GSTDTAP  > 气候变化
DOI10.1029/2019JD030421
Multiconstituent Data Assimilation With WRF-Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China
Ma, Chaoqun1; Wang, Tijian1; Mizzi, Arthur P.2; Anderson, Jeffrey L.3; Zhuang, Bingliang1; Xie, Min1; Wu, Rongsheng1
2019-07-16
发表期刊JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
ISSN2169-897X
EISSN2169-8996
出版年2019
卷号124期号:13页码:7393-7412
文章类型Article
语种英语
国家Peoples R China; USA
英文摘要

We use the Weather Research and Forecasting Model with the chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical weather forecasting/data assimilation system with multiconstituent data assimilation to investigate the improvement of air quality forecasts over eastern China. We assimilate surface in situ observations of sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O-3), carbon monoxide (CO), particulate matter with diameters less than 2.5 mu m (PM2.5) and 10 mu m (PM10), and satellite aerosol optical depth to adjust the related anthropogenic emissions as well as the chemical initial conditions. We validate our forecast results out to 72 hr by comparison with the in situ observations. Results show that updated emissions improve the model performance between 10% and 65% root mean square error reduction for the assimilated species except particulate matter with a diameter between 2.5 and 10 mu m (PM2.5-10), which is slightly improved due to the limited anthropogenic contribution to it. In a sensitivity experiment with a different update interval, the CO improvement is found to be sensitive to the cycling time used to update the CO emissions. In another sensitivity experiment when NO2 observations are not assimilated and nitrogen oxides (NOx) emission are adjusted by only O-3, NO2 forecasts show similar root mean square error improvement but have lower spatial correlation, indicating the value and limitation of the O-3-NOx cross-variable relationship.


领域气候变化
收录类别SCI-E
WOS记录号WOS:000477580200043
WOS关键词ENSEMBLE DATA ASSIMILATION ; VARIATIONAL DATA ASSIMILATION ; PHASE-SPACE RETRIEVALS ; KALMAN FILTER ; INITIAL CONDITIONS ; NOX EMISSIONS ; SATELLITE NO2 ; CO EMISSIONS ; MODEL ; OZONE
WOS类目Meteorology & Atmospheric Sciences
WOS研究方向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/185153
专题气候变化
作者单位1.Nanjing Univ, Sch Atmospher Sci, Nanjing, Jiangsu, Peoples R China;
2.Colorado Dept Publ Hlth & Environm, Air Pollut Control Div, Denver, CO USA;
3.Natl Ctr Atmospher Res, Computat & Informat Syst Lab, POB 3000, Boulder, CO 80307 USA
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GB/T 7714
Ma, Chaoqun,Wang, Tijian,Mizzi, Arthur P.,et al. Multiconstituent Data Assimilation With WRF-Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2019,124(13):7393-7412.
APA Ma, Chaoqun.,Wang, Tijian.,Mizzi, Arthur P..,Anderson, Jeffrey L..,Zhuang, Bingliang.,...&Wu, Rongsheng.(2019).Multiconstituent Data Assimilation With WRF-Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,124(13),7393-7412.
MLA Ma, Chaoqun,et al."Multiconstituent Data Assimilation With WRF-Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 124.13(2019):7393-7412.
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