Skip Content
You are currently on the new version of our website. Access the old version .
Remote SensingRemote Sensing
  • Article
  • Open Access

31 October 2025

Optical Properties and Radiative Forcing Estimations of High-Altitude Aerosol Transport During Saharan Dust Events Based on Laser Remote Sensing Techniques (CLIMPACT Campaign 2021, Greece)

,
,
,
,
,
,
,
and
1
Laser Remote Sensing Unit, Department of Physics, National and Technical University of Athens, 15780 Zografou, Greece
2
Laboratory of Atmospheric Processes and their Impacts (LAPI), Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
3
Raymetrics S.A., Spartis 32 & Fil. Eterias, GR-14452 Metamorfosis, Greece
4
Consiglio Nazionale delle Ricerche-Istituto di Metodologie per l’Analisi Ambientale CNR-IMAA, 85050 Potenza, Italy
This article belongs to the Section Environmental Remote Sensing

Highlights

What are the main findings?
  • Lidar measurements from the Helmos high-altitude observatory revealed distinct free-tropospheric aerosol layers, distinguishing between pure Saharan dust and mixed dust-smoke plumes with enhanced particle depolarization ratios.
  • Radiative transfer simulations showed contrasting radiative impacts, with dust layers producing net cooling and mixed layers inducing local heating, indicating the complexity of aerosol–radiation interactions in the eastern Mediterranean region.
What is the implication of the main finding?
  • These results indicate the importance of mountain observatories for characterizing long-range transported aerosols above the planetary boundary layer and understanding their radiative impacts.
  • The first combined lidar measurements with radiative forcing dataset at the Helmos Observatory provide a reference for integrating high-altitude aerosol observations into regional climate models to improve predictions of dust and smoke radiative effects over the Mediterranean.

Abstract

We present two case studies of tropospheric aerosol transport observed over the high-altitude Helmos observatory (1800–2300 m a.s.l.) in Greece during September 2021. Two cases were linked to Saharan dust intrusions, of which one was additionally linked to a mixture of biomass-burning and continental aerosols. Aerosol vertical profiles from the AIAS mobile backscatter/depolarization lidar (532 nm, NTUA) revealed distinct aerosol layers between 2 and 6 km a.s.l., with particle linear depolarization ratio values of up to 0.30–0.40, indicative of mineral dust. The elevated location of Helmos allows lidar measurements in the free troposphere, minimizing planetary boundary layer influence and improving the attribution of long-range transported aerosols. Radiative impacts were quantified using the LibRadtran model. For the 27 September dust outbreak, simulations showed strong shortwave absorption within 3–7 km, peaking at 5–6 km, with surface forcing reaching −25 W m−2 and TOA forcing around −12 W m−2, thus, implying a net cooling by 13 W m−2 on the Earth’s atmosphere system. In contrast, the 30 September mixed aerosol case produced substantial solar attenuation, a surface heating rate of 2.57 K day−1, and a small positive forcing aloft (~0.05 K day−1). These results emphasize the contrasting radiative roles of dust and smoke over the Mediterranean and the importance of high-altitude observatories for constraining aerosol–radiation interactions.

1. Introduction

Atmospheric aerosols are important, though uncertain, drivers in modulating the radiation balance of the atmosphere by scattering and absorbing (the Sun’s shortwave radiation and Earth’s longwave radiation)—the so-called “direct aerosol effect”, and by acting as cloud condensation nuclei (CCN) or as ice nuclei (IN) and, thus, affecting cloud properties and precipitation (modification of clouds’ microphysical properties, lifetime, albedo, precipitation cycle, ice content, etc.)—the so-called “indirect aerosol effect”. In addition, absorbing aerosols such as black carbon can locally heat the atmosphere and reduce cloud cover, a process known as the semi-direct aerosol effect [1,2,3,4]. According to Forster et al. (2021) [1], climate forcing by both direct and indirect aerosol effects offsets about a third of greenhouse gas forcing and contributes to the largest uncertainty of total anthropogenic forcing. A synopsis of the aerosol direct effect is provided by Haywood and Boucher (2000) [5].
The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) [2] attributes the cooling aerosol effect to the Earth’s effective radiative forcing (ERF) (called in this paper as RF, for simplicity) of the order of −1.3 [−2.0 to −0.6] W m−2 over the period 1750 to 2014 [2]. This large uncertainty [−2.0 to −0.6] W m−2, although reduced in recent years in current estimates of aerosol forcing, arises from the incomplete knowledge concerning the vertical distribution of the physico-chemical properties of aerosols over the globe as well as the incomplete knowledge on aerosol–cloud interactions, especially the cloud microphysical properties. The aerosol–cloud interaction producing an ERF equal to −0.3 [−0.6 to 0.0] W m−2 [2] involves the interactions of aerosol particles with liquid or ice clouds that change the cloud microphysical properties and suppress precipitation [6,7], thus, modifying their lifetime and changing their ice content [8,9,10,11].
The Mediterranean basin is a crossroad of air masses with where different chemical characteristics originating from different sources and continents meet and mix within the troposphere [12]. The mountainous sites are often influenced by aerosol sources from the planetary boundary layer (PBL) and/or from long-range transported air masses, thus, an experimental site in these regions may reside within the PBL at certain time periods of the day/season or in the free troposphere (FT) [13,14]. Moreover, the African continent, especially the Saharan desert, is considered to be the largest dust-producing region in the world [15,16]. It is well documented that desert dust aerosols originating from Sahara are frequently lifted into the atmosphere and can be transported over long distances under specific weather conditions [17,18,19]. Similarly, due to large wildfires in Southern Europe in summer and fall periods, smoke particles may be found in tropospheric air masses over Greece [20]. Thus, the experimental stations located in the Mediterranean area are frequently influenced by the presence of Saharan dust, smoke, or mixed dust–smoke aerosols [21,22,23,24,25,26,27]. In addition, extensive international research campaigns have investigated aerosol transport over the Mediterranean region through numerous ground-based, airborne, and satellite campaigns, providing valuable insights into the optical, microphysical, and chemical aerosol properties as well as the impacts of these aerosol events [28,29,30,31,32,33].
The total direct radiative effect of mineral dust (coarse and fine modes), although updated to −0.11 ± 0.3 W m−2 [34], still presents a high uncertainty [35]. Therefore, it is of particular importance to study the effects of desert dust particles (pure or mixed with other aerosol types) in the Earth radiative budget [36,37,38,39,40] by measuring the optical properties of dust aerosols aloft. The lidar technique is a very efficient tool to provide the vertical profiles of the aerosol optical and microphysical properties with very high spatial (typically 7.5 m) and temporal resolutions (typically 1–1.5 min) up to heights of the order of 6–10 km a.s.l. [41,42,43,44].
In this paper, Section 2 presents the experimental site, the instrumentation, and the modeling tools used to follow the evolution of the dust events, while Section 3 presents the vertical profiles of the aerosol optical properties and the relevant radiative effects of the dusty and smoky air masses passing over our station. Finally, Section 4 presents our discussion and conclusions.

2. Experimental Site, Instrumentation, and Modeling Tools

The experimental site, the instrumentation, and the modeling tools are presented in the following subsections.

2.1. Experimental Site

The data presented in this paper were obtained in the frame of a short experimental campaign (23–30 September 2021) within the National Research Network for Climate Change and Its Effects—CLIMPACT project, which is an interdisciplinary consortium comprising 28 members from the academic and research communities in Greece and Cyprus. This consortium is dedicated to addressing issues related to Climate Change (CC) and the associated climate risks, natural disasters, as well as social and economic impacts (https://climpact.gr, last accessed on 15 September 2025).
This campaign took place at a remote high-altitude site located at the Kalavryta Ski Center (KSC) on Mount Helmos (also known as Aroania) at an altitude of 1750 m a.s.l. (37.984°N, 22.1969°E), near the Helmos Atmospheric Aerosol and Climate Change Station ((HAC)2) (Figure 1) [14]. Mount Helmos, situated in Northern Peloponnese, Greece, hosts the only high-altitude atmospheric research station in the Eastern Mediterranean region, located at 2314 m a.s.l. The unique position of the (HAC)2 station enables the study of aerosol–cloud interactions, as the site is frequently immersed in clouds, particularly during autumn and winter [13].
Figure 1. (a) The study area (b) and the sub-domain over Greece; (c) the regional area around Helmos and the lidar site located at 1750 m a.s.l. The red dot depicts the location of the Kalavryta Ski Center (KSC).
According to Collaud Coen et al. (2018) [45], Helmos has the lowest planetary boundary layer (PBL)–TopoIndex among similar European mountaintop stations, indicating minimal PBL influence and providing favorable conditions for the characterization of free-tropospheric (FT) aerosols. However, depending on the season and time of day, the station can also be affected by PBL air masses, allowing the investigation of locally emitted or regionally aged aerosol types (e.g., biogenic or biomass-burning aerosols) and their role in aerosol–cloud interactions [8,9,13].
Furthermore, the site lies at the crossroads of air masses of diverse origins, including continental, Saharan, and long-range biomass-burning sources [46], facilitating the study of aerosols with distinct physical and chemical properties. The (HAC)2 station contributes to several international and national research infrastructures, including the Global Atmosphere Watch (GAW) program, the Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS, under the acronym HAC), and the PANhellenic infrastructure for Atmospheric Composition and climatE chAnge (PANACEA) [47].

