retrieval : embedding machine learning to simulate complex 2 physical parameters

19 Satellite remote sensing of PM2.5 mass concentration has become one of the most 20 popular atmospheric research aspects, resulting in the development of different models. 21 Among them, the semi-empirical physical approach constructs the transformation 22 relationship between the aerosol optical depth (AOD) and PM2.5 based on the optical 23 properties of particles, which has strong physical significance. Also, it performs the 24 PM2.5 retrieval independently of the ground stations. However, due to the complex 25 physical relationship, the physical parameters in the semi-empirical approach are 26 difficult to calculate accurately, resulting in relatively limited accuracy. To achieve the 27 optimization effect, this study proposes a method of embedding machine learning into 28 a semi-physical empirical model (RF-PMRS). Specifically, based on the theory of the 29 physical PM2.5 remote sensing approach (PMRS), the complex parameter (VEf, a 30 columnar volume-to-extinction ratio of fine particles) is simulated by the random forest 31 model (RF). Also, a fine mode fraction product with higher quality is applied to make 32 up for the insufficient coverage of satellite products. Experiments in North China show 33 that the surface PM2.5 concentration derived by RF-PMRS has an average annual value 34 of 57.92 μg/m3 versus the ground value of 60.23 μg/m3. Compared with the original 35 https://doi.org/10.5194/egusphere-2022-946 Preprint. Discussion started: 27 October 2022 c © Author(s) 2022. CC BY 4.0 License.

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