  A question of global concern regarding the sustainable future of humankind
stems from the effect due to aerosols on the global climate. The quantification
of atmospheric aerosols and their relationship to climatic impacts are key to
understanding the dynamics of climate forcing and to improve our knowledge
about climate change. Due to its response to precipitation, temperature,
topography and human activity, one of the most dynamical atmospheric regions is
the atmospheric boundary layer (ABL): ABL aerosols have a sizable impact on the
evolution of the radiative forcing of climate change, human health, food
security, and, ultimately, on the local and global economy. The identification
of ABL pattern behaviour requires constant monitoring and the application of
instrumental and computational methods for its detection and analysis. Here, we
show a new method for the retrieval of ABL top arising from light detection and
ranging (LiDAR) signals, by training a convolutional neural network in a
supervised manner; forcing it to learn how to retrieve such a dynamical
parameter on real, non-ideal conditions and in a fully automated, unsupervised
way. Our findings pave the way for a full integration of LiDAR elastic,
inelastic, and depolarisation signal processing, and provide a novel approach
for real-time quantitative sensing of aerosols.