2.2. NTUA Mobile Lidar System

The mobile single-wavelength (532 nm) depolarization Aerosol lIdAr System (AIAS) was deployed at the KSC from 23 to 30 September 2021. AIAS is based on a Nd:YAG laser emitting linearly polarized pulses of 98 mJ at 532 nm with a 10 Hz pulse repetition rate. The elastically backscattered lidar signals are collected by a 200 mm diameter Cassegrainian telescope and separated into parallel and vertical polarization components, which are detected by photomultiplier tubes (PMTs). The signals are then digitized usingTR 20–160 transient recorders (Licel GmbH, Berlin, Germany) that combine analog and photon-counting acquisition modes. In this configuration, the 20 analog-to-digital converter (ADC) clock and photon counter readout cycle provide a 7.5 m range resolution, while the 160-memory length allows signal storage up to 16,384 data bins. AIAS profiles have a spatial vertical resolution of 7.5 m and a temporal resolution of 1.5 min. The full overlap height of AIAS is reached at 250 m above ground level (a.g.l.). The overlap function was determined experimentally by comparing the measured range-corrected signal under clean atmospheric conditions with the theoretical molecular backscatter profile [48,49]. This procedure accounts for the actual system alignment and instrumental parameters, such as laser beam divergence and the receiver field of view. The retrieved overlap profile was normalized to unity at the altitude of full overlap and applied to all lidar profiles to correct for partial overlap effects in the near-field region. The technical characteristics of the AIAS lidar system, which was designed in compliance with EARLINET quality assurance tests and standards [50], are provided by Papayannis et al. (2020) [51] and Mylonaki et al. (2021) [52]. The altitude range sampled by AIAS over ground was between 400 and 6000 m, covering the PBL and the lower FT [10]. The spatio-temporal evolution of the vertical profiles of the aerosol optical properties was captured by AIAS to provide the aerosol backscatter coefficient (baer) and the particle linear depolarization ratio (PLDR) at 532 nm. The collected data are processed using the Single Calculus Chain (SCC), the centralized processing tool developed within EARLINET/ACTRIS with estimated uncertainties in the retrieved baer of approximately 15–30% [53,54].

2.3. METAL-WRF Dust Modeling

The atmospheric conditions and the transport of dust towards the experimental site are simulated with the METAL-WRF model [55]. This model is an extended version of WRF-Chem and is based on the GOCART-AFWA dust module, including a new wet deposition parameterization for dust [56]. The GOCART-AFWA scheme has been successfully used in several earlier studies for dust in the same region [57,58].
The dust particles are represented in five size bins with effective radii of 0.73, 1.4, 2.4, 4.5, and 8 μm, respectively. More details on the model development can be found in Solomos et al. (2023) [55]. The horizontal grid space of the model is set at 12 km × 12 km, with 32 vertical hybrid-sigma levels stretching from the surface to the top of the atmosphere (50 hPa). The initial and boundary conditions are provide by the ERA5 reanalysis dataset [59], and the simulation period is from 20 to 30 September 2021. The physical parameterizations for the model runs are shown in Table 1.
Table 1. Physical schemes in METAL-WRF setup.

2.4. MODIS Satellite

The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites is an operational satellite sensor which, among others, provides Aerosol Optical Depth (AOD) retrievals at 500 nm twice daily (under cloud-free conditions) on a near-global basis with high spatial resolution over ocean and land [68]. Aside from AOD, MODIS provides additional aerosol parameters (e.g., Ångström exponent, refractive index) which will not be used in the frame of this paper. Their retrieval is more precise over dark surfaces (in visible wavelengths) over land [69] and ocean [70].

2.5. HYSPLIT Air Mass Trajectory Model

The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), developed by NOAA’s Air Resources Laboratory (ARL) (https://www.ready.noaa.gov (accessed on 23 October 2025) [71], was used to retrieve the atmospheric transport of the Saharan dust particles over our experimental site. For the analysis of the air mass backward trajectories, we used the “normal” method along with the GDAS1 (Global Data Analysis System) meteorological data. The vertical motion used to calculate the trajectories was the model vertical velocity. The initial values used to run the model were the coordinates of the AIAS lidar system at the Helmos site (cf. Section 2.1) and the altitude (a.s.l.) of the dust and smoke aerosol layers observed. The duration of the backward air mass trajectories was set to 120 h, a typical duration characterizing dust air masses in the Eastern Mediterranean region [72].

2.6. LibradTran Radiative Transfer Model

The shortwave (SW, 280–2500 nm) and longwave (LW, 2.5–40 µm) irradiances at the top-of-atmosphere (TOA) and the bottom-of-atmosphere (BOA) levels have been simulated using the libRadtran radiative transfer model version 2.0.2. [73] based on the uvspec program which calculates the radiation field in the Earth’s atmosphere using disort radiative transfer equation. A set of four simulations was carried out per case of the studied dust events: The first set (SW-LW) of calculations was necessary to estimate clear-sky conditions with background/baseline aerosol conditions (rural type aerosol inside the boundary layer, background aerosol above 2 km, and a visibility of 50 km). The second set of simulations corresponds to the aerosol-loaded atmosphere, where the vertical profiles of the aerosol layers were used as additional inputs.
Additionally, the Optical Properties of Aerosol and Clouds (OPAC) dataset [74] for desert spheroids (T-matrix calculations) was used in the input parameters. OPAC allows the calculation of the optical properties of aerosols at 61 wavelengths between 0.25 and 40 µm, and therefore, the real and imaginary parts of the refractive indices are given for these wavelengths. The following outputs were estimated in our simulations: the direct horizontal irradiance (edir), the global irradiance (eglo), the diffuse downward irradiance (edn), the diffuse upward irradiance (eup), and the heating rates (heat) in K day−1 [75].
The following equations were applied:
ΔF = F↓ − F↑
where ΔF (net flux) is the difference between the downwelling and upwelling flux, F↓ and F↑, at a height z.
Therefore, the radiative forcing (RF) and the net radiative forcing (NRF) can be estimated as follows:
RF = ΔFaerosol − ΔFclear
RFNET = RFSW + RFLW
In the following sections, we focus on two long-range transport events of Saharan dust particles that occurred in the period 27–30 September 2021: first, pure Saharan dust (27 September 2021), and second, desert dust mixed with biomass-burning particles (30 September 2021).

3. Observations—Experimental Results

To provide a comprehensive understanding of the events, we combined multiple observational and modeling approaches. The temporal and vertical evolution of the plumes was characterized using lidar measurements, while satellite observations provided a broader spatial perspective. The transportation of air masses and source regions of dust and smoke were investigated using WRF and HYSPLIT simulations. By comparing lidar profiles with satellite imagery and model outputs, we were able to identify aerosol origins and track their long-range transport. The LibRadTran model provided the radiative forcing and heating rates of aerosol layers. This integrated approach enables a robust interpretation of the events, linking emission sources, atmospheric transport, and local impacts at multiple spatial and temporal scales.

3.1. Synoptic Meteorological Conditions of the Dust Event (27–30 September 2021)

The synoptic situation during the study period is described by the METAL-WRF results regarding the meteorological fields and the dust parameters. The main reason for Saharan dust mobilization and transport towards Europe was the establishment of a persisting anticyclone over the central Mediterranean throughout the modeling period. As shown in Figure 2, the dust plume was not directly advected from Africa towards Greece and Helmos station. Rather, it was initially transported from the western parts of the Saharan desert, then traveled north eastwards towards Spain, France, and Italy (23–26 September 2021) and moved eastwards towards Greece on 27–30 September 2021, which was when it was detected at Helmos station.
Figure 2. Vertically integrated dust load (mg m−2) in METAL-WRF model for the period 23–26 September 2021 (12:00 UTC) (ad), 27 September 2021 (08:00, 13:00 and 16:00 UTC) (eg, respectively) and 30 September 2021 (15:00 UTC) (h).

