Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system
文献类型：期刊论文
| 作者 | Kong, Yawen1; Sheng, Lifang2,3; Li, Yanpeng4; Zhang, Weihang2; Zhou, Yang2; Wang, Wencai2; Zhao, Yuanhong2 |
| 刊名 | ATMOSPHERIC RESEARCH
 |
| 出版日期 | 2021-02-01 |
| 卷号 | 249页码:14 |
| ISSN号 | 0169-8095 |
| 关键词 | Data assimilation WRF-Chem PM2.5 forecast LETKF Air pollution |
| DOI | 10.1016/j.atmosres.2020.105366 |
| 通讯作者 | Kong, Yawen(kongyw.16b@igsnrr.ac.cn) |
| 英文摘要 | To improve the PM2.5 forecast during severe haze episodes, we developed a data assimilation system based on the four-dimensional local ensemble transform Kalman filter (4D-LETKF) and the WRF-Chem model to assimilate surface PM2.5 observations. The data assimilation system was successful in optimizing the initial PM2.5 mass concentrations. The root-mean-square error (RMSE) of the initial PM2.5 concentrations after assimilation decreased at 76.75% of the stations and the RMSE reduction exceeds 30% at 20.7% of the stations. The correlation coefficients for the PM2.5 analyses increased by more than 0.3 at 33% of the stations. The forecasts for the spatial distribution and evolution of the haze were improved remarkably after assimilation while the forecasts without assimilation usually significantly underestimated the PM2.5 mass concentrations during the severe haze episodes. The RMSE of the 24-h forecasts after assimilation can be reduced by 32.02% in the polluted regions. During haze episodes, the 48-h forecasts after assimilation can benefit from the assimilation to a similar extent with the 24-h forecasts. Both the forecast accuracy and the duration of assimilation benefits were improved remarkably which demonstrate the effectiveness of the 4D-LETKF-PM2.5 data assimilation system, and further experiments are to be conducted to improve its performance. |
| 资助项目 | National Natural Science Foundation of China[41675146] |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCIENCE INC |
| WOS记录号 | WOS:000596915000002 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/137696]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Kong, Yawen |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Ocean Univ China, Coll Ocean & Atmospher Sci, Dept Marine Meteorol, Qingdao 266100, Peoples R China 3.Ocean Univ China, Ocean Atmosphere Interact & Climate Lab, Key Lab Phys Oceanog, Qingdao 266100, Peoples R China 4.China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Peoples R China |
| 推荐引用方式 GB/T 7714 | Kong, Yawen,Sheng, Lifang,Li, Yanpeng,et al. Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system[J]. ATMOSPHERIC RESEARCH,2021,249:14. |
| APA | Kong, Yawen.,Sheng, Lifang.,Li, Yanpeng.,Zhang, Weihang.,Zhou, Yang.,...&Zhao, Yuanhong.(2021).Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system.ATMOSPHERIC RESEARCH,249,14. |
| MLA | Kong, Yawen,et al."Improving PM2.5 forecast during haze episodes over China based on a coupled 4D-LETKF and WRF-Chem system".ATMOSPHERIC RESEARCH 249(2021):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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