Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 14
Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4137-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4137-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 14
Development and technical paper
 | 
24 Jul 2023
Development and technical paper |  | 24 Jul 2023

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

Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang

Data sets

Physical and deep learning retrieved fine mode fraction (Phy-DL FMF) Xing Yan https://doi.org/10.5281/zenodo.5105617

MCD19A2 MODIS/Terra+Aqua Aerosol Optical Thickness Daily L2G Global 1km SIN Grid A. Lyapustin and Y. Wang https://doi.org/10.5067/MODIS/MCD19A2.006

ERA5 hourly data on single levels from 1979 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horanyi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.adbb2d47

Model code and software

An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: using random forest model to simulate the complex parameter Caiyi Jin https://doi.org/10.5281/zenodo.7183822

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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.
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