Comparison of aerosol lidar retrieval methods for boundary layer height detection using ceilometer aerosol backscatter data

Abstract

Abstract. Three algorithms for estimating the boundary layer heights are assessed: an aerosol gradient method, a cluster analysis method, and a Haar wavelet method. Over 40 daytime clear-sky radiosonde profiles are used to compare aerosol backscatter boundary layer heights retrieved by a Vaisala CL31 ceilometer. Overall good agreement between radiosonde-and aerosol-derived boundary layer heights was found for all methods. The cluster method was found to be particularly sensitive to noise in ceilometer signals and lofted aerosol layers (48.8 % of comparisons), while the gradient method showed limitations in low-aerosol-backscatter conditions. The Haar wavelet method was demonstrated to be the most robust, only showing limitations in 22.5 % of all observations. Occasional differences between thermodynamically and aerosol-derived boundary layer heights were observed.

Atmospheric boundary‐layer characteristics from ceilometer measurements. Part 1: A new method to track mixed layer height and classify clouds

“…Caicedo et al . () discuss the implications of ALC signal response in clouds for different Z ML ‐methods. To address the role of clouds in sufficient detail (Schween et al, ), classification of ABL by not only cloud cover but also cloud type is required (section 2.3).…”

Mixing layer height as an indicator for urban air quality?

“…The same is true for sophisticated multi-wavelength lidars (e.g., Baars et al, 2008), sodars (e.g., Beyrich, 1995), combinations of instruments (e.g., Cohn and Angevine, 2000) and combinations of models and measurements (e.g., Bachtiar et al, 2014). A large number of studies relying on lidar data have been published introducing different methodologies to determine MLH: among others, Endlich et al (1979) and Flamant et al (1997) used algorithms based on first derivatives of the backscatter signal; Menut et al (1999) used second derivatives; Hooper and Eloranta (1986) the temporal variance; Cohn and Angevine (2000), Brooks (2003) and Baars et al (2008) applied wavelet covariance transforms; de Bruine et al (2017) used graph theory, Caicedo et al (2017) cluster analysis; and statistical methods were used by Eresmaa et al (2006) and Lange et al (2014). With recent upgrades of the hardware those methodologies can also be applied to ALC, and with the implementation of networks they have become more attractive as they provide continuous monitoring and good spatial coverage.…”

Application of Convective Condensation Level Limiter in Convective Boundary Layer Height Retrieval Based on Lidar Data

“…The Haar coefficient, W f (∆h, b), presents the maxima when the translation of Haar wavelet b coincides with the CBLH. The method is well suited to recognize a step function and has been adopted by other investigators and applied in several studies [17,34,50]. Similarly, the Mexican hat wavelet method (hereafter referred to as the MHM) allows a comparison between the NRB gradient (g(z)) and the Mexican hat wavelet.…”

“…Therefore, the retrieved boundary layer height is always replaced by the cloud "top". As clouds are characterized by a steep increase in the NRB at the cloud base, the cloud base can be identified as the altitude at which the Haar wave coefficient is lower than a chosen negative threshold [16,34]. In such studies, the signal values below the cloud base are then used only to determine the CBLH.…”

“…Davis et al [21] and Baars et al [16] applied a Haar wavelet transform to distinguish the boundary layer from the cloud layers by altering the suitable wavelet dilation or by choosing a negative threshold. However, those techniques depend strongly on the vertical distribution of particle layers (aerosols and clouds) and are not suitable for dealing with complicated multiple-layer conditions [5,34].…”