@misc{indiciae7a6bff3b3b48,
  title = {Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability},
  author = {Chu, Yufei [Stony Brook Univ., NY (United States)] (ORCID:0000000263347293) and Lin, Guo [National Oceanic and Atmospheric Administration (NOAA), Miami, HI (United States); Univ. of Miami, FL (United States)] (ORCID:0000000244708882) and Deng, Min [Brookhaven National Laboratory (BNL), Upton, NY (United States)] (ORCID:000000026076282X) and Xue, Lulin [NSF National Center for Atmospheric Research, Boulder, CO (United States)] (ORCID:0000000255019134) and Li, Weiwei [NSF National Center for Atmospheric Research, Boulder, CO (United States)] and Shin, Hyeyum Hailey [NSF National Center for Atmospheric Research, Boulder, CO (United States)] and Zhang, Jun A. [National Oceanic and Atmospheric Administration (NOAA), Miami, HI (United States); Univ. of Miami, FL (United States)] (ORCID:0000000337130223) and Guo, Hanqing [Univ. of Hawaii at Manoa, Honolulu, HI (United States)] and Wang, Zhien [Stony Brook Univ., NY (United States)] (ORCID:0000000338713834)},
  year = {2026},
  doi = {10.5194/acp-26-1415-2026},
  url = {https://www.osti.gov/biblio/3017222},
  note = {Source identifier: 3017222}
}