An automatic observation-based aerosol typing method for EARLINET

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 lit-erature 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 Published by Copernicus Publications on behalf of the European Geosciences Union. 15880 N. Papagiannopoulos et al.: Automatic aerosol classification extremely suitable for the implementation within the single calculus chain (SCC), the EARLINET centralized processing suite.

Interrelations between surface, boundary layer, and columnar aerosol properties derived in summer and early autumn over a continental urban site in Warsaw, Poland

“…Amiridis et al (2005) reported a 4-year mean AOD ABL of 0.44±0.16 at 355 nm and a mean LR ABL of 49±25 at 355 nm in summer at Thessaloniki, Greece. According to Papayannis et al (2008), this much higher value of AOD ABL (355) can be attributed to a significantly stronger impact from the Figure 2. Frequency distribution of the aerosol optical depth (AOD), the lidar ratio (LR), the linear particle depolarization ratio (δ), and the Ångström exponent (ÅE) at 355 and 532 nm derived within the atmospheric boundary layer at the EARLINET/ACTRIS lidar site, Warsaw, for the July-September period of 2013, 2015, and 2016.…”

Optical properties of Central Asian aerosol relevant for spaceborne lidar applications and aerosol typing at 355 and 532 nm

“…In this study, we focus on the main findings regarding those particle optical properties that are used in aerosol-typing efforts with lidar networks and spaceborne lidars. Observations of particle extinction-to-backscatter ratios (lidar ratios), linear depolarization ratios at 355 and 532 nm wavelength, and backscatter-related and extinction-related Ångström exponents (describing the wavelength dependence of backscatter and extinction coefficients from 355 to 1064 nm), as presented here for Central Asian aerosol mixtures, are basic optical parameters in these aerosol-typing efforts and allow us to draw conclusions on the chemical composition (aerosol type), absorption and scattering properties, and size and shape characteristics of the particles (see, e.g., Müller et al, 2007;Burton et al, 2012Burton et al, , 2015Groß et al, 2013;Nicolae et al, 2018;Papagiannopoulos et al, 2016Papagiannopoulos et al, , 2018Voudouri et al, 2019). This article contributes to the steadily growing lidar aerosol-typing data base.…”

An EARLINET Early Warning System for atmospheric aerosol aviation hazards

“…These variables are shown to vary with the aerosol type and location and, consequently, EARLINET stations are able to characterize the aerosol load (Müller et al, 2007). Accordingly, EARLINET has established tools for the automatic aerosol characterization (Nicolae et al, 2018;Papagiannopoulos et al, 2018).…”

“…The input lidar ratio value used in the retrieval could significantly affect the results. Papagiannopoulos et al (2018) used 48 ± 13 sr for fresh volcanic particles and 55 ± 7 sr for desert dust particles observed over EARLINET sites in their aerosol classification, which illustrates the variability of this intensive parameter. The uncertainty induced due to the assumption of lidar ratio can easily exceed 20 % (Sasano et al, 1985) and presents an important source that affects the retrieval.…”