Abstract::In the present study, a predictive technique incorporating driving forces was used to predict the atmospheric aerosol number concentration at the foot of Huangshan mountain which extractedthe driving force from the observation data by Slow Feature Analysis. To appraise its effectiveness, some prediction experiments were carried out using the hourly atmospheric aerosol number concentration in Huangshan. When the forecast step was 1, the correlation coefficient between the stationary model predictions and observation data was 0.6982; the correlation coefficent between the single external forcing model and observation data was 0.7390; the correlation coefficient between the double external forcing model and observation data was 0.7475. Adding external forcing can effectively improve the forecasting skills
陈潇潇, 王革丽, 金莲姬. 包含外强迫因子的大气气溶胶数浓度的预测[J]. 中国环境科学, 2015, 35(3): 694-699.
CHEN Xiao-Xiao, WANG Ge-Li, JIN Lian-Ji. Prediction of the atmospheric aerosol number concentration using a new predictive technique. CHINA ENVIRONMENTAL SCIENCECE, 2015, 35(3): 694-699.