Acta Optica Sinica, Volume. 45, Issue 12, 1201008(2025)
Atmospheric Model Construction Method Based on K‑means Clustering and Random Forest Regression
Fig. 4. Classification of temperature and pressure over eastern China in different months
Fig. 5. Temperature patterns of different categories in the eastern China. (a) January; (b) April; (c) July; (d) October
Fig. 6. Pressure profiles of different ategories in the eastern China region in January
Fig. 7. Regional mean pressure profiles for representative months. (a) 1–20 km; (b) 7–13 km
Fig. 8. Classification results of water vapor profiles in the eastern China region for different months
Fig. 9. Water vapor patterns of different categories in the eastern China. (a) January; (b) April; (c) July; (d) October
Fig. 10. Classification results of ozone profiles in the eastern China region for different months
Fig. 11. Ozone patterns of different categories in the eastern China. (a) January; (b) April; (c) July; (d) October
Fig. 12. Total volume fraction of CH4 in the eastern China region for different months
Fig. 16. Comparison of radiation transfer simulation results from 1976 US standard atmosphere model and self-built atmospheric model with HIRAS measured spectra in July
Fig. 18. Comparison of radiation transfer simulation results from 1976 US standard atmosphere model and self-built atmospheric model with HIRAS measured spectra in January
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Haosen Wang, Chen Cheng, Hailiang Shi, Xianhua Wang, Hanhan Ye, Shichao Wu, Erchang Sun. Atmospheric Model Construction Method Based on K‑means Clustering and Random Forest Regression[J]. Acta Optica Sinica, 2025, 45(12): 1201008
Category: Atmospheric Optics and Oceanic Optics
Received: Jan. 20, 2025
Accepted: Feb. 17, 2025
Published Online: Apr. 27, 2025
The Author Email: Chen Cheng (chengchen@aiofm.ac.cn)
CSTR:32393.14.AOS250520