气候变化领域集成服务门户(CCPortal): Impact of data assimilation and aerosol radiation interaction on Lagrangian particle dispersion modelling

CCPortal

DOI	10.1016/j.atmosenv.2020.118179
	Impact of data assimilation and aerosol radiation interaction on Lagrangian particle dispersion modelling
	Jia M.; Huang X.; Ding K.; Liu Q.; Zhou D.; Ding A.
发表日期	2021
ISSN	1352-2310
卷号	247
英文摘要	Lagrangian particle dispersion models (LPDMs) have been widely used in air pollution studies. However, substantial uncertainties still exist in LPDM simulations due to biased meteorological data, especially under stagnant and highly-polluted conditions. In this work, to better investigate the source contribution and formation of winter haze pollution in eastern China, we conduct a sensitivity study of WRF-FLEXPART by using different reanalysis data, applying observational meteorological nudging, and considering aerosols' radiative feedback on meteorology. We find that simulations driven by reanalysis datasets generally underestimate pollutant concentration, especially during periods with heavy haze pollution. The underestimation is directly caused by overestimated planetary boundary layer (PBL) height and lower PBL horizontal wind speeds. By assimilating meteorological data from surface and radiosonde observation, the WRF model can well represent the PBL dynamics and wind fields, especially those near the ground surface, which then substantially improves particle tracing in the LPDM. In addition, by including aerosols' radiative feedback in the WRF-Chem model, which significantly influences PBL evolution, the biases between LPDM modelling and observations are notably narrowed, particularly when the haze pollution is severe. Quantitatively, the accuracy increase of the simulations with aerosols’ radiative effect accounted for 48% of the improvement produced by assimilating meteorological data. Overall, meteorological input is of great importance in LPDM modelling. In regions with intensive pollution like China and India, applying observational data assimilation or considering the feedbacks of aerosols to meteorology serve as an effective way to reduce the biases of LPDMs and better understand the source contributions as well as the formation and accumulation of pollution. © 2020 Elsevier Ltd
关键词	Aerosols-PBL feedback Data assimilation Eastern China Lagrangian particle dispersion modelling WRF-FLEXPART
语种	英语
scopus关键词	Aerosols; Atmospheric movements; Boundary layers; Lagrange multipliers; Meteorology; Radiosondes; Aerosol-radiation interactions; Lagrangian particle dispersion model; Lagrangian particle dispersions; Meteorological input; Planetary boundary layers; Pollutant concentration; Radiosonde observations; Source contributions; Air pollution; carbon monoxide; aerosol; air pollution; air temperature; Article; atmospheric dispersion; atmospheric transport; boundary layer; China; concentration (parameter); data assimilation; haze; Lagrangian particle dispersion model; mathematical model; meteorology; particulate matter 2.5; priority journal; radiation; radiative forcing; simulation; wind speed; winter
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248611
作者单位	School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China; Collaborative Innovation Center of Climate Change, Nanjing, Jiangsu Province 210023, China
推荐引用方式 GB/T 7714	Jia M.,Huang X.,Ding K.,et al. Impact of data assimilation and aerosol radiation interaction on Lagrangian particle dispersion modelling[J],2021,247.
APA	Jia M.,Huang X.,Ding K.,Liu Q.,Zhou D.,&Ding A..(2021).Impact of data assimilation and aerosol radiation interaction on Lagrangian particle dispersion modelling.ATMOSPHERIC ENVIRONMENT,247.
MLA	Jia M.,et al."Impact of data assimilation and aerosol radiation interaction on Lagrangian particle dispersion modelling".ATMOSPHERIC ENVIRONMENT 247(2021).