Application of Multiple-Stepwise and Kalman Filtering in Haze Forecast

doi:10.16182/j.issn1004731x.joss.201804033

Abstract

Abstract: 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.

Key words: multiple-stepwise regression, Kalman filtering, forecasting model, visibility, haze forecast

CLC Number:

TP391.9

Xian Yunhao, Zhang Hengde, Xie Yonghua, Yang Le. Application of Multiple-Stepwise and Kalman Filtering in Haze Forecast[J]. Journal of System Simulation, 2018, 30(4): 1482-1489.

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