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

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