Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 14
Articles | Volume 18, issue 14
https://doi.org/10.5194/amt-18-3453-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-3453-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 14
Research article
 | 
28 Jul 2025
Research article |  | 28 Jul 2025

Best estimate of the planetary boundary layer height from multiple remote sensing measurements

Damao Zhang, Jennifer Comstock, Chitra Sivaraman, Kefei Mo, Raghavendra Krishnamurthy, Jingjing Tian, Tianning Su, Zhanqing Li, and Natalia Roldán-Henao

Supplement

https://doi.org/10.5194/amt-18-3453-2025-supplement

Data sets

Planetary Boundary Layer Height observations from Radiosondes L. Riihimaki et al. https://doi.org/10.5439/1991783

Planetary Boundary Layer Height C. Sivaraman and D. Zhang https://doi.org/10.5439/1637942

Boundary-layer height data with CEIL (CEILPBLHT) V. Morris et al. https://doi.org/10.5439/1095593

Ceilometer (CEIL) D. Zhang et al. https://doi.org/10.5439/1181954

Planetary Boundary Layer Height (PBLHTDL) C. Sivaraman and D. Zhang https://doi.org/10.5439/1726254

Doppler Lidar Vertical Velocity Statistics (DLPROFWSTATS4NEWS) T. Shippert et al. https://doi.org/10.5439/1178583

Planetary Boundary Layer Height (PBLHTRL1ZHANG) D. Zhang and C. Sivaraman https://doi.org/10.5439/2282350

Raman Lidar Temperature VAP (RLPROFTEMP2NEWS10M) R. Newsom et al. https://doi.org/10.5439/1415138

Tropospheric Optimal Estimation Retrieval (TROPOE) D. Turner https://doi.org/10.5439/1996977

Carbon Dioxide Flux Measurement Systems (30CO2FLX25M) S. Biraud et al. https://doi.org/10.5439/1989776

Data Quality Assessment for ARM Radiation Data (QCRAD1LONG) L. Riihimaki et al. https://doi.org/10.5439/1227214

Surface Meteorological Instrumentation (MET) J. Kyrouac et al. https://doi.org/10.5439/1786358

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Short summary
Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
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