Improving Spatiotemporal Fine Particulate Matter from a Data Assimilation Approach
- 1School of Management, Xi’an Jiaotong University, Xi’an, China (xzhangbg@connect.ust.hk).
- 2Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China.
- 3Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China.
- 4Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
- 5Key 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.
- 6Qingdao Pilot National Laboratory for Marine Science and Technology, Qingdao, China.
- 7College of Business, Southern University of Science and Technology, Shenzhen, China.
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 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 data assimilation. 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-hour assimilation cycle considerably improves the model, assimilation at a 6-hour cycle raises the performance for PM2.5 to the performance goal level. The analysis shows that assimilating at a 24-hour cycle diminishes over time, whereas the positive impact of the 6-hour 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.
How to cite: Zhang, X., Fung, J., Lau, A., Zhang, S., and Huang, W. (.: Improving Spatiotemporal Fine Particulate Matter from a Data Assimilation Approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-661, https://doi.org/10.5194/egusphere-egu22-661, 2022.
