Automatic detection of atmospheric boundary layer height using ceilometer backscatter data assisted by a boundary layer model

Abstract

This work tackles the problem of the automated detection of the atmospheric boundary layer (BL) height h, from aerosol lidar/ceilometer observations. A new method, the Bayesian selective method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in a statistically optimal way different sources of information. Firstly, atmospheric stratification boundaries are located from discontinuities in the ceilometer backscattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneous physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice.The BSM algorithm has been tested for 4 months of continuous ceilometer measurements collected during the BASE:ALFA project, and is shown to realistically diagnose the BL depth evolution in many different weather conditions. A standard one-dimensional processing of the ceilometer signal without the a priori support of the dynamical and climatological BL models often fails to correctly detect h, with the greatest inaccuracies occurring at night-time when residual layers can generate very strong signals, which are then classified by an automated application of the gradient or of the wavelet analysis as the most probable BL height. The BSM approach instead carries information on the low climatological probability to find elevated BL depths at night and penalizes the selection of these points. Moreover, this method is able to correctly convey information along the temporal dimension, thus filling data gaps using earlier and subsequent ceilometer information for the retrieval.

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

“…8) in order to prevent the detection of RL signals or lofted aerosol layers. Other methods to prevent the incorrect detection of the BLH include those proposed by de Haij et al (2006), Di Giuseppe et al (2012, and Pal et al (2013). However, our study uses the height restraint as it has been shown to successfully prevent the detection of RL signals in the example shown in Fig.…”

PathfinderTURB: an automatic boundary layer algorithm. Development, validation and application to study the impact on in situ measurements at the Jungfraujoch

“…It is also worth mentioning the method proposed in Steyn et al (1999), which consists of fitting an idealized backscatter profile at the transition between the BL and the FT. In the more recent literature there are examples of different methods combining the lidar gradient-based retrievals with temporal height-tracking techniques, for example observational (Martucci et al, 2010a, b), predictive (Tomás et al, 2010) or model-based first guesses (Di Giuseppe et al, 2012). Pal et al (2013) proposed a simplified bulk model combined with surface turbulence measurements and atmospheric variance measurements, to help select the BL height amongst all candidates.…”

High concentrations of sub-3nm clusters and frequent new particle formation observed in the Po Valley, Italy, during the PEGASOS 2012 campaign

“…The data obtained with a SO 2 monitor (model 43i-TLE, Thermo Scientific) were also utilized. Furthermore, the measurement station was equipped with a ceilometer (Jenoptik CHM15K), which allowed us to monitor the evolution of planetary boundary layer (PBL; Di Giuseppe et al, 2012;Angelini and Gobbi, 2014).…”