气象》
CSCD
北大核心
2012年第10期1295-1300,共6页
Meteorological Monthly
基金
山东省气象局重点项目(2007sdqxz20)资助
