Articles | Volume 14, issue 6
https://doi.org/10.5194/amt-14-4403-2021
https://doi.org/10.5194/amt-14-4403-2021
Research article
 | 
15 Jun 2021
Research article |  | 15 Jun 2021

On the estimation of boundary layer heights: a machine learning approach

Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner

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Cited articles

Atmospheric Radiation Measurement (ARM) user facility: Balloon-Borne Sounding System (SONDEWNPN). Southern Great Plains (SGP) Central Facility, Lamont, OK (C1), updated hourly, compiled by: Keeler, E., Ritsche, M., Coulter, R., Kyrouac, J., and Holdridge, D., ARM Data Center, https://doi.org/10.5439/1021460, 2001. 
Atmospheric Radiation Measurement (ARM) user facility: Constrained Variational Analysis (60VARANARUC). Southern Great Plains (SGP) Central Facility, Lamont, OK (C1), compiled by: Tao, C. and Xie, S., ARM Data Center, https://doi.org/10.5439/1647300, 2004. 
Banakh, V. A., Smalikho, I. N., and Falits, A. V.: Estimation of the height of the turbulent mixing layer from data of Doppler lidar measurements using conical scanning by a probe beam, Atmos. Meas. Tech., 14, 1511–1524, https://doi.org/10.5194/amt-14-1511-2021, 2021. 
Berg, L. K., Newsom, R. K., and Turner, D. D.: Year-long vertical velocity statistics derived from Doppler lidar data for the continental convective boundary layer, J. Appl. Meteorol. Clim., 56, 2441–2454, 2017. 
Biraud, S., Billesbach, D., and Chan, S.: ECOR: 30-minute averaged surface vertical fluxes of momentum, sensible heat, and latent heat at Southern Great Plains central facility, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program (United States) [data set], available at: https://adc.arm.gov/discovery/#/results/datastream::sgpco2flx25mC1.b1 (last access: 7 March 2020), 2015. 
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Short summary
Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
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