GSTDTAP  > 地球科学
DOI10.5194/acp-17-4837-2017
Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter
Peng, Zhen1,2; Liu, Zhiquan2; Chen, Dan2,3; Ban, Junmei2
2017-04-13
发表期刊ATMOSPHERIC CHEMISTRY AND PHYSICS
ISSN1680-7316
EISSN1680-7324
出版年2017
卷号17期号:7
文章类型Article
语种英语
国家Peoples R China; USA
英文摘要

In an attempt to improve the forecasting of atmospheric aerosols, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions (ICs) and emission input. The forecast model, which was expanded by combining the Weather Research and Forecasting with Chemistry (WRF-Chem) model and a forecast model of emission scaling factors, generated both chemical concentration fields and emission scaling factors. The forecast model of emission scaling factors was developed by using the ensemble concentration ratios of the WRF-Chem forecast chemical concentrations and also the time smoothing operator. Hourly surface fine particulate matter (PM2.5) observations were assimilated in this system over China from 5 to 16 October 2014. A series of 48 h forecasts was then carried out with the optimized initial conditions and emissions on each day at 00:00UTC and a control experiment was performed without data assimilation. In addition, we also performed an experiment of pure assimilation chemical ICs and the corresponding 48 h forecasts experiment for comparison. The results showed that the forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment. In the Yangtze River delta (YRD) and the Pearl River delta (PRD) regions, large reduction of the root-mean-square errors (RMSEs) was obtained for almost the entire 48 h forecast range attributed to assimilation. In particular, the relative reduction in RMSE due to assimilation was about 37.5% at nighttime when WRF-Chem performed comparatively worse. In the Beijing-Tianjin-Hebei (JJJ) region, relatively smaller improvements were achieved in the first 24 h forecast but then no improvements were achieved afterwards. Comparing to the forecasts with only the optimized ICs, the forecasts with the joint adjustment were always much better during the night in the PRD and YRD regions. However, they were very similar during daytime in both regions. Also, they performed similarly for almost the entire 48 h forecast range in the JJJ region.


领域地球科学
收录类别SCI-E
WOS记录号WOS:000403956400001
WOS关键词DATA ASSIMILATION SYSTEM ; AEROSOL OPTICAL DEPTH ; VARIATIONAL DATA ASSIMILATION ; ATMOSPHERIC DATA ASSIMILATION ; OPTIMAL INTERPOLATION METHOD ; MODIS AOD ASSIMILATION ; PM10 DATA ASSIMILATION ; TRANSPORT MODEL ; GOCART MODEL ; GLOBAL-MODEL
WOS类目Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/30569
专题地球科学
作者单位1.Nanjing Univ, Sch Atmospher Sci, Nanjing, Jiangsu, Peoples R China;
2.Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA;
3.CMA, Inst Urban Meteorol, Beijing, Peoples R China
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GB/T 7714
Peng, Zhen,Liu, Zhiquan,Chen, Dan,et al. Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2017,17(7).
APA Peng, Zhen,Liu, Zhiquan,Chen, Dan,&Ban, Junmei.(2017).Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter.ATMOSPHERIC CHEMISTRY AND PHYSICS,17(7).
MLA Peng, Zhen,et al."Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter".ATMOSPHERIC CHEMISTRY AND PHYSICS 17.7(2017).
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