Enhanced Boundary Layer Height Detection Using Ceilometer, Surface Meteorology, and Radiation Products With a Random Forest Ensemble Method

Journal Article · Mon Nov 17 00:00:00 EST 2025 · Journal of Geophysical Research. Atmospheres

DOI:https://doi.org/10.1029/2025JD043385· OSTI ID:3005265

Caicedo, Vanessa ^[1]; Sedlar, Joseph ^[1]; Riihimaki, Laura D. ^[1]; Angevine, Wayne M. ^[1]; Turner, David D. ^[2]; Zhang, Damao ^[3]; Lantz, K. ^[2]

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1. Univ. of Colorado, Boulder, CO (United States); National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States)
2. National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States)
3. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

This study develops and evaluates a Random Forest (RF) model for estimating planetary boundary layer height (PBLH) using 9 years of data from the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) user facility, with potential application in the NOAA Surface Radiation (SURFRAD) Network. The model integrates ceilometer, surface meteorology, and radiation measurements, and is trained using thermodynamic PBLH estimates derived from radiosondes. This approach aims to bridge gaps between aerosol-based and thermodynamic-based PBLH estimates. The RF model outperformed traditional methods during daytime and better captured transition periods, demonstrating improved accuracy and robustness. At ARM SGP, it showed a substantial reduction in both bias and RMSE, with a bias near zero (−4.9 m) compared with traditional Haar Wavelet (HW) (70.9 m) and Vaisala BL-View software (124.1 m), and an RMSE of 303.2 m, lower than both BL-View (566.9 m) and HW (404.6 m). During daytime hours, RF consistently outperformed both alternatives, maintaining lower bias and RMSE across all periods. At a second evaluation site, RF achieved the lowest overall RMSE (323.7 m), similar to HW (326.4 m) and significantly better than BL-View (738.3 m). However, all models showed reduced accuracy under stable nighttime conditions, limiting the reliability of PBLH estimates. Key predictors for the model included the lifting condensation level height (LCLH), aerosol gradients, and month for seasonal variability. The study underscores the potential of integrating machine learning with multiple data sets such as surface energy and thermodynamic data to advance PBLH estimation.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: National Oceanic and Atmospheric Administration (NOAA); USDOE Office of Science (SC)
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 3005265
• Report Number(s): PNNL-SA--208623
• Journal Information: Journal of Geophysical Research. Atmospheres, Journal Name: Journal of Geophysical Research. Atmospheres Journal Issue: 22 Vol. 130; ISSN 2169-8996; ISSN 2169-897X
• Publisher: American Geophysical Union; Wiley
• Country of Publication: United States
• Language: English

Related Subjects

boundary layer height
ceilometer