Man-made activities dictate the aerosol pattern within the atmospheric boundary layer, and affect the observations in the lower troposphere in Europe. Anthropogenic particles show a strong wavelength dependence of their optical properties, i.e., high Ångström exponent values. Moreover, they are typically small and do not significantly depolarize the backscattered light δaer532=0.04±0.04;, and due to the high carbon content, these particles reveal high lidar ratios . Herein, we refer to this particle type as polluted continental.

Typically, the clean continental aerosol over Europe is a mixture of anthropogenic pollution with particles from natural sources. The clean continental type shows a low depolarizing ability with values lower than 0.07 ; low lidar ratio values, i.e., 20–40 sr; and relatively high Ångström exponents, i.e., 1.0–2.5 . The clean continental, therefore, differentiates from the polluted continental type due to lower lidar ratio values.

Marine particles are produced at the sea surface and dominate the shallow boundary layer over the oceans e.g.,. Specifically, the sea-salt particles feature a predominant coarse mode; however, they are spherical in humid conditions and weakly absorbing in contrast to the dust particles. Therefore, they yield low particle lidar ratio values, are almost non depolarizing, and exhibit low Ångström exponent values e.g.,. This aerosol type is mainly identifiable by the low particle lidar ratio, i.e., 15–25 sr at 532 nm . As marine aerosol layers manifest themselves over water bodies, either stations only located at the shorelines and under specific meteorological conditions or shipborne measurements can observe pure maritime particles. Consequently, the observations of pure maritime particles is rare within EARLINET and, generally, when these particles are observed their characteristics are far from pristine . However, mixtures with important contribution of marine particles can be observed in the Mediterranean basin . Thus, we consider pure marine and marine-dominated layers as one single category denoted as mixed marine.

Mineral dust is produced in arid and semi arid regions of the world, and has a profound contribution to the total natural aerosol loading . The optical properties are considerably different from the other types, thus making them easy to identify. The irregular shape and the large size <50 µm; lead to a significant high depolarization of the backscattered radiation e.g., δaer532=0.34±0.02 for Saharan dust over Germany;, and to medium lidar ratio values e.g., Saer532=55±10 sr;. They are spectrally neutral to backscatter and extinction, and thus produce low Ångström exponent values . Therefore, the particle lidar ratio, particle linear depolarization ratio, and the Ångström exponent are excellent physical parameters to characterize mineral dust and to distinguish it from other aerosol types. However, it needs to be taken into account that the dust optical properties depend on the source region and the transport pattern , which is a source of variability detected in the lidar ratio e.g.,. Recently, showed that dust originating from the Arabian desert produced significantly lower lidar ratio values (34–39 sr at 532 nm) than respective values (50–60 sr at 532 nm) from western Saharan dust particles. An overview on the dust characterization using lidar measurements can be found in .

Dust can be transported over continental scales. In particular, Saharan dust outbreaks in Europe and across the Atlantic Ocean have been deeply investigated. The European continent is regularly influenced by advected Saharan particles as has been discussed by, e.g., , and . The study of indicated a large variability in the measured lidar ratio and Ångström exponent values among the different sites, suggesting mixing at different levels. Additionally, the mixture processes also produce large variability in intensive properties as measured at the same site e.g.,. As a consequence of the complex structure of the observed aerosols over Europe and the effects of transport and mixing on the properties of these particles, we consider the use of three dust groups: pure dust, mixed dust, and polluted dust. The pure dust group refers to particles for which the mixing with other aerosol types is negligible. Mixed dust refers to dust-dominated layers mixed with marine particles. This leads to less depolarizing, and less absorbing particles with respect to pure dust particles. Several studies have indicated that this mixture is important and suggested its inclusion in the CALIPSO retrieval scheme for improving the accuracy of aerosol backscatter and extinction coefficient profiles. Finally, the polluted dust category consists of dust-dominated mixtures with smoke and/or continental pollution, which produce lower depolarization, higher lidar ratios, and enhanced Ångström exponent values owing to the presence of small, spherical particles .

Biomass burning is a major global source of atmospheric aerosols. Generally, smoke particles are relatively small and spherical that produce low depolarization, high Ångström exponents, and large lidar ratios . The optical properties of smoke particles may vary due to the vegetation type of the emitting source, the combustion type (smouldering or flaming fires), and atmospheric conditions e.g.,. Furthermore, the particles are susceptible to changes during their lifetime in the atmosphere . Several EARLINET-based studies have focused on observations and characterization of smoke plumes e.g.,, demonstrating that it is a frequently encountered aerosol type over Europe. In particular, biomass burning aerosol originating from forest fires in Canada and Siberia is regularly observed between May and October . However, the similarities of the physical characteristics of smoke particles and continental particles result in similar optical properties, making these types difficult to distinguish. In this work, biomass burning particles are treated as a single category called smoke.

