ML4WX

Observations & remote sensing

Machine learning model to accurately estimate the planetary boundary layer height of Beijing urban area with ERA5 data

Kecheng Peng, Jinyuan Xin, Xiaoqian Zhu, Xiaoyuan Wang, Xiaoqun Cao, Yongjing Ma, Xinbing Ren, Dandan Zhao, Junji Cao, Zifa Wang

Atmospheric Research · · Institute of Atmospheric Physics, Chinese Academy of Sciences

Paper

In one sentence. Machine learning model estimates urban Beijing planetary boundary layer height from ERA5 predictors.

Labels

Topic
Observations & remote sensing
Type
New model or method
Method
Classical ML (trees, GP, linear, kernels)
Domain
Local / station
Data
ERA5 / reanalysis
Status
Research
Identifiers
DOI:10.1016/j.atmosres.2023.106925 OpenAlex:W4384297306
Added
2026-10-22

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