@misc{10481/57632,
year = {2018},
month = {11},
url = {http://hdl.handle.net/10481/57632},
abstract = {We present an automatic aerosol classification
method based solely on the European Aerosol Research Lidar
Network (EARLINET) intensive optical parameters with
the aim of building a network-wide classification tool that
could provide near-real-time aerosol typing information. The
presented method depends on a supervised learning technique
and makes use of the Mahalanobis distance function
that relates each unclassified measurement to a predefined
aerosol type. As a first step (training phase), a reference
dataset is set up consisting of already classified EARLINET
data. Using this dataset, we defined 8 aerosol classes: clean
continental, polluted continental, dust, mixed dust, polluted
dust, mixed marine, smoke, and volcanic ash. The effect of
the number of aerosol classes has been explored, as well as
the optimal set of intensive parameters to separate different
aerosol types. Furthermore, the algorithm is trained with literature
particle linear depolarization ratio values. As a second
step (testing phase), we apply the method to an already
classified EARLINET dataset and analyze the results of the
comparison to this classified dataset. The predictive accuracy
of the automatic classification varies between 59% (minimum)
and 90% (maximum) from 8 to 4 aerosol classes, respectively,
when evaluated against pre-classified EARLINET
lidar. This indicates the potential use of the automatic classification
to all network lidar data. Furthermore, the training of
the algorithm with particle linear depolarization values found
in the literature further improves the accuracy with values for
all the aerosol classes around 80 %. Additionally, the algorithm
has proven to be highly versatile as it adapts to changes
in the size of the training dataset and the number of aerosol
classes and classifying parameters. Finally, the low computational
time and demand for resources make the algorithm extremely suitable for the implementation within the single
calculus chain (SCC), the EARLINET centralized processing
suite.},
organization = {The research
leading to these results has received funding from the European
Union’s Horizon 2020 research and innovation program under
grant agreement no. 602014 (project ECARS – East European
Centre for Atmospheric Remote Sensing) and from the European
Union’s Horizon 2020 research program for societal challenges
– smart, green and integrated transport under grant agreement
no. 723986 (project EUNADICS-AV – European Natural Disaster
Coordination and Information System for Aviation).},
publisher = {European Geosciences Union},
title = {An automatic observation-based aerosol typing method for EARLINET},
doi = {10.5194/acp-18-15879-2018},
author = {Papagiannopoulos, Nikolaos and Alados Arboledas, Lucas and Guerrero Rascado, Juan Luis},
}