Volcanoes are another important source of atmospheric aerosols. Volcanic eruptions eject great amounts of material in the atmosphere (tephra), while the fraction smaller than 2 mm is labeled as volcanic ash. Most of these aerosols will settle only a few tens of kilometers away from the volcano but smaller particles can travel thousands of kilometers and affect wider areas . The optical properties of volcanic ash aerosols is generally similar to the one of desert dust, as was shown by and for fresh ash with particle linear depolarization ratios reaching 0.37 and lidar ratio at 532 sr of 50–65 sr. Aged volcanic particles as observed by indicate less non-sphericity with depolarization ratio values of 0.1–0.25 and lidar ratios for 355 nm within the range 55–67 sr and for 532 nm 76–89 sr. More details can be found in where the authors give a summary of how the intensive optical properties vary as a function of time. Furthermore, volcanic eruptions inject sulfur dioxide into the atmosphere thus leading to sulfate particles. and reported lidar ratios of 50–60 sr at 355 sr and backscatter-related Ångström exponent of 2.7 (355 532), signature of sulfate particles originating from Mount Etna, Italy. Moreover, CALIPSO measurements indicated low particle depolarization ratios for sulfate-rich volcanic clouds . Consequently, the difference in the optical properties make lidar a powerful tool for volcano monitoring. However, in this study sulfate particles and aged volcanic particles are not considered. The aerosol type relevant to the airborne ash refers to fresh ash and is denoted as volcanic ash.

As an additional consideration, the defined aerosol types presented in Sect.  may not be representative of the entire aerosol load and, apart from the dust mixtures, they do not consider other aerosol mixtures. For example, this aspect can be observed in the definition of the volcanic category where the particles have different characteristics depending on the transport pattern. The particles near the source have optical properties similar to desert dust whereas long-range-transported volcanic plumes have altered properties due to the sedimentation of the coarser particles. Therefore, it is important to further include a more exhaustive aerosol class analysis.

In supervised learning techniques, the reference dataset is crucial to the overall predictive performance of the algorithm. Therefore, it is fundamental to use well-characterized EARLINET profiles. Namely, EARLINET aerosol classified layers from , and were used and will be presented below.

EARLINET observations from 2008 to 2010 were analyzed and the aerosol types were determined with respect to the source origin following a similar approach to Sect.  and present the backbone of the reference dataset. Table  lists the classified aerosol types of the above study (644 individual aerosol layers) with respect to the aerosol types presented in Sect. ; however, all these aerosol layers cannot be used given the need for the maximum optical properties available (column “only from 3β+2α”). The mixtures category includes all the mixtures of two or more aerosol species without containing polluted dust and mixed dust categories that are reported individually.

As discussed above, the requirement for 3β+2α lidar configuration pinpoints the low occurrence (see Table ) of some aerosol types such as the clean continental, polluted dust, and dust. Furthermore, marine aerosol was not reported in the study and the volcanic layers do not reflect the volcanic ash characteristics described in Sect. . Conversely, the latter were volcanic layers found in the stratosphere and thus different from the fresh ash that we consider. In order that the aerosol classes include all the major aerosol components, the aforementioned aerosol types need to be enhanced with other observations. Therefore, we implemented EARLINET network-wide typing results already published in the literature for a total of 69 layers as the reference dataset. Note that calibrated particle linear depolarization ratio profiles are not available in the selected dataset.