3.2. HYSPLIT Backward Trajectory Analysis (27 and 30 September 2021)

The trajectories of the sampled air masses arriving at Helmos (denoted by a yellow asterisk in Figure 3) were verified using backward trajectory analysis based on the HYSPLIT model, calculated in isentropic vertical motion mode. As shown in Figure 3a, the air masses which arrived over Helmos on 27 September at 08:00 UTC had two main characteristics: the long-range ones which arrived at ~4 km in height (a.s.l.) (cf. Figure 3a, dark magenta color) originated from the Eastern United States of America 192 h before; the near-range ones mainly followed a similar pattern, due to a persisting anticyclonic system established over the central Mediterranean region, as shown in Section 3.1. Thus, the air masses off the Western Saharan region were advected over Spain, Southern France, overpassing Italy before arriving over Helmos at between 4 and 6 km in height (cf. Figure 3a). Later, on the same day (14:00–17:00 UTC), the anticyclonic system shifted to north-eastern latitudes with the Central–Western Saharan desert as the source region. The dust plume rose to ~4.5–6 km in height a.s.l. during its overpass over the Mediterranean Sea, mainly for the air masses arriving at 14:00 and 17:00 UTC over Helmos on 27 September (cf. Figure 3b,d and Figure S1f,i).
Figure 3. Air mass trajectories arriving over Helmos at various heights on 27 September 2021 (ad) for 08:00, 14:00, 16:00, 17:00 UTC, respectively) and on 30 September 2021 (15:00 UTC) (e), calculated by the HYSPLIT air mass back-trajectory model (starting point at 190 h before). The red dots delineate the presence of biomass-burning spots.
Concerning the air masses arriving over Helmos between the ground and 2.9 km in height a.s.l. on 30 September at 15:00 UTC, they originated from the Atlantic Ocean, east of Greenland, then they overpassed Central Europe, the Balkans, and Turkey at heights below 2 km, being enriched with smoke particles from wildfires in Romania (cf. red dots in Figure 3e). The air masses arriving at around 3.5 km in height a.s.l. at the Helmos site, on the same day and hour, had previously overpassed the Saharan desert at heights below 500 m, being enriched with desert aerosols (cf. Figure 3e and Figure S2).

3.3. MODIS Satellite Data

Figure 4 presents the MODIS “True Image” alongside the Combined Value-Added Aerosol Optical Depth normalized at 550 nm (AODMODIS) produced from both Terra and Aqua satellites for the two selected days of the dust event (27 and 30 September 2021). The color scale layering represents the values of AODMODIS at 500 nm, at a scale of 0 (yellow) to 5 (dark red). On 27 September, the MODIS AODMODIS values over Helmos were around 0.1–0.2 (Figure 4a–d) and had similar values (0.5–0.1) on 30 September.
Figure 4. (a,c) MODIS-EFFIS “True Image” and (b,d) the AOD at 550 nm obtained from MODIS Aqua and Terra merged data on 27 and 30 September 2021, respectively.

3.4. Laser Aerosol Remote Sensing Observations

3.4.1. Case 1—Saharan Dust Transport Event (27 September 2021)

Figure 5 presents the spatio-temporal evolution of the range-corrected lidar signal (RCS) at 532 nm, retrieved on 27 September 2021, from the ground up to ~6.0 km a.s.l.
Figure 5. Spatio-temporal evolution of the range-corrected lidar signal obtained by the AIAS lidar at 532 nm in arbitrary units (A.U.) over Helmos on 27 September 2021.
Between 08:24 and 09:00 UTC, two distinct aerosol layers were observed: the first extending from ~2.0 to 2.8 km and the second from ~3.2 to 3.5 km. In addition, a thin elevated aerosol layer was also detected at ~5.5 km, most likely associated with lofted Saharan dust transported in the free troposphere. Later, around 09:36 UTC, the layer near 3.5 km extended vertically and became denser. We also observed that the aerosols inside the PBL (between the ground and 3.0 km in height) merged with those of the layer at 3.5 km after 12:30 UTC, and thus, a distinct and thicker layer is found up to 3.5 km in height (~14:24 UTC). Additionally, a distinct aerosol layer becomes visible (from 11:00 to 14:24 UTC) between 3.5 and 4.2 km. Furthermore, at ~12:30 UTC, the previously diffused aerosols at around 6.3 km in height began to consolidate into a very thin, well-defined layer, still visible up to 16:00 UTC.
By 15:30 UTC, the two mid-tropospheric layers (~3.5 and 4.0 km) merged and ascended, forming an extended layer at ~3.0 to 5.5 km. Meanwhile, the lower layer at between 2.5 and 3.0 km remained persistent throughout the day. Towards the evening (~16:48 UTC), the elevated aerosol layers exhibited a gradual descent, marking the transition toward local nighttime and possibly indicating the onset of stratification and descend.
To corroborate the presence of dust aerosols, we present in Figure S3 the aerosol volume depolarization ratio (VDR) at 532 nm [76], as retrieved from the depolarization channel of AIAS at 532 nm on the same day. In this figure the VDR remains below 0.12, indicating the presence of spherical (non-dust) particles below 3.0 km in height; between 3.0 and 4.0 km in height (09:36–13:12 UTC), two distinct aerosol layers (VDR~0.2–0.3) are clearly visible, indicative of the presence of dust aerosols. After 13:12 UTC, the VDR values significantly increased (VDR~0.35–0.45), showing the arrival of purer dust particles (at 3.2–5.5 km in height), in accordance with similar observations by Papetta et al. (2024) [77].
Figure 6 presents the mean vertical profiles of the aerosol backscatter coefficient β at 532 nm (a–d) and the corresponding particle linear depolarization ratio (PLDR; e–h) for four temporal intervals on 27 September 2021: (a) 07:26–08:16, (b) 13:00–14:00, (c) 15:36–16:16, and (d) 16:31–17:07 UTC. The aerosol backscatter profiles reveal the presence of multiple aerosol layers at between ~3.0 and 6.5 km throughout the day, consistent with the structure shown in Table 2. The increased PLDR values (0.2 to 0.4) at altitudes between 3.5 and 7.0 km a.s.l. (07:26–14:00 UTC) indicate the clear presence of non-spherical particles, likely desert dust, on that day (Figure 6e,f) [78]. Later that day (15:36–17:07 UTC), the PLDR values ranged between 0.2 and 0.3 (Figure 6g,h), thus suggesting a gradual decrease in the intensity of the dust event at the same altitude heights [79].
Figure 6. Vertical profiles of the aerosol backscatter coefficient at 532 nm (ad) and the corresponding PLDR (eh) for four time intervals on 27 September 2021: (a,e) 07:2608:16 UTC, (b,f) 13:00–14:00 UTC, (c,g) 15:36–16:16 UTC, and (d,h) 16:31–17:07 UTC. The colored horizontal bars represent the studied aerosol layers.
Table 2. Geometrical and mean aerosol properties at different layers on 27 September 2021 at 15:36–16:16 UTC.
Table 2 summarizes the geometrical and mean optical properties of three aerosol layers observed on 27 September 2021 at 15:36–16:16 UTC. The first layer was located at between 2.92 and 3.70 km, with a center of mass (CoM) at 3.30 km. The second layer extended from 3.70 to 4.30 km (CoM at 4.02 km), while the third was observed higher in the atmosphere, spanning from 5.44 to 6.80 km with a CoM at 6.13 km. The aerosol backscatter coefficient (βaer) ranged from 1.02 ± 0.14 Mm−1 sr−1 in the lower layer to 0.85 ± 0.47 Mm−1 sr−1 in the uppermost one, indicating a slight decrease in aerosol load with height. The particle linear depolarization ratio (PLDR) increased with altitude from 0.11 ± 0.03 in Layer 1 to 0.32 ± 0.13 in Layer 3, corroborating the presence of non-spherical particles, indicating the strong contribution of dust aerosols of Saharan desert origin in the upper layers (Figure 3c).

