Journal Article
Atmospheric Measurement Techniques, vol. 14, iss. 6, pp. 4403-4424, 2021
Authors
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, David D. Turner
Abstract
Abstract. The planetary boundary layer height (zi) is a key
parameter used in atmospheric models for estimating the exchange of heat,
momentum, and moisture between the surface and the free troposphere.
Near-surface atmospheric and subsurface properties (such as soil
temperature, relative humidity, etc.) are known to have an impact on
zi. Nevertheless, precise relationships between these surface properties
and zi are less well known and not easily discernible from the
multi-year dataset. Machine learning approaches, such as random forest (RF),
which use a multi-regression framework, help to decipher some of the
physical processes linking surface-based characteristics to zi. In this
study, a 4-year dataset from 2016 to 2019 at the Southern Great Plains
site is used to develop and test a machine learning framework for estimating zi. Parameters derived from Doppler lidars are used in combination with
over 20 different surface meteorological measurements as inputs to a RF
model. The model is trained using radiosonde-derived zi values spanning
the period from 2016 through 2018 and then evaluated using data from 2019.
Results from 2019 showed significantly better agreement with the radiosonde
compared to estimates derived from a thresholding technique using Doppler
lidars only. Noteworthy improvements in daytime zi estimates were
observed using the RF model, with a 50 % improvement in mean absolute
error and an R2 of greater than 85 % compared to the Tucker method
zi. We also explore the effect of zi uncertainty on convective
velocity scaling and present preliminary comparisons between the RF model
and zi estimates derived from atmospheric models.
