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Improved Modeling of Spatiotemporal Variations of Fine Particulate Matter Using a Three‐Dimensional Variational Data Fusion Method
Author(s) -
Zhang Xuguo,
Fung Jimmy C. H.,
Lau Alexis K. H.,
Zhang Shaoqing,
Huang Wei
Publication year - 2021
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2020jd033599
Subject(s) - data assimilation , mean squared error , environmental science , assimilation (phonology) , particulates , meteorology , correlation coefficient , pollution , atmospheric sciences , computer science , statistics , mathematics , physics , chemistry , ecology , linguistics , philosophy , organic chemistry , biology
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 2.5 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 PM 2.5 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 PM 2.5 , PM 10 , and O 3 . Higher assimilation frequency improves the simulation in all geographic areas. Two statistical indicators—the root mean square error and the correlation coefficient of PM 2.5 mass concentrations in the analysis field—are improved by 12.19 µg/m 3 (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 2.5 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 PM 2.5 in the outermost domain results in a substantial improvement in PM 2.5 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.