27 Oct 2022
27 Oct 2022
Status: this preprint is open for discussion.

An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters

Caiyi Jin1, Qiangqiang Yuan1,3,4, Tongwen Li2, Yuan Wang1, and Liangpei Zhang3,5 Caiyi Jin et al.
  • 1School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
  • 2School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China
  • 3The Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
  • 4The Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan 430079, China
  • 5State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Abstract. Satellite remote sensing of PM2.5 mass concentration has become one of the most popular atmospheric research aspects, resulting in the development of different models. Among them, the semi-empirical physical approach constructs the transformation relationship between the aerosol optical depth (AOD) and PM2.5 based on the optical properties of particles, which has strong physical significance. Also, it performs the PM2.5 retrieval independently of the ground stations. However, due to the complex physical relationship, the physical parameters in the semi-empirical approach are difficult to calculate accurately, resulting in relatively limited accuracy. To achieve the optimization effect, this study proposes a method of embedding machine learning into a semi-physical empirical model (RF-PMRS). Specifically, based on the theory of the physical PM2.5 remote sensing approach (PMRS), the complex parameter (VEf, a columnar volume-to-extinction ratio of fine particles) is simulated by the random forest model (RF). Also, a fine mode fraction product with higher quality is applied to make up for the insufficient coverage of satellite products. Experiments in North China show that the surface PM2.5 concentration derived by RF-PMRS has an average annual value of 57.92 μg/m3 versus the ground value of 60.23 μg/m3. Compared with the original method, RMSE decreases by 39.95 μg/m3, and the relative deviation reduces by 44.87%. Moreover, validation at two AERONET sites presents a trend closer to the true values, with an R of about 0.80. This study is also a preliminary attempt to combine model-driven and data-driven models, laying a foundation for further atmospheric research on optimization methods.

Caiyi Jin et al.

Status: open (until 04 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2022-946', Adelaide Plaza., 27 Oct 2022 reply
    • AC2: 'Reply on CC1', Qianqqiang Yuan, 27 Oct 2022 reply
  • CC2: 'Comment on egusphere-2022-946', Adelaide Plaza., 27 Oct 2022 reply
    • AC1: 'Reply on CC2', Qianqqiang Yuan, 27 Oct 2022 reply
  • RC1: 'Comment on egusphere-2022-946', Anonymous Referee #1, 14 Nov 2022 reply
    • AC3: 'Reply on RC1', Qianqqiang Yuan, 14 Nov 2022 reply
  • RC2: 'Comment on egusphere-2022-946', Anonymous Referee #2, 29 Nov 2022 reply

Caiyi Jin et al.

Caiyi Jin et al.


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Short summary
The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.