气候变化领域集成服务门户(CCPortal): Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China

CCPortal

DOI	10.1016/j.atmosres.2020.104945
	Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China
	Liu J.; Hong J.; Mao F.; Gong W.; Shen L.; Liang S.; Chen J.
发表日期	2020
ISSN	0169-8095
卷号	241
英文摘要	Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China. © 2020 Elsevier B.V.
关键词	Sounding apparatus AMSU-A Data assimilation In-situ observations WRF-Chem WRFDA Weather forecasting air temperature AMSU atmospheric modeling data assimilation forecasting method particulate matter radiance relative humidity China
语种	英语
来源机构	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/132477
推荐引用方式 GB/T 7714	Liu J.,Hong J.,Mao F.,et al. Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China[J]. Atmospheric Research,2020,241.
APA	Liu J..,Hong J..,Mao F..,Gong W..,Shen L..,...&Chen J..(2020).Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China.,241.
MLA	Liu J.,et al."Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China".241(2020).