气候变化领域集成服务门户(CCPortal): Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three-Dimensional Variational Data Fusion Method

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DOI	10.1029/2020JD033599
	Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three-Dimensional Variational Data Fusion Method
	Zhang X.; Fung J.C.H.; Lau A.K.H.; Zhang S.; Huang W.
发表日期	2021
ISSN	2169897X
卷号	126 期号:6
英文摘要	The spatiotemporal concentration of multiple pollutants is crucial information for pollution control strategies to safeguard public health. Despite considerable efforts, however, significant uncertainty remains. In this study, a three-dimensional variational model is coupled with a data assimilation (DA) system to analyze the spatiotemporal variation of PM2.5 for the whole of China. Monthly simulations of six sensitivity scenarios in different seasons, including different assimilation cycles, are carried out to assess the impact of the assimilation frequency on the PM2.5 simulations and the model simulation accuracy afforded by DA. The results show that the coupled system provides more reliable initial fields to substantially improve the model performance for PM2.5, PM10, and O3. Higher assimilation frequency improves the simulation in all geographic areas. Two statistical indicators—the root mean square error and the correlation coefficient of PM2.5 mass concentrations in the analysis field—are improved by 12.19 µg/m3 (33%) and 0.21 (48%), respectively. Although the 24-h assimilation cycle considerably improves the model, assimilation at a 6-h cycle raises the performance for PM2.5 to the performance goal level. The analysis shows that assimilating at a 24-h cycle diminishes over time, whereas the positive impact of the 6-h cycle persists. One pivotal finding is that the assimilation of PM2.5 in the outermost domain results in a substantial improvement in PM2.5 prediction for the innermost domain, which is a potential alternative method to the existing domain-wide data fusion algorithm. The effect of assimilation varies among topographies, a finding that provides essential support for further model development. © 2021. American Geophysical Union. All Rights Reserved.
英文关键词	assimilation cycles; China; coupling system; data fusion; spatiotemporal model
语种	英语
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/185399
作者单位	School of Management, Xi'an Jiaotong University, Xi'an, China; Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong; Key Laboratory of Physical Oceanography, The College of Oceanic and Atmospheric Sciences & Institute for Advanced Ocean Study/Frontiers Science Center for Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China, Qingdao, China; Qingdao Pilot National Laboratory for Marine Science and Technology, Qingdao, China; College of Business, Southern University of Science and Technology, Shenzhen, China
推荐引用方式 GB/T 7714	Zhang X.,Fung J.C.H.,Lau A.K.H.,et al. Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three-Dimensional Variational Data Fusion Method[J],2021,126(6).
APA	Zhang X.,Fung J.C.H.,Lau A.K.H.,Zhang S.,&Huang W..(2021).Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three-Dimensional Variational Data Fusion Method.Journal of Geophysical Research: Atmospheres,126(6).
MLA	Zhang X.,et al."Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three-Dimensional Variational Data Fusion Method".Journal of Geophysical Research: Atmospheres 126.6(2021).