Haze forecast based on time series analysis and Kalman filtering

doi:10.11772/j.issn.1001-9081.2017.11.3311

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (11): 3311-3316.DOI: 10.11772/j.issn.1001-9081.2017.11.3311

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Haze forecast based on time series analysis and Kalman filtering

ZHANG Hengde¹, XIAN Yunhao², XIE Yonghua^2,3, YANG Le², ZHANG Tianhang¹

1. National Meteorological Center, China Meteorological Administration, Beijing 100081, China;
2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China;
3. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information and Technology, Nanjing Jiangsu 210044, China

Received:2017-05-22 Revised:2017-08-16 Online:2017-11-10 Published:2017-11-11
Supported by:
The work is partially supported by the National Key R&D Program (2016YFC0203301).

基于时间序列分析和卡尔曼滤波的霾预报技术

张恒德¹, 咸云浩², 谢永华^2,3, 杨乐², 张天航¹

1. 中国气象局国家气象中心, 北京 100081;
2. 南京信息工程大学计算机与软件学院, 南京 210044;
3. 南京信息工程大学江苏省网络监控中心, 南京 210044

通讯作者: 咸云浩
作者简介:张恒德(1977-),男,安徽含山人,高级工程师,博士,主要研究方向:环境气象、灾害性天气;咸云浩(1991-),男,江苏淮安人,硕士研究生,主要研究方向:人工智能、机器学习、污染物浓度预报;谢永华(1976-),男,江苏靖江人,教授,博士,主要研究方向:人工智能、图像处理;杨乐(1990-),男,江苏淮安人,硕士研究生,主要研究方向:人工智能、机器学习、污染物浓度预报;张天航(1987-),男,湖北荆门人,工程师,博士,主要研究方向:数值环境气象预报。
基金资助:
国家重点研发计划项目（2016YFC0203301）。

Abstract

Abstract: In order to improve the accuracy of haze forecast and resolve the time lagging and low accuracy of temporal model, a mixed forecast method based on time series analysis and Karman filter was proposed. Firstly, the stability of time series was tested by graph analysis and eigenvalue analysis (ADF). Unstable time series were converted to stable ones by differential operation. A statistical function was established based on the stable time series. And then, the obtained model equations were used as the state and observation equation for Kalman filtering. Final haze forecast was based on recursion by Karman filtering. The experimental results showed that the accuracy of haze forecast is effectively improved by the mixed forecast method based on time series analysis and Karman filtering.

Key words: time series, Kalman filtering, visibility, haze, forecasting model

摘要： 为了提高霾预报的准确率，解决时序模型的预测延时和准确率不高的问题，提出了一种基于时间序列分析和卡尔曼滤波相结合的混合霾预报算法。首先，利用图检验法和单位根检验法（ADF）检验时间序列的平稳性，通过差分运算将非平稳序列转化成平稳序列，对转化后的平稳序列进行建模；然后，将得到的模型方程作为卡尔曼滤波的状态方程和观测方程，依靠卡尔曼滤波递推性进行预报。实验结果表明，采用时间序列分析和卡尔曼滤波相结合的混合霾客观预报订正方法能有效提高霾预测精度。

关键词: 时间序列, 卡尔曼滤波, 能见度, 霾, 预报模型

CLC Number:

TP391.9

ZHANG Hengde, XIAN Yunhao, XIE Yonghua, YANG Le, ZHANG Tianhang. Haze forecast based on time series analysis and Kalman filtering[J]. Journal of Computer Applications, 2017, 37(11): 3311-3316.

张恒德, 咸云浩, 谢永华, 杨乐, 张天航. 基于时间序列分析和卡尔曼滤波的霾预报技术[J]. 计算机应用, 2017, 37(11): 3311-3316.

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Haze forecast based on time series analysis and Kalman filtering

基于时间序列分析和卡尔曼滤波的霾预报技术

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