3.4.2. Case 2—Saharan Dust Mixed with Biomass-Burning Particles (30 September 2021)

On 30 September 2021, the lidar observations reveal two distinct aerosol layers during the early afternoon hours (Figure 7). At 14:50 UTC, a stable lower layer is observed at between 2.0 and 3.0 km, with a second layer extending from 3.0 to 3.9 km. These layers remain well-defined until around 15:40 UTC, when a thin cloud begins to form near the top of the upper aerosol layer, around 3.8 km. This cloud, persisting until approximately 16:15 UTC, may be associated with the interaction between aerosol particles and humid air masses, thus leading to cloud formation at around 3.5 km in height.
Figure 7. Spatio-temporal evolution of the range-corrected lidar signal obtained by the AIAS lidar at 532 nm in arbitrary units (A.U.) over Helmos on 30 September 2021.
The mean vertical profiles of βaer and PLDR at 532 nm, were retrieved for the period 15:00–15:30 UTC on 30 September 2021 (Figure 8a,b). Based on Figure 8a, we observe decreasing values of βaer (3.9 to 0.9 Mm−1 sr−1) from 2 to ~3 km a.s.l., respectively; the corresponding PLDR values remain well below 0.04, indicating the presence of spherical particles [27,80], corroborating our argument of the arrival of biomass-burning particles over our observing site, as discussed in Figure 3e. Moreover, a distinct aerosol layer, decoupled from the lower one, is observed from ~3.10 to ~3.8 km (Figure 8a) and is related to the arrival of air masses rich in desert dust particles (cf. Figure 3e). However, its low PLDR value peaking at 0.078 at 3.4 km a.s.l. (Figure 8b) suggests a possible mixing of desert dust with marine aerosols (which typically exhibit PLDR values of below 0.1) during the air mass transport from the Saharan desert to Helmos over the Mediterranean Sea (see also Figure 3e). Above 3.6 km a.s.l., the PLDR values show an increasing trend but remain relatively low, reaching up to 0.1. This again suggests a possible mixing of desert dust at this height level with marine aerosols which passed over the Mediterranean Sea below the height of 1 km (Figure 3e).
Figure 8. Vertical profiles of (a) the aerosol backscatter coefficient and (b), and the corresponding PLDR at 532 nm on 30 September 2021 at 15:00–15:30 UTC. The colored horizontal bars represent the studied aerosol layers.
In Figure S4, we present the aerosol volume depolarization ratio (VDR) at 532 nm for the same day. In this figure, in contrast to Figure S3, the values of VDR remained, at all altitudes, below ~0.15 (2.0–3.5 km in height) but with slightly higher VDR values (~0.17) in the distinct aerosol layer (yellow color) between 3.0 and 3.4 km.
Table 3 summarizes the geometrical and mean optical properties of the aerosol layer observed on 30 September 2021. A single layer is identified between 3.10 and 3.88 km, with a center of mass (CoM) at 3.46 km. The βaer at 532 nm is 1.00 ± 0.44 Mm−1 sr−1, while the PLDR is relatively low (0.05 ± 0.02), indicating the predominance of spherical particles, consistent with a strong contribution of biomass-burning and marine aerosols to the desert ones.
Table 3. Geometrical and mean optical aerosol properties for 30 September 2021.

3.5. Radiative Forcing Calculations

In this Section we focus on the radiative forcing calculations performed for the dust outbreaks of 27 and 30 September 2021. More specifically, the radiative transfer analysis of the dust outbreak event on 27 September 2021 was based on the LibRadtran model to assess the atmospheric impact of dust compared to a clear-sky day used as a reference. Thus, the simulations were configured to capture the vertical profiles of irradiance, radiative flux divergence (DRF), and atmospheric heating rates. The results are presented in Figure 9 as functions of altitude from ground up to 15 km in height.
Figure 9. Vertical profiles of (a) irradiance (upward irradiance—eup, red line; downward irradiance—edn, yellow line; direct irradiance—edir, blue line), (b) the radiative forcing (RF) for shortwave (SW, red line), longwave (LW, blue line) irradiance, and the net flux (yellow), (c) the heating rate for the sum of shortwave- and longwave-loaded atmosphere (red), the sum of shortwave and longwave clear atmosphere (blue) and their difference (yellow), as estimated for 27 September 2021 over Helmos.
For the irradiance profiles presented in Figure 9a, the diffused downward irradiance (edn, yellow line) decreases monotonically with height, while the upward component (eup, red) increases due to scattering and reflection by atmospheric particles. The direct irradiance (edir, blue) exhibits rapid attenuation near the surface, indicative of substantial extinction by dust aerosols, consistent with a dense, elevated dust layer over the region. The net radiative forcing (Figure 9b) indicates regions of heating (positive) or cooling (negative). In this case, strong shortwave flux (SW, red) divergence is evident within the lower to mid-troposphere, aligned with the presence of the dust layer. Longwave divergence (LW, blue), is comparatively smaller but significant near the surface, reflecting enhanced thermal emission and absorption processes. In Figure 9c, a pronounced heating enhancement is evident between approximately 3 and 7 km altitude, peaking at around 5–6 km, coinciding with the expected vertical distribution of mineral dust. This heating is related to the absorption of shortwave radiation by dust particles, which substantially modifies the thermal structure of the atmosphere during the event. These values highlight a significant surface-dimming effect and atmospheric heating induced by the dust layer. The net negative forcing at the top of the atmosphere (TOA) implies a cooling effect on the Earth’s energy budget, while the large negative surface forcing and associated positive heating rate near the ground suggest substantial energy absorption and re-radiation within the atmosphere.
Figure 10 illustrates the atmospheric radiative impacts of a biomass-burning aerosol layer with mixtures on 30 September 2021 over Helmos, Greece, simulated using the LibRadtran radiative transfer model. Here, the upward irradiance (eup, red) increases with altitude, peaking near the top of the aerosol layer due to strong scattering and reflection by smoke particles. Downward irradiance (edn, yellow) decreases sharply with altitude, indicating substantial attenuation of solar radiation by the absorbing smoke layer. Direct irradiance (edir, blue) is drastically reduced in the lower troposphere, confirming strong absorption and scattering by the biomass-burning aerosols. The net flux (yellow) profile reveals a clear imbalance in radiative energy, leading to heating in the atmospheric column and cooling at the surface. The positive heating rate at the top of the atmosphere (0.05 K day−1) indicates a small net energy gain in the upper layers, while the surface heating rate of 2.57 K day−1 implies substantial atmospheric warming near the ground due to the aerosol presence.
Figure 10. Vertical profiles of (a) irradiance (upward irradiance—eup, red line, downward irradiance—edn, yellow line, direct irradiance—edir, blue line), (b) the radiative forcing RF for shortwave (SW, red line), longwave (LW, blue line) irradiance, and the net flux (yellow), (c) the heating rate for the sum of shortwave- and longwave-loaded atmosphere (red), the sum of shortwave and longwave clear atmosphere (blue), and their difference (yellow), as estimated for 30 September 2021 over Helmos.
Table 4 summarizes the calculated radiative forcing values for 27 and 30 September 2021. At the top of the atmosphere (TOA), the net radiative forcing was strongly negative on both days, with values of −65.51 W m−2 and −72.74 W m−2, indicating a cooling effect. At the surface, the forcing was even more negative, reaching −212.49 W m−2 on 27 September and −201.41 W m−2 on 30 September, reflecting substantial energy loss. Despite this, the heating rates show a weak warming trend at TOA, with a heating rate of +0.04 and +0.05 K day−1, while at the surface, the atmosphere experienced stronger heating of +2.55 and +2.57 K day−1, respectively.
Table 4. Radiative forcing calculations (30 September 2021).

4. Discussion

In this paper we presented two selected case studies of tropospheric aerosol transport (between 2 and 6 km a.s.l.) over the high-altitude measuring site (1800–2300 m) of Helmos during September 2021. These case studies focused on one day when Saharan dust transport was detected over the Helmos site, and one day of mixture of biomass-burning aerosols with continental ones based on the aerosol profiles measured by the mobile backscatter/depolarization aerosol AIAS lidar system at 532 nm. Moreover, we showed the spatio-temporal evolution of the vertical profiles of the aerosol optical properties (backscatter coefficient and particle linear depolarization ratio-PLDR) from ground level to typical heights of 5–6 km, showing PLDR values reaching 0.30–0.40, and VDR values mainly between 0.2 and 0.45, typical of the presence of dust aerosols.
The elevated location of the Helmos observatory played a key role in these observations. By operating above the influence of the local PBL-related air pollution, the site provided direct access to free-tropospheric aerosol layers, enabling a clearer characterization of long-range transported dust and smoke plumes. This is particularly important for lidar measurements, as operating above the PBL height reduces interference from local aerosol sources and turbulence, allowing more accurate retrievals of the optical properties which are representative of free-tropospheric aerosol layers.
Such measurements are particularly valuable in the Mediterranean basin, where the interplay between Saharan dust and biomass-burning emissions is frequent and can significantly alter regional radiative forcing. Thus, in this site, the atmospheric radiative forcing associated with the observed aerosol layers was estimated using the Libradtran radiation model, both at the top of the atmosphere (TOA) and at the Earth’s surface, along with the corresponding heating rates (HR). The results are in qualitative agreement with those reported in Kokkalis et al. (2021) [21], where a long-lasting Saharan dust event over Athens during the COVID-19 lockdown showed a net heating rate of +0.156 K day−1 at the surface and +2.543 K day−1 within 1–6 km altitude, despite reduced background air pollution.
Additionally, De Rosa et al. (2025) [81], in a 13-year lidar-based study over Potenza, Italy, estimated a negative radiative effect at both the surface and TOA across all seasons, with values reaching up to −22.08 W m−2 (surface) and −51.36 W m−2 (TOA) during summer, highlighting the significant cooling potential of dust under varying seasonal conditions. Moreover, a study conducted closer to the source regions in West Africa, Raut and Chazette (2008) [82] observed a multi-layered structure where mineral dust was situated below biomass-burning particles. They determined that this underlying dust layer acted to increase the surface albedo by 3–4%, thereby enhancing the absorption of solar radiation by the absorptive biomass-burning layer. This interaction, where one layer directly influences the radiative budget of another, highlights how the relative vertical structure of aerosols remains a key determinant of the net radiative forcing on the Earth–atmosphere system. Together, these studies confirm the strong radiative impact of Saharan dust across the Mediterranean, modulated by aerosol altitude, mixing state, and background atmospheric conditions.