The type-dependent mean properties are reported in Table  and coincide with the typical values as of those in Sect. . However, aerosol classification is based on an interpretative analysis of the retrieved optical properties and the model simulations, and it is a qualitative method of type assignment. Thus, there is an inherent possibility of error in the determination of the true aerosol type. This error, if made, propagates into the automatic algorithm and the predicted aerosol class might deviate from the “truth” aerosol class. Specifically, dust and volcanic types present the same characteristics with Ångström exponents as low as 0, although dust lidar ratios are 58±12 and 55±7 sr for 355 nm and 532 nm, respectively, and are higher than the volcanic lidar ratios (Saer355=50±11 and Saer532=48±13 sr). The Ångström exponents (i.e., κβ(355,1064), κβ(532,1064), κβ(355,532), and κα(355,532)) for mixed dust are between 0.4 and 0.7 and lidar ratio values are below 50 sr, whereas for polluted dust the Ångström exponents lie within 0.6–1.0 and lidar ratio values for 355 and 532 nm are 54±8 and 64±9 sr, respectively. This behavior reflects the mixing of dust with pollution/smoke that tends to decrease the size of the aerosol mixture and increase its absorbing capacity. Polluted continental and smoke reveal the same size characteristics with mean Ångström exponents from all the available variables around ∼1.4 and ∼1.3, respectively. The smoke mean lidar ratio values present the higher ones among the aerosol types – i.e., 81±16 and 78±11 sr for 355 and 532 nm, respectively – and the polluted continental values succeed with 69±12 and 63±13 sr for 355 and 532 nm, respectively. For clean continental, the Ångström exponents (i.e., κβ(355,1064), κβ(532,1064), κβ(355,532), and κα(355,532)) are between 1.0 and 1.7 and lidar ratios, for 355 and 532 nm, are 50±8 and 41±6 sr. This characteristic separates clean continental from polluted continental as the particles yield lower lidar ratio values. Finally, mixed marine particles are found to be relatively small in size with Ångström exponents (i.e., κβ(355,1064), κβ(532,1064), κβ(355,532), and κα(355,532)) in the range 0.8–1.0 and thus overlap with other aerosol types. The characteristic parameter that defines the mixed marine category is the lidar ratio, the values are found to be the smallest (Saer532=24±8 sr) among the aerosol types.

In the proposed method, the aerosol layers are classified in terms of the aerosol types described in Sect. . As a starting point for this study, we use 8 aerosol classes: clean continental (CC), polluted continental (PC), pure dust (D), mixed dust (MD = dust + marine), polluted dust (PD = dust + smoke and/or dust + polluted continental), mixed marine (MM), smoke (S), and volcanic (V). However, some of these 8 classes overlap consistently in the feature space. As a consequence, we exploited the combined use of overlapping aerosol types. Therefore, we merged the types that tend to reflect the same aerosol characteristics, and hence we evaluate the corresponding effects on the prediction rate of the algorithm. Two pathways were followed. First, the smoke and the polluted continental categories were grouped into the more generic type of small with high lidar ratio values. Second, all the dust-like aerosol types were merged. The different grouping categories are summarized in Table .

Next, we performed a sensitivity analysis to identify which classifying properties provide the adequate information to better predict the correct aerosol class. We used three aerosol intensive properties due to the lack of particle linear depolarization ratio profiles to evaluate the strength of the selected classifier to discriminate among the predefined classes. Two statistical parameters are used: the total and the partial Wilks' lambda Λ; that are widely used, e.g., and . The total Λ statistic shows the tendency of the above set of pre-specified classes (or any subset of it) to separate. The partial Λ is calculated for each of the intensive properties separately and indicates the discriminatory power of the used intensive property. For both parameters, values range from 0 to 1. Values near 0 show high discriminatory power while values near 1 show low discriminatory power.

The lowest total Λ was found to be 0.033 for the set κβ(355,1064), Saer532, and Saer532/Saer355; whereas the partial Λ is 0.51 for Saer532/Saer355, 0.17 for κβ, and 0.30 for Saer532. For this dataset, the low Λ value for κβ indicates that this variable has the most weight in the classification. The decision for the selected parameters stems solely from the lowest arithmetic value of the total Λ. Therefore, for the other groups of parameters the total Λ is equally low, ∼0.05, which indicates that a 2β+2α lidar setup could also be equally used when the algorithm is trained with κβ(355,532). With reference to the lidar ratio, the Saer532 and Saer355 can be used interchangeably due to the almost equal total Λ (i.e., 0.034).

For the rest of the aerosol groups reported in Table 2, the total and partial (for κβ) Λ are, respectively, 0.036 and 0.18 (7a classes), 0.041 and 0.18 (7b classes), 0.044 and 0.18 (6 classes), 0.057 and 0.20 (5 classes), and 0.070 and 0.21 (4 classes). The Λ shows good discriminatory power for each of the grouping classes, although there is a slight increase in the values as the number of classes is reduced. This behavior can be ascribed to the high variance in the combined aerosol types which makes the classification less selective.

Figure  shows the characteristics of the reference dataset in terms of the Saer532 and κβ(355,1064) for the 8 and 4 aerosol classes that represent the maximum and minimum aerosol groupings used. The coloring corresponds to the various classes and the crosshairs indicate the standard deviation of each of the aerosol layers. The 90 % confidence ellipses are calculated using the eigenvalues and eigenvectors of the covariance matrix and define the region that contains 90 % of all the points that can be drawn from the underlying normal class distribution. The various aerosol classes tend to populate specific areas of the graph, whereas the overlap of the neighboring classes is significant, although the classes are better pinpointed as long as we merge classes with similar characteristics. However, the latter does not reflect the obtained values of the statistical parameters (total Λ increased from 0.033 for 8 classes to 0.070 for 4 classes), and, as explained above, the reference dataset very well delineates the aerosol types and by combining the neighboring types the variance increases.

