Preprints
https://doi.org/10.5194/acp-2022-744
https://doi.org/10.5194/acp-2022-744
 
12 Dec 2022
12 Dec 2022
Status: this preprint is currently under review for the journal ACP.

Improving Ozone Simulations in Asia via Multisource Data Assimilation: Results from an Observing System Simulation Experiment with GEMS Geostationary Satellite Observations

Lei Shu1,2, Lei Zhu2,3, Juseon Bak4, Peter Zoogman5, Han Han1, Song Liu2, Xicheng Li2, Shuai Sun2, Juan Li2, Yuyang Chen2, Dongchuan Pu2, Xiaoxing Zuo2, Weitao Fu2, Xin Yang2,3, and Tzung-May Fu2,3 Lei Shu et al.
  • 1School of Geographical Sciences, Fujian Normal University, Fuzhou, Fujian 350007, China
  • 2School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
  • 3Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen, Guangdong 518055, China
  • 4Institute of Environmental Studies, Pusan National University, Busan 46241, South Korea
  • 5Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts 02138, United States

Abstract. The applications of geostationary (GEO) satellite measurements at an unprecedented spatial and temporal resolution from the Geostationary Environment Monitoring Spectrometer (GEMS) for monitoring and forecasting the alarming ozone pollution in Asia through data assimilation remain at the early stage. Here we investigate the benefit of multiple ozone observations from GEMS geostationary satellite, low Earth orbit (LEO) satellite, and surface networks on summertime ozone simulations through individual or joint data assimilation, built on our previous Observing System Simulation Experiment (OSSE) framework (Shu et al., 2022). We find that data assimilation better represents the exceedance, spatial patterns, and diurnal variations of surface ozone, with a regional mean negative bias reduction from 2.1 to 0.2–1.2 ppbv in ozone simulations as well as precision improvements of a root-mean-square error (RMSE) of by 5–69 % in most Asian countries. Furthermore, the joint assimilation of GEMS and surface observations performs the best. GEMS also brings direct added value for better reproducing ozone vertical distributions, especially in the middle to upper troposphere at low latitudes, but may mask the added value of LEO measurements, which are crucial to constrain surface and upper tropospheric ozone simulations when observations from other platforms are inadequate. Our study provides a valuable reference for ozone data assimilation as multisource observations become gradually available in the era of GEO satellites.

Lei Shu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-744', Anonymous Referee #2, 31 Dec 2022
  • RC2: 'Comment on acp-2022-744', Anonymous Referee #1, 02 Jan 2023

Lei Shu et al.

Viewed

Total article views: 390 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
287 94 9 390 27 3 5
  • HTML: 287
  • PDF: 94
  • XML: 9
  • Total: 390
  • Supplement: 27
  • BibTeX: 3
  • EndNote: 5
Views and downloads (calculated since 12 Dec 2022)
Cumulative views and downloads (calculated since 12 Dec 2022)

Viewed (geographical distribution)

Total article views: 374 (including HTML, PDF, and XML) Thereof 374 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 01 Feb 2023
Download
Short summary
We quantify the benefit of multisource observations (GEMS, LEO satellite, and surface) on ozone simulations in Asia. Data assimilation improves the monitoring of exceedance, spatial pattern, and diurnal variation of surface ozone, with the regional mean bias reduced from –2.1 to –0.2 ppbv. Data assimilation also better represents ozone vertical distributions in the middle to upper troposphere at low latitudes. Our results offer a valuable reference for future ozone simulations.
Altmetrics