5. Conclusions

Overall, our findings demonstrated the value of combining high-altitude lidar profiling with radiative transfer modeling to assess the impact of transported aerosols. The Helmos measurements highlighted, once again, the strategic importance of mountain observatories [9,43,83,84,85,86,87,88] in measuring free-tropospheric aerosol layers that are often inaccessible from low-level sites. Continuous observations at such locations will be essential for reducing uncertainties in aerosol radiative forcing and for improving our understanding of the increasingly frequent occurrence of dust–smoke mixtures in a warming Mediterranean climate [2].
The contrasting radiative behavior between pure dust and mixed aerosol layers, with dust producing a net cooling effect and mixed layers showing local heating, highlights the complexity of aerosol–radiation interactions in this region [89,90]. These observations, representing the first combined lidar and radiative forcing dataset at Helmos high-altitude station, provide a new reference point for the characterization of free-tropospheric aerosol layers over continental Greece. These results pave the way for the future integration of Helmos observations into regional and climate models to evaluate aerosol optical and radiative properties under diverse transport regimes. Such efforts will help improve the representation of dust and smoke mixtures in model simulations and refine the estimates of their radiative and climatic impacts over the Mediterranean basin [68,91].

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/rs17213607/s1, Figure S1: (a–c) Ensemble air mass trajectories arriving over Helmos at various heights on 27 September 2021 for (a–c) 08:00, (d–f) 14:00, 16:00, (g–i) 17:00 UTC calculated by the HYSPLIT air mass back trajectory model (starting point 190 h before).; Figure S2: (a–c) Ensemble air mass trajectories arriving over Helmos at various heights on 30 September 2021 for 15:00 UTC calculated by the HYSPLIT air mass back trajectory model (starting point 190 h before).; Figure S3: Spatio-temporal evolution of volume depolarization ratio obtained by the AIAS lidar at 532 nm in arbitrary units (A.U.) over Helmos on 27 September 2021.; Figure S4: Spatio-temporal evolution of volume depolarization ratio obtained by the AIAS lidar at 532 nm in arbitrary units (A.U.) over Helmos on 30 September 2021.

Author Contributions

Conceptualization, A.P.; methodology, A.P., O.S. and M.G.; software, O.S., S.S., C.-A.P. and M.G.; validation, O.S. and S.S.; formal analysis, A.P.; investigation, C.-A.P. and D.A.; resources, A.P.; data curation, C.-A.P., D.A., M.M., S.S., O.S., M.G. and R.F.; writing—original draft preparation, A.P.; writing—review and editing, M.G., O.S., S.S. and K.M.; visualization, O.S., S.S. and C.-A.P.; supervision, A.P.; project administration, A.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work has been supported by the “National Νetwork on Climate Change and its Impacts (CLIMPACT)” which is implemented under the sub-project 3 of the project “Infrastructure of national research networks in the fields of Precision Medicine, Quantum Technology and Climate Change”, funded by the Public Investment Program of Greece, General Secretary of Research and Technology/Ministry of Development and Investments. M.G. was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the fourth call for HFRI Ph.D. fellowships (fellowship number: 9293).

Data Availability Statement

The data are available upon request to the first author.

Acknowledgments

The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (http://www.ready.noaa.gov) used in this publication. The Biomedical Research Foundation of the Academy of Athens (BRFAA) is acknowledged for the provision of its mobile platform to host the NTUA AIAS lidar system. We acknowledge the use of data and/or imagery from NASA’s Fire Information for Resource Management System (FIRMS) (https://earthdata.nasa.gov/firms (accessed on 19 August 2025)), part of NASA’s Earth Observing System Data and Information System (EOSDIS).

