摘要
针对目前霾预报的重要性和霾客观预报准确率较低,提出基于多元逐步回归算法和卡尔曼滤波算法的霾客观预报订正技术。利用多元逐步回归法控制因变量的物理因子,建立能见度预报方程,利用卡尔曼滤波法根据实况资料对多元逐步回归算法中回归系数进行订正,建立霾客观预报订正模型。以北京站、广州站、南京站、杭州站四个站为例,对站点进行预报实验和检验。实验结果表明,与业务上运行的雾-霾数值预报系统(CUACE)进行对比,提出的多元逐步回归与卡尔曼滤波法的预报准确率有所提高。
Considering the importance of objective haze forecast and the low accuracy rate of haze forecast, a new haze objective forecast correction method based on the multiple stepwise regression algorithms and the Kalman filtering algorithm is proposed. The multiple stepwise regression method is used to control the physical factor of the dependent variable, and the visibility forecast equation is established. The Kalman filtering method is adopted to correct the regression coefficient in multivariate stepwise regression algorithm according to the actual data, and the haze objective forecast correction model is established. The experiments are carried out in Beijing, Guangzhou, Nanjing and Hangzhou. The experimental results show that comparing with the operational running fog - haze numerical prediction system （CUACE）, the prediction accuracy of the multiple stepwise regression and Kalman filtering method is improved.
作者
咸云浩
张恒德
谢永华
杨乐
Xian Yunhao;Zhang Hengde;Xie Yonghua;Yang Le(School of Computer and Software, Nanjing University of Information science and Technology, Nanjing 210044, China;Nation Meteorological Center of CMA, Beijing 100081, China;Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information and Technology, Nanjing 210044, China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2018年第4期1482-1489,共8页
Journal of System Simulation
基金
国家自然科学基金(61375030)
科技部大气污染专项(JFY2016ZY01002213)
关键词
多元逐步回归
卡尔曼滤波
预报模型
能见度
霾预报
multiple-stepwise regression
Kalman filtering
forecasting model
visibility
haze forecast
