EGU23-11352, updated on 10 Dec 2024
https://doi.org/10.5194/egusphere-egu23-11352
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Impacts of meteorological biases on forecasting surface PM2.5 concentration over the Beijing-Tianjing-Hebei region

Qiuyan Du, Chun Zhao, Jiawang Feng, Zining Yang, Jiamin Xu, Jun Gu, Mingshuai Zhang, Mingyue Xu, and Shengfu Lin
Qiuyan Du et al.
  • University of Science and Technology of China, school of Earth and Space Science, Geophysics, China (sadu@mail.ustc.edu.cn)

Forecast biases in the meteorological fields have long been recognized as the main limitation on accuracy and predictability of air quality forecast. However, the quantitative studies on the impacts of meteorological forecast biases on air quality forecast are insufficient, and its mechanisms in different seasons are still unclear. In this study, series of forecasts from 2-Day (24 ~ 48-hour) to 7-Day (144 ~168-hour) for January, April, July, and October of 2018 are conducted over the Beijing-Tianjing-Hebei (BTH) region and the impacts of meteorological forecast bias on surface PM2.5 concentration forecast at each leading-time are analyzed. The results show that the forecast biases of surface PM2.5 concentration caused by meteorological forecast biases keep increasing with the increasing leading-time in all seasons. The impacts of meteorological forecast biases are strongest in spring, with the forecast bias of PM2.5 concentrations up to 187% in the 7-Day forecast, followed by autumn (123%), summer (112%) and winter (80%). By the contribution analysis of relevant processes and their changes with the increase of leading-time, it is found that the dry deposition, transport and PBL mixing are the main processes contributing to the growth of forecast bias of surface PM2.5 concentrations. In addition, the correspondence between the contributions of relevant processes and the meteorological factor are examined. It is found that the changes of contributions of processes are closely related to the difference in the forecasted meteorological factors such as surface winds, wind fields at 850hPa, PBL height, shortwave radiation and precipitations at each leading-time. This study highlights the importance of meteorological forecast bias in surface PM2.5 concentration forecasting, and provides useful information for the improvement of air quality forecast accuracy and supports the policies of air pollution controlling.

How to cite: Du, Q., Zhao, C., Feng, J., Yang, Z., Xu, J., Gu, J., Zhang, M., Xu, M., and Lin, S.: Impacts of meteorological biases on forecasting surface PM2.5 concentration over the Beijing-Tianjing-Hebei region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11352, https://doi.org/10.5194/egusphere-egu23-11352, 2023.