Conflicts of Interest

Author Ourania Soupiona was employed by the company Raymetrics S.A., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Forster, P.; Ramaswamy, V.; Artaxo, P.; Berntsen, T.; Betts, R.; Fahey, D.W.; Haywood, J.; Lean, J.; Lowe, D.C.; Raga, G.; et al. Changes in Atmospheric Constituents and in Radiative Forcing; Cambridge University Press: Cambridge, UK, 2007. [Google Scholar]
  2. Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021–The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st ed.; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., et al., Eds.; Cambridge University Press: Cambridge, UK, 2023; ISBN 978-1-009-15789-6. [Google Scholar]
  3. Lohmann, U.; Feichter, J. Global Indirect Aerosol Effects: A Review. Atmos. Chem. Phys. 2005, 5, 715–737. [Google Scholar] [CrossRef]
  4. Murray, B.J.; O’Sullivan, D.; Atkinson, J.D.; Webb, M.E. Ice Nucleation by Particles Immersed in Supercooled Cloud Droplets. Chem. Soc. Rev. 2012, 41, 6519. [Google Scholar] [CrossRef] [PubMed]
  5. Haywood, J.; Boucher, O. Estimates of the Direct and Indirect Radiative Forcing Due to Tropospheric Aerosols: A Review. Rev. Geophys. 2000, 38, 513–543. [Google Scholar] [CrossRef]
  6. Andreae, M.O.; Rosenfeld, D. Aerosol–Cloud–Precipitation Interactions. Part 1. The Nature and Sources of Cloud-Active Aerosols. Earth-Sci. Rev. 2008, 89, 13–41. [Google Scholar] [CrossRef]
  7. Rosenfeld, D. Suppression of Rain and Snow by Urban and Industrial Air Pollution. Science 2000, 287, 1793–1796. [Google Scholar] [CrossRef]
  8. Gao, K.; Vogel, F.; Foskinis, R.; Vratolis, S.; Gini, M.I.; Granakis, K.; Billault-Roux, A.-C.; Georgakaki, P.; Zografou, O.; Fetfatzis, P.; et al. Biological and Dust Aerosols as Sources of Ice-Nucleating Particles in the Eastern Mediterranean: Source Apportionment, Atmospheric Processing and Parameterization. Atmos. Chem. Phys. 2024, 24, 9939–9974. [Google Scholar] [CrossRef]
  9. Gao, K.; Vogel, F.; Foskinis, R.; Vratolis, S.; Gini, M.I.; Granakis, K.; Zografou, O.; Fetfatzis, P.; Papayannis, A.; Möhler, O.; et al. On the Drivers of Ice Nucleating Particle Diurnal Variability in Eastern Mediterranean Clouds. NPJ Clim. Atmos. Sci. 2025, 8, 160. [Google Scholar] [CrossRef]
  10. Foskinis, R.; Motos, G.; Gini, M.I.; Zografou, O.; Gao, K.; Vratolis, S.; Granakis, K.; Vakkari, V.; Violaki, K.; Aktypis, A.; et al. Drivers of Droplet Formation in East Mediterranean Orographic Clouds. Atmos. Chem. Phys. 2024, 24, 9827–9842. [Google Scholar] [CrossRef]
  11. Seigel, R.B.; van den Heever, S.C.; Saleeby, S.M. Mineral Dust Indirect Effects and Cloud Radiative Feedbacks of a Simulated Idealized Nocturnal Squall Line. Atmos. Chem. Phys. 2013, 13, 4467–4485. [Google Scholar] [CrossRef]
  12. Lelieveld, J.; Berresheim, H.; Borrmann, S.; Crutzen, P.J.; Dentener, F.J.; Fischer, H.; Feichter, J.; Flatau, P.J.; Heland, J.; Holzinger, R.; et al. Global Air Pollution Crossroads over the Mediterranean. Science 2002, 298, 794–799. [Google Scholar] [CrossRef]
  13. Foskinis, R.; Gini, M.I.; Kokkalis, P.; Diapouli, E.; Vratolis, S.; Granakis, K.; Zografou, O.; Komppula, M.; Vakkari, V.; Nenes, A.; et al. On the Relation between the Planetary Boundary Layer Height and in Situ Surface Observations of Atmospheric Aerosol Pollutants during Spring in an Urban Area. Atmos. Res. 2024, 308, 107543. [Google Scholar] [CrossRef]
  14. Zografou, O.; Gini, M.; Fetfatzis, P.; Granakis, K.; Foskinis, R.; Manousakas, M.I.; Tsopelas, F.; Diapouli, E.; Dovrou, E.; Vasilakopoulou, C.N.; et al. High-Altitude Aerosol Chemical Characterization and Source Identification: Insights from the CALISHTO Campaign. Atmos. Chem. Phys. 2024, 24, 8911–8926. [Google Scholar] [CrossRef]
  15. Prospero, J.M.; Mayol-Bracero, O.L. Understanding the Transport and Impact of African Dust on the Caribbean Basin. Bull. Am. Meteorol. Soc. 2013, 94, 1329–1337. [Google Scholar] [CrossRef]
  16. Evan, A.T.; Flamant, C.; Gaetani, M.; Guichard, F. The Past, Present and Future of African Dust. Nature 2016, 531, 493–495. [Google Scholar] [CrossRef] [PubMed]
  17. Groß, S.; Freudenthaler, V.; Schepanski, K.; Toledano, C.; Schäfler, A.; Ansmann, A.; Weinzierl, B. Optical Properties of Long-Range Transported Saharan Dust over Barbados as Measured by Dual-Wavelength Depolarization Raman Lidar Measurements. Atmos. Chem. Phys. 2015, 15, 11067–11080. [Google Scholar] [CrossRef]
  18. Haarig, M.; Ansmann, A.; Engelmann, R.; Baars, H.; Toledano, C.; Torres, B.; Althausen, D.; Radenz, M.; Wandinger, U. First Triple-Wavelength Lidar Observations of Depolarization and Extinction-to-Backscatter Ratios of Saharan Dust. Atmos. Chem. Phys. 2022, 22, 355–369. [Google Scholar] [CrossRef]
  19. Chazette, P.; Totems, J. Lidar Profiling of Aerosol Vertical Distribution in the Urbanized French Alpine Valley of Annecy and Impact of a Saharan Dust Transport Event. Remote Sens. 2023, 15, 1070. [Google Scholar] [CrossRef]
  20. Amiridis, V.; Kazadzis, S.; Gkikas, A.; Voudouri, K.A.; Kouklaki, D.; Koukouli, M.-E.; Garane, K.; Georgoulias, A.K.; Solomos, S.; Varlas, G.; et al. Natural Aerosols, Gaseous Precursors and Their Impacts in Greece: A Review from the Remote Sensing Perspective. Atmosphere 2024, 15, 753. [Google Scholar] [CrossRef]
  21. Kokkalis, P.; Soupiona, O.; Papanikolaou, C.A.; Foskinis, R.; Mylonaki, M.; Solomos, S.; Vratolis, S.; Vasilatou, V.; Kralli, E.; Anagnou, D.; et al. Radiative Effect and Mixing Processes of a Long-Lasting Dust Event over Athens, Greece, during the COVID-19 Period. Atmosphere 2021, 12, 318. [Google Scholar] [CrossRef]
  22. Papayannis, A.; Balis, D.; Amiridis, V.; Chourdakis, G.; Tsaknakis, G.; Zerefos, C.; Castanho, A.D.A.; Nickovic, S.; Kazadzis, S.; Grabowski, J. Measurements of Saharan Dust Aerosols over the Eastern Mediterranean Using Elastic Backscatter-Raman Lidar, Spectrophotometric and Satellite Observations in the Frame of the EARLINET Project. Atmos. Chem. Phys. 2005, 5, 2065–2079. [Google Scholar] [CrossRef]
  23. Papayannis, A.; Amiridis, V.; Mona, L.; Tsaknakis, G.; Balis, D.; Bösenberg, J.; Chaikovski, A.; De Tomasi, F.; Grigorov, I.; Mattis, I.; et al. Systematic Lidar Observations of Saharan Dust over Europe in the Frame of EARLINET (2000–2002). J. Geophys. Res. Atmos. 2008, 113, D10204. [Google Scholar] [CrossRef]
  24. Soupiona, O.; Samaras, S.; Ortiz-Amezcua, P.; Böckmann, C.; Papayannis, A.; Moreira, G.A.; Benavent-Oltra, J.A.; Guerrero-Rascado, J.L.; Bedoya-Velásquez, A.E.; Olmo, F.J.; et al. Retrieval of Optical and Microphysical Properties of Transported Saharan Dust over Athens and Granada Based on Multi-Wavelength Raman Lidar Measurements: Study of the Mixing Processes. Atmos. Environ. 2019, 214, 116824. [Google Scholar] [CrossRef]
  25. Soupiona, O.; Papayannis, A.; Kokkalis, P.; Foskinis, R.; Sánchez Hernández, G.; Ortiz-Amezcua, P.; Mylonaki, M.; Papanikolaou, C.-A.; Papagiannopoulos, N.; Samaras, S.; et al. EARLINET Observations of Saharan Dust Intrusions over the Northern Mediterranean Region (2014–2017): Properties and Impact on Radiative Forcing. Atmos. Chem. Phys. 2020, 20, 15147–15166. [Google Scholar] [CrossRef]
  26. Barreto, Á.; Cuevas, E.; García, R.D.; Carrillo, J.; Prospero, J.M.; Ilić, L.; Basart, S.; Berjón, A.J.; Marrero, C.L.; Hernández, Y.; et al. Long-Term Characterisation of the Vertical Structure of the Saharan Air Layer over the Canary Islands Using Lidar and Radiosonde Profiles: Implications for Radiative and Cloud Processes over the Subtropical Atlantic Ocean. Atmos. Chem. Phys. 2022, 22, 739–763. [Google Scholar] [CrossRef]