In order to evaluate the predictive accuracy of the automatic method, it is needed to split the initial reference dataset into a training and a validation dataset. Like this, we use the training dataset to calculate the classification functions and then submit each observation in the validation dataset to the classification function obtained from the training dataset. For this study, we make use of the leave-one-out cross validation (LOOCV) procedure, also referred to as holdout procedure or simply cross validation, which is a degenerate case of the k fold cross validation, where k is chosen as the total number of samples . The choice of the procedure, even though computationally expensive, is used when datasets are sparse and trains the algorithm with as many observations as possible. Each measurement is separately removed from the training dataset in order to compute the classification rule, and this rule is used to classify the removed observation. The error rate is estimated as a percentage of all incorrect predictions divided by the total number of the reference dataset, and is equivalent to 1 minus accuracy. Values near 0 show high predictive performance while values near 1 show low predictive performance. For the classification options of the Table , the error rate, expectedly, decreases with decreasing number of aerosol classes (39 % for 8 classes, 36 % for 7a classes, 30 % for 7b classes, 28 % for 6 classes, 19 % for 5 classes, and 10 % for 4 classes). It should be mentioned that the typing in multiple classes and typing accuracy are two conflicting aspects. The choice of 8 aerosol classes appears to be sufficient to describe the major aerosol components, but ostentatious for a 3β+2α lidar configuration. Four classes, on the other hand, provide a coarse aerosol characterization and the prediction accuracy of the algorithm is expected to increase.

Several studies have shown the unique information provided by depolarization measurements e.g.,, thus making this intensive property a robust means to discriminate the various aerosol types. Valuable typing information can also be obtained by the color ratio of the particle depolarization ratios when more depolarization channels exist . As already stated in Sect. , the majority of the stations perform depolarization measurements, and profiles are routinely delivered by SCC. However, the reference dataset does not contain depolarization information because it has been released before the assessment of the quality assurance procedures within EARLINET. Therefore, a method applicable to EARLINET data collected since 2000 is proposed in this work. We investigate the effect of adding depolarization information to the described method as the next releases of EARLINET dataset will contain quality assured particle depolarization profiles and can be used for more accurate aerosol typing. To complement the reference dataset in this context, we used general literature values for particle linear depolarization ratio at 532 nm (Table ) in order to train the algorithm. For the clean continental type, the values ingested in the algorithm are retrieved from and refer to the polluted marine category. The decision for this inconsistency stems from the shortage of clean continental particle depolarization values in the literature; however, the reported values coincide with the type characteristics described in Sect.  and the values used in the CALIPSO typing scheme .

In this case, the particle linear depolarization ratio was added to the classifying parameters. Values within the aerosol type range were randomly assigned to each sample and the Λ distribution was calculated. Total Λ is 0.004. The value of partial Λ for κβ(355,1064), Saer532, Saer532/Saer355, and δaer532 are 0.55, 0.34, 0.52, and 0.12, respectively. The values found for the partial Λ confirm the δaer532 as the most important classifying parameter for the considered dataset. For the rest of the aerosol groups, the total and partial (for depolarization ratio) Λ are, respectively, 0.005 and 0.14 (7a classes), 0.005 and 0.12 (7b classes), 0.006 and 0.14 (6 classes), 0.040 and 0.68 (5 classes), and 0.050 and 0.69 (4 classes).

For the sake of completeness, the LOOCV method was also performed and the error rate was calculated. Figure  comparatively presents the training of the algorithm when depolarization information is available and when not in terms of the total, partial Λ, and the error rate of the LOOCV method. The figure highlights the strength of polarization-sensitive observations, while for the 5 and 4 classes (Fig. b) the particle linear depolarization ratio becomes less important (in this case the highest weight in the classification corresponds to the lidar ratio at 532 nm) due to the fact that only one dust type represents volcanic and other dust mixtures. Figure  presents cumulative bar plots with the median (black dots), the 25–75 percentile (box), the 5–95 percentile (whiskers) for all four classifying parameters. The figure highlights the discriminatory power of δaer532, κβ(355,1064), and Saer532, whereas the Saer532/Saer355 performs the worst. Furthermore, the figure depicts the discriminatory power of the classifying parameter among the dust-like aerosol classes; however, the particle depolarization ratio seems to have no power to separate the non-dust classes as discussed above.