Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three-Dimensional Variational Data Fusion Method

Zhang，Xuguo1,2,3; Fung，Jimmy C.H.2,3; Lau，Alexis K.H.2,4; Zhang，Shaoqing5,6; Huang，Wei1,7

2021-03-27

10.1029/2020JD033599

JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES   影响因子和分区

2169-897X

2169-8996

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 PM 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 PM 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 PM, PM, and O. Higher assimilation frequency improves the simulation in all geographic areas. Two statistical indicators—the root mean square error and the correlation coefficient of PM mass concentrations in the analysis field—are improved by 12.19 µg/m (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 PM 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 PM in the outermost domain results in a substantial improvement in PM 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.

assimilation cycles China coupling system data fusion spatiotemporal model

SCI

英语

其他

NSFC Key Research Project[91644221] ; Research Grants Council of Hong Kong Government[T24/504/17]

Meteorology & Atmospheric Sciences

Meteorology & Atmospheric Sciences

WOS:000634788300019

AMER GEOPHYSICAL UNION

GEOSCIENCES

2-s2.0-85103018500

Scopus

1.School of Management,Xi'an Jiaotong University,Xi'an,China
2.Division of Environment and Sustainability,The Hong Kong University of Science and Technology,Hong Kong
3.Department of Mathematics,The Hong Kong University of Science and Technology,Hong Kong
4.Department of Civil and Environmental Engineering,The Hong Kong University of Science and Technology,Hong Kong
5.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
6.Qingdao Pilot National Laboratory for Marine Science and Technology,Qingdao,China
7.College of Business,Southern University of Science and Technology,Shenzhen,China

Zhang，Xuguo,Fung，Jimmy C.H.,Lau，Alexis K.H.,et al. Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three-Dimensional Variational Data Fusion Method[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2021,126(6).

Zhang，Xuguo,Fung，Jimmy C.H.,Lau，Alexis K.H.,Zhang，Shaoqing,&Huang，Wei.(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).

Zhang，Xuguo,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).