多模式集成方法在安徽地区PM_(2.5)预报中的应用研究  

Application study of multi-mode integration method in PM forecast in Anhui Province

作　　者：杨关盈[1,2] 石春娥 邓学良[1,2] 翟菁 霍彦峰[1,2] 于彩霞 赵倩[1,2] YANG Guanying;SHI Chun'e;DENG Xueliang;ZHAI Jing;HUO Yanfeng;YU Caixia;ZHAO Qian(Anhui Key Lab of Atmospheric Science and Remote Sensing,Anhui Meteorology Institute,Hefei 230031;Shouxian National Climatology Observatory and Huai River Basin Typical Farm Eco-meteorological Experiment Field of CMA,Shouxian 232200)

机构地区：[1]安徽省气象科学研究所,大气科学与卫星遥感重点实验室,合肥230031 [2]寿县国家气候观象台,中国气象局淮河流域典型农田生态气象野外科学试验基地,寿县232200

出　　处：《环境科学学报》2021年第3期806-816,共11页Acta Scientiae Circumstantiae

基　　金：安徽省气象局科技发展基金(No.KM201804);国家自然科学基金青年基金(No.41705014);安徽省公益性研究联动计划项目(No.1604f0804003)。

摘　　要：分别采用算术平均、权重平均、多元线性回归和神经网络的集成方法,对3种空气质量模式在安徽地区2017年2月—2018年2月PM_(2.5)预报结果进行集成释用.结果表明:各模式和订正产品的预报值与实况值之间均能达到显著相关,相较于WRF-Chem,多元线性回归的均方根误差(RMSE)下降了21.7%,归一化平均偏差(NMB)下降了6%,且在16个地市中NMB均下降至-25%~25%之间;从不同时次的预报效果来看,在3个代表性城市(淮北、合肥和芜湖),多元线性回归均能大幅度降低RMSE和NMB,但从时间和空间效果来看,其对于提升预报值与实况值之间的相关性方面,略差于权重平均的集成方法;多元线性回归方法对于重污染天气PM_(2.5)预报评分(TS)最高,为0.46.该方法能较为有效地提升不同模式的预报效果,可为重污染天气预报预警提供参考.The integration methods of arithmetic average, weighted average, multiple linear regression and neural network were used to integrate and interpret the PM_(2.5) forecasts at 16 cities in Anhui province by three air quality models during the period from February 2017 to February 2018. The results show that the forecasted values of the three models and integrated products were highly correlated with observations. Compared with WRF-Chem, the RMSE of multiple regression decreased by 21.7%, NMB decreased by 6%, and NMB dropped to between-25% and 25% in all cities. Multiple regression significantly reduced RMSE and NMB in the three representative cities, Huaibei, Hefei and Wuhu;however, in terms of the spatiotemporal correlation, it was slightly worse than the weighted average for improving the correlation between the forecast value and the observed value. As for the forecast of PM_(2.5) heavy pollution weather, the multiple regression method performed best with the highest Ts, indicating that this integrated method can improve the forecasting effect of different models effectively, and provide a reference for early warning of heavy pollution weather.

关 键 词：集成预报 安徽 空气质量模式 多元回归 

分 类 号：X16[环境科学与工程—环境科学] X823