  27. Janicka, L.; Stachlewska, I.S.; Veselovskii, I.; Baars, H. Temporal Variations in Optical and Microphysical Properties of Mineral Dust and Biomass Burning Aerosol Derived from Daytime Raman Lidar Observations over Warsaw, Poland. Atmos. Environ. 2017, 169, 162–174. [Google Scholar] [CrossRef]
  28. Granados-Muñoz, M.J.; Navas-Guzmán, F.; Guerrero-Rascado, J.L.; Bravo-Aranda, J.A.; Binietoglou, I.; Pereira, S.N.; Basart, S.; Baldasano, J.M.; Belegante, L.; Chaikovsky, A.; et al. Profiling of Aerosol Microphysical Properties at Several EARLINET/AERONET Sites during the July 2012 ChArMEx/EMEP Campaign. Atmos. Chem. Phys. 2016, 16, 7043–7066. [Google Scholar] [CrossRef]
  29. Chazette, P.; Marnas, F.; Totems, J. The Mobile Water Vapor Aerosol Raman LIdar and Its Implication in the Framework of the HyMeX and ChArMEx Programs: Application to a Dust Transport Process. Atmos. Meas. Tech. 2014, 7, 1629–1647. [Google Scholar] [CrossRef]
  30. Dulac, F.; Hamonou, E.; Sauvage, S.; Debevec, C. Introduction to Volume 1 of Atmospheric Chemistry in the Mediterranean Region and to the Experimental Effort During the ChArMEx Decade. In Atmospheric Chemistry in the Mediterranean Region: Volume 1-Background Information and Pollutant Distribution; Dulac, F., Sauvage, S., Hamonou, E., Eds.; Springer International Publishing: Cham, Switzerland, 2023; pp. 1–25. ISBN 978-3-031-12741-0. [Google Scholar]
  31. Heintzenberg, J. The SAMUM-1 Experiment over Southern Morocco: Overview and Introduction. Tellus B Chem. Phys. Meteorol. 2009, 61, 2–11. [Google Scholar] [CrossRef]
  32. Petzold, A.; Rasp, K.; Weinzierl, B.; Esselborn, M.; Hamburger, T.; Dörnbrac, A.; Kandler, K.; Schütz, L.; Knippertz, P.; Fiebig, M.; et al. Saharan Dust Absorption and Refractive Index from Aircraft-Based Observations during SAMUM 2006. Tellus B Chem. Phys. Meteorol. 2009, 61, 118. [Google Scholar] [CrossRef]
  33. Groß, S.; Esselborn, M.; Abicht, F.; Wirth, M.; Fix, A.; Minikin, A. Airborne High Spectral Resolution Lidar Observation of Pollution Aerosol during EUCAARI-LONGREX. Atmos. Chem. Phys. 2013, 13, 2435–2444. [Google Scholar] [CrossRef]
  34. Adebiyi, A.A.; Kok, J.F. Climate Models Miss Most of the Coarse Dust in the Atmosphere. Sci. Adv. 2020, 6, eaaz9507. [Google Scholar] [CrossRef]
  35. Bellouin, N.; Quaas, J.; Gryspeerdt, E.; Kinne, S.; Stier, P.; Watson-Parris, D.; Boucher, O.; Carslaw, K.S.; Christensen, M.; Daniau, A.-L.; et al. Bounding Global Aerosol Radiative Forcing of Climate Change. Rev. Geophys. 2020, 58, e2019RG000660. [Google Scholar] [CrossRef] [PubMed]
  36. Heinold, B.; Baars, H.; Barja, B.; Christensen, M.; Kubin, A.; Ohneiser, K.; Schepanski, K.; Schutgens, N.; Senf, F.; Schrödner, R.; et al. Important Role of Stratospheric Injection Height for the Distribution and Radiative Forcing of Smoke Aerosol from the 2019–2020 Australian Wildfires. Atmos. Chem. Phys. 2022, 22, 9969–9985. [Google Scholar] [CrossRef]
  37. Liu, C.-C.; Portmann, R.W.; Liu, S.; Rosenlof, K.H.; Peng, Y.; Yu, P. Significant Effective Radiative Forcing of Stratospheric Wildfire Smoke. Geophys. Res. Lett. 2022, 49, e2022GL100175. [Google Scholar] [CrossRef]
  38. Iacono, M.J.; Delamere, J.S.; Mlawer, E.J.; Shephard, M.W.; Clough, S.A.; Collins, W.D. Radiative Forcing by Long-Lived Greenhouse Gases: Calculations with the AER Radiative Transfer Models. J. Geophys. Res. Atmos. 2008, 113, D13103. [Google Scholar] [CrossRef]
  39. Pérez-Ramírez, D.; Whiteman, D.N.; Veselovskii, I.; Ferrare, R.; Titos, G.; Granados-Muñoz, M.J.; Sánchez-Hernández, G.; Navas-Guzmán, F. Spatiotemporal Changes in Aerosol Properties by Hygroscopic Growth and Impacts on Radiative Forcing and Heating Rates during DISCOVER-AQ 2011. Atmos. Chem. Phys. 2021, 21, 12021–12048. [Google Scholar] [CrossRef]
  40. Barragan, R.; Molero, F.; Granados-Muñoz, M.J.; Salvador, P.; Pujadas, M.; Artíñano, B. Feasibility of Ceilometers Data to Estimate Radiative Forcing Values: Application to Different Conditions around the COVID-19 Lockdown Period. Remote Sens. 2020, 12, 3699. [Google Scholar] [CrossRef]
  41. Miri, R.; Pujol, O.; Hu, Q.; Goloub, P.; Veselovskii, I.; Podvin, T.; Ducos, F. Innovative Aerosol Hygroscopic Growth Study from Mie–Raman–Fluorescence Lidar and Microwave Radiometer Synergy. Atmos. Meas. Tech. 2024, 17, 3367–3375. [Google Scholar] [CrossRef]
  42. Veselovskii, I.; Hu, Q.; Goloub, P.; Podvin, T.; Boissiere, W.; Korenskiy, M.; Kasianik, N.; Khaykyn, S.; Miri, R. Derivation of Depolarization Ratios of Aerosol Fluorescence and Water Vapor Raman Backscatters from Lidar Measurements. Atmos. Meas. Tech. 2024, 17, 1023–1036. [Google Scholar] [CrossRef]
  43. Hu, Q.; Goloub, P.; Veselovskii, I.; Bravo-Aranda, J.-A.; Popovici, I.E.; Podvin, T.; Haeffelin, M.; Lopatin, A.; Dubovik, O.; Pietras, C.; et al. Long-Range-Transported Canadian Smoke Plumes in the Lower Stratosphere over Northern France. Atmos. Chem. Phys. 2019, 19, 1173–1193. [Google Scholar] [CrossRef]
  44. Bohlmann, S.; Shang, X.; Vakkari, V.; Giannakaki, E.; Leskinen, A.; Lehtinen, K.E.J.; Pätsi, S.; Komppula, M. Lidar Depolarization Ratio of Atmospheric Pollen at Multiple Wavelengths. Atmos. Chem. Phys. 2021, 21, 7083–7097. [Google Scholar] [CrossRef]
  45. Collaud Coen, M.; Andrews, E.; Aliaga, D.; Andrade, M.; Angelov, H.; Bukowiecki, N.; Ealo, M.; Fialho, P.; Flentje, H.; Hallar, A.G.; et al. Identification of Topographic Features Influencing Aerosol Observations at High Altitude Stations. Atmos. Chem. Phys. 2018, 18, 12289–12313. [Google Scholar] [CrossRef]
  46. Katsivela, E.; Chatoutsidou, S.E.; Saridaki, A.; Raisi, L.; Stathopoulou, P.; Tsiamis, G.; Kunfeng, G.; Fetfatzis, P.; Romanos, F.; Gidarakou, M.; et al. Airborne Microorganisms at Hellenic Atmospheric Aerosol and Climate Change Station in Helmos Mountain (Greece). ACS Earth Space Chem. 2025, 9, 1801–1814. [Google Scholar] [CrossRef] [PubMed]
  47. Rose, C.; Collaud Coen, M.; Andrews, E.; Lin, Y.; Bossert, I.; Lund Myhre, C.; Tuch, T.; Wiedensohler, A.; Fiebig, M.; Aalto, P.; et al. Seasonality of the Particle Number Concentration and Size Distribution: A Global Analysis Retrieved from the Network of Global Atmosphere Watch (GAW) near-Surface Observatories. Atmos. Chem. Phys. 2021, 21, 17185–17223. [Google Scholar] [CrossRef]
  48. Wandinger, U.; Ansmann, A. Experimental Determination of the Lidar Overlap Profile with Raman Lidar. Appl. Opt. 2002, 41, 511. [Google Scholar] [CrossRef]
  49. Freudenthaler, V.; Linné, H.; Chaikovski, A.; Rabus, D.; Groß, S. EARLINET Lidar Quality Assurance Tools. Atmos. Meas. Tech. Discuss. 2018; preprint. [Google Scholar]
  50. Freudenthaler, V.; Esselborn, M.; Wiegner, M.; Heese, B.; Tesche, M.; Ansmann, A.; Müller, D.; Althausen, D.; Wirth, M.; Fix, A.; et al. Depolarization Ratio Profiling at Several Wavelengths in Pure Saharan Dust during SAMUM 2006. Tellus B Chem. Phys. Meteorol. 2009, 61, 165–179. [Google Scholar] [CrossRef]
  51. Papayannis, A.; Kokkalis, P.; Mylonaki, M.; Soupiona, R.; Papanikolaou, C.A.; Foskinis, R.; Giakoumaki, A. Recent Upgrades of the EOLE and AIAS Lidar Systems of the National Technical University of Athens Operating Since 2000 in Athens, Greece. EPJ Web Conf. 2020, 237, 02030. [Google Scholar] [CrossRef]
  52. Mylonaki, M.; Papayannis, A.; Anagnou, D.; Veselovskii, I.; Papanikolaou, C.-A.; Kokkalis, P.; Soupiona, O.; Foskinis, R.; Gidarakou, M.; Kralli, E. Optical and Microphysical Properties of Aged Biomass Burning Aerosols and Mixtures, Based on 9-Year Multiwavelength Raman Lidar Observations in Athens, Greece. Remote Sens. 2021, 13, 3877. [Google Scholar] [CrossRef]
