Preprints
https://doi.org/10.5194/acp-2022-301
https://doi.org/10.5194/acp-2022-301
 
17 May 2022
17 May 2022
Status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

Four-dimensional Variational Assimilation for SO2 Emission and its Application around the COVID-19 lockdown in the spring 2020 over China

Yiwen Hu1,2, Zengliang Zang2, Xiaoyan Ma1, Yi Li2, Yanfei Liang3, Wei You2, Xiaobin Pan2, and Zhijin Li4 Yiwen Hu et al.
  • 1Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing, 210044, China
  • 2College of Meteorology and Oceanography, National University of Defense Technology, Changsha, 410073, China
  • 3No. 32145 Unit of PLA, Xinxiang, 453000, China
  • 4University of California Los Angeles, California, 91109, USA

Abstract. Emission inventories are essential for modeling studies and pollution control, but traditional emission inventories have large uncertainties and are often not real-time because they are highly human resource demanding to develop. In this study, a four-dimensional variational assimilation (4DVAR) system was developed to optimize sulfur dioxide (SO2) emissions by assimilating hourly SO2 concentrations. An observation system simulation experiment was conducted to evaluate the performance of the system. This evaluation indicates that the 4DVAR system can effectively reduce the uncertainty in SO2 emissions at a regional level. The 4DVAR system was then applied to optimize SO2 emissions during the early period of COVID-19 (from January 17 to February 6, 2020), and the reduction in SO2 emissions was assessed in comparison with the 2016 inventory. The hourly surface SO2 observations were assimilated. The results show that the emissions in 2020 decreased by 18.0 % compared with those in 2019, indicating a significant decrease between 2019 and 2020 due to the COVID-19 related lockdown. Three forecast experiments were conducted using emissions in 2016, 2019, and 2020 to demonstrate the effects of optimized emissions. The root mean square error in 2020 decreased by 47.9 % and the correlation coefficient increased by 300.0 % compared with 2016 emissions. This suggests that the 4DVAR system can effectively optimize emissions to describe the actual change in SO2 emissions during special events and improve the forecast skill.

Yiwen Hu et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-301', Anonymous Referee #3, 25 May 2022
    • AC1: 'Reply on RC1', Zengliang Zang, 19 Aug 2022
  • RC2: 'Comment on acp-2022-301', Anonymous Referee #1, 01 Jun 2022
    • AC2: 'Reply on RC2', Zengliang Zang, 19 Aug 2022
  • RC3: 'Comment on acp-2022-301', Anonymous Referee #2, 03 Jun 2022
    • AC3: 'Reply on RC3', Zengliang Zang, 19 Aug 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-301', Anonymous Referee #3, 25 May 2022
    • AC1: 'Reply on RC1', Zengliang Zang, 19 Aug 2022
  • RC2: 'Comment on acp-2022-301', Anonymous Referee #1, 01 Jun 2022
    • AC2: 'Reply on RC2', Zengliang Zang, 19 Aug 2022
  • RC3: 'Comment on acp-2022-301', Anonymous Referee #2, 03 Jun 2022
    • AC3: 'Reply on RC3', Zengliang Zang, 19 Aug 2022

Yiwen Hu et al.

Yiwen Hu et al.

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Short summary
This study developed a four-dimensional variational assimilation (4DVAR) system based on WRF-Chem to optimize SO2 emissions. The system was applied to investigate the changes in SO2 emission in China during the COVID-19 lockdown, with special focus on Central China, by assimilating surface hourly SO2 observations. The results showed that the 4DVAR system can effectively optimize the emissions to describe the actual change in SO2 emissions during special events and improve the forecast skill.
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