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一种最优多模式集成方法在我国重污染区域PM2.5浓度预报中的应用

张天航 王继康 张恒德 张碧辉 吕梦瑶 江琪 迟茜元 栾天

张天航, 王继康, 张恒德, 张碧辉, 吕梦瑶, 江琪, 迟茜元, 栾天. 一种最优多模式集成方法在我国重污染区域PM2.5浓度预报中的应用[J]. 环境工程技术学报, 2019, 9(5): 520-530. doi: 10.12153/j.issn.1674-991X.2019.04.250
引用本文: 张天航, 王继康, 张恒德, 张碧辉, 吕梦瑶, 江琪, 迟茜元, 栾天. 一种最优多模式集成方法在我国重污染区域PM2.5浓度预报中的应用[J]. 环境工程技术学报, 2019, 9(5): 520-530. doi: 10.12153/j.issn.1674-991X.2019.04.250
ZHANG Tianhang, WANG Jikang, ZHANG Hengde, ZHANG Bihui, LÜ Mengyao, JIANG Qi, CHI Qianyuan, LUAN Tian. Application of a best multi-model ensemble method in PM2.5 forecast in heavily polluted regions of China[J]. Journal of Environmental Engineering Technology, 2019, 9(5): 520-530. doi: 10.12153/j.issn.1674-991X.2019.04.250
Citation: ZHANG Tianhang, WANG Jikang, ZHANG Hengde, ZHANG Bihui, LÜ Mengyao, JIANG Qi, CHI Qianyuan, LUAN Tian. Application of a best multi-model ensemble method in PM2.5 forecast in heavily polluted regions of China[J]. Journal of Environmental Engineering Technology, 2019, 9(5): 520-530. doi: 10.12153/j.issn.1674-991X.2019.04.250

一种最优多模式集成方法在我国重污染区域PM2.5浓度预报中的应用

doi: 10.12153/j.issn.1674-991X.2019.04.250
详细信息
    作者简介:

    张天航(1987—),男,工程师,博士,主要从事空气质量预报和检验研究, sharp@mail.iap.ac.cn

    通讯作者:

    王继康 E-mail: wjk_1990@126.com

  • 中图分类号: X513

Application of a best multi-model ensemble method in PM2.5 forecast in heavily polluted regions of China

More Information
    Corresponding author: Jikang WANG E-mail: wjk_1990@126.com
  • 摘要: 为了提高我国重污染区域PM2.5浓度预报准确率,基于4套国家级以及区域环境气象业务中心发展和维护的空气质量数值预报模式,通过均值集成、权重集成、多元线性回归集成和BP-ANNs集成分别建立集成预报,在实时预报效果评估基础上,建立了最优多模式集成预报。对2015—2016年预报效果进行评估,结果表明:相对于单个空气质量数值预报模式,均值和权重集成对预报偏差的改进幅度有限,但多元线性回归、BP-ANNs和最优集成能较大幅度降低预报偏差;最优集成预报与观测值间的归一化平均偏差(NMB)和均方根误差(RMSE)分别为-10%~10%和10~70 μg/m 3,且在更多的站点表现出强相关性,但依然低估了高污染等级的PM2.5浓度。对2018年2月25日—3月4日京津冀地区污染过程进行评估,结果表明:最优集成能较好预报出该过程中PM2.5浓度的变化趋势和量级;在北京、石家庄和郑州3个代表城市中,预报和观测值间的NMB和相关系数(R)分别为-26%~-4%和0.49~0.77;最优集成对轻度污染及中度污染的TS评分为0.39~0.73,重度污染及以上TS评分为0.13~0.30,能为预报员提供客观参考,但对污染峰值的预报能力还需进一步改进。

     

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出版历程
  • 收稿日期:  2019-01-21
  • 刊出日期:  2019-09-20

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