  53. D’Amico, G.; Amodeo, A.; Baars, H.; Binietoglou, I.; Freudenthaler, V.; Mattis, I.; Wandinger, U.; Pappalardo, G. EARLINET Single Calculus Chain–Overview on Methodology and Strategy. Atmos. Meas. Tech. 2015, 8, 4891–4916. [Google Scholar] [CrossRef]
  54. Mattis, I.; D’Amico, G.; Baars, H.; Amodeo, A.; Madonna, F.; Iarlori, M. EARLINET Single Calculus Chain–Technical–Part 2: Calculation of Optical Products. Atmos. Meas. Tech. 2016, 9, 3009–3029. [Google Scholar] [CrossRef]
  55. Solomos, S.; Spyrou, C.; Barreto, A.; Rodríguez, S.; González, Y.; Neophytou, M.K.A.; Mouzourides, P.; Bartsotas, N.S.; Kalogeri, C.; Nickovic, S.; et al. The Development of METAL-WRF Regional Model for the Description of Dust Mineralogy in the Atmosphere. Atmosphere 2023, 14, 1615. [Google Scholar] [CrossRef]
  56. LeGrand, S.L.; Polashenski, C.; Letcher, T.W.; Creighton, G.A.; Peckham, S.E.; Cetola, J.D. The AFWA Dust Emission Scheme for the GOCART Aerosol Model in WRF-Chem v3.8.1. Geosci. Model Dev. 2019, 12, 131–166. [Google Scholar] [CrossRef]
  57. Drakaki, E.; Amiridis, V.; Tsekeri, A.; Gkikas, A.; Proestakis, E.; Mallios, S.; Solomos, S.; Spyrou, C.; Marinou, E.; Ryder, C.L.; et al. Modeling Coarse and Giant Desert Dust Particles. Atmos. Chem. Phys. 2022, 22, 12727–12748. [Google Scholar] [CrossRef]
  58. Spyrou, C.; Solomos, S.; Bartsotas, N.S.; Douvis, K.C.; Nickovic, S. Development of a Dust Source Map for WRF-Chem Model Based on MODIS NDVI. Atmosphere 2022, 13, 868. [Google Scholar] [CrossRef]
  59. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 Global Reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
  60. Thompson, G.; Field, P.R.; Rasmussen, R.M.; Hall, W.D. Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization. Mon. Weather. Rev. 2008, 136, 5095–5115. [Google Scholar] [CrossRef]
  61. Grell, G.A.; Freitas, S.R. A Scale and Aerosol Aware Stochastic Convective Parameterization for Weather and Air Quality Modeling. Atmos. Chem. Phys. 2014, 14, 5233–5250. [Google Scholar] [CrossRef]
  62. Janjic, Z.I. The Surface Layer in the NCEP Eta Model. In Proceedings of the Eleventh Conference on Numerical Weather Prediction, Norfolk, VA, USA, 19–23 August 1996; American Meteorological Society: Boston, MA, USA, 1996; pp. 354–355. [Google Scholar]
  63. Janjic, Z. Nonsingular Implementation of the Mellor-Yamada Level 2.5 Scheme in the NCEP Meso Model; NCEP Office Note No. 437; National Centers for Environmental Prediction: College Park, MD, USA, 2002; p. 61. [Google Scholar]
  64. Tewari, M.; Chen, F.; Wang, W.; Dudhia, J.; LeMone, M.A.; Gayno, G.; Wegiel, J.; Cuenca, R.H. 14.2A Implementation and Verification of the Unified Noah Land Surface Model in the WRF Model; CiNii: Online, 2004. [Google Scholar]
  65. Mesinger, F. Forecasting Upper Tropospheric Turbulence within the Framework of the Mellor-Yamada 2.5 Closure. Res. Activ. Atmos. Oceanic Mod. 2020, 18, 4.28–4.29. [Google Scholar]
  66. Janjic, Z.I. The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes. Mon. Weather. Rev. 1994, 122, 927–945. Available online: https://journals.ametsoc.org/view/journals/mwre/122/5/1520-0493_1994_122_0927_tsmecm_2_0_co_2.xml (accessed on 12 September 2025). [CrossRef]
  67. Ginoux, P.; Chin, M.; Tegen, I.; Prospero, J.M.; Holben, B.; Dubovik, O.; Lin, S.-J. Sources and Distributions of Dust Aerosols Simulated with the GOCART Model. J. Geophys. Res. Atmos. 2001, 106, 20255–20273. [Google Scholar] [CrossRef]
  68. Nabat, P.; Somot, S.; Mallet, M.; Chiapello, I.; Morcrette, J.J.; Solmon, F.; Szopa, S.; Dulac, F.; Collins, W.; Ghan, S.; et al. A 4-D Climatology (1979–2009) of the Monthly Tropospheric Aerosol Optical Depth Distribution over the Mediterranean Region from a Comparative Evaluation and Blending of Remote Sensing and Model Products. Atmos. Meas. Tech. 2013, 6, 1287–1314. [Google Scholar] [CrossRef]
  69. Levy, R.C.; Remer, L.A.; Kleidman, R.G.; Mattoo, S.; Ichoku, C.; Kahn, R.; Eck, T.F. Global Evaluation of the Collection 5 MODIS Dark-Target Aerosol Products over Land. Atmos. Chem. Phys. 2010, 10, 10399–10420. [Google Scholar] [CrossRef]
  70. Tanré, D.; Kaufman, Y.J.; Herman, M.; Mattoo, S. Remote Sensing of Aerosol Properties over Oceans Using the MODIS/EOS Spectral Radiances. J. Geophys. Res. Atmos. 1997, 102, 16971–16988. [Google Scholar] [CrossRef]
  71. Stein, A.F.; Draxler, R.R.; Rolph, G.D.; Stunder, B.J.B.; Cohen, M.D.; Ngan, F. NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System. Bull. Am. Meteorol. Soc. 2015, 96, 2059–2077. [Google Scholar] [CrossRef]
  72. Soupiona, O.; Papayannis, A.; Kokkalis, P.; Mylonaki, M.; Tsaknakis, G.; Argyrouli, A.; Vratolis, S. Long-Term Systematic Profiling of Dust Aerosol Optical Properties Using the EOLE NTUA Lidar System over Athens, Greece (2000–2016). Atmos. Environ. 2018, 183, 165–174. [Google Scholar] [CrossRef]
  73. Emde, C.; Buras-Schnell, R.; Kylling, A.; Mayer, B.; Gasteiger, J.; Hamann, U.; Kylling, J.; Richter, B.; Pause, C.; Dowling, T.; et al. The libRadtran Software Package for Radiative Transfer Calculations (Version 2.0.1). Geosci. Model Dev. 2016, 9, 1647–1672. [Google Scholar] [CrossRef]
  74. Koepke, P.; Gasteiger, J.; Hess, M. Technical Note: Optical Properties of Desert Aerosol with Non-Spherical Mineral Particles: Data Incorporated to OPAC. Atmos. Chem. Phys. 2015, 15, 5947–5956. [Google Scholar] [CrossRef]
  75. Mayer, B.; Kylling, A.; Emde, C.; Hamann, U.; Buras, R.; Gasteiger, J.; Jakub, F.; Richter, B. libRadtran User’s Guide; Ludwig-Maximilians-Universität München: Munich, Germany, 2017. [Google Scholar]
  76. Freudenthaler, V. About the Effects of Polarising Optics on Lidar Signals and the Δ90 Calibration. Atmos. Meas. Tech. 2016, 9, 4181–4255. [Google Scholar] [CrossRef]
  77. Papetta, A.; Marenco, F.; Kezoudi, M.; Mamouri, R.-E.; Nisantzi, A.; Baars, H.; Popovici, I.E.; Goloub, P.; Victori, S.; Sciare, J. Lidar Depolarization Characterization Using a Reference System. Atmos. Meas. Tech. 2024, 17, 1721–1738. [Google Scholar] [CrossRef]
  78. Gidarakou, M.; Papayannis, A.; Kokkalis, P.; Evangeliou, N.; Vratolis, S.; Remoundaki, E.; Groot Zwaaftink, C.; Eckhardt, S.; Veselovskii, I.; Mylonaki, M.; et al. Optical and Microphysical Properties of the Aerosols during a Rare Event of Biomass-Burning Mixed with Polluted Dust. Atmosphere 2024, 15, 190. [Google Scholar] [CrossRef]
  79. Papagiannopoulos, N.; Mona, L.; Amodeo, A.; D’Amico, G.; Gumà Claramunt, P.; Pappalardo, G.; Alados-Arboledas, L.; Guerrero-Rascado, J.L.; Amiridis, V.; Kokkalis, P.; et al. An Automatic Observation-Based Aerosol Typing Method for EARLINET. Atmos. Chem. Phys. 2018, 18, 15879–15901. [Google Scholar] [CrossRef]
  80. Murayama, T.; Müller, D.; Wada, K.; Shimizu, A.; Sekiguchi, M.; Tsukamoto, T. Characterization of Asian Dust and Siberian Smoke with Multi-Wavelength Raman Lidar over Tokyo, Japan in Spring 2003. Geophys. Res. Lett. 2004, 31, L23103. [Google Scholar] [CrossRef]
  81. De Rosa, B.; Amodeo, A.; D’Amico, G.; Papagiannopoulos, N.; Rosoldi, M.; Veselovskii, I.; Cardellicchio, F.; Falconieri, A.; Gumà-Claramunt, P.; Laurita, T.; et al. Characterization of Fresh and Aged Smoke Particles Simultaneously Observed with an ACTRIS Multi-Wavelength Raman Lidar in Potenza, Italy. Remote Sens. 2025, 17, 2538. [Google Scholar] [CrossRef]
  82. Raut, J.-C.; Chazette, P. Radiative Budget in the Presence of Multi-Layered Aerosol Structures in the Framework of AMMA SOP-0. Atmos. Chem. Phys. 2008, 8, 6839–6864. [Google Scholar] [CrossRef]
  83. Ketterer, C.; Zieger, P.; Bukowiecki, N.; Collaud Coen, M.; Maier, O.; Ruffieux, D.; Weingartner, E. Investigation