An automatic observation-based aerosol typing method for EARLINET
2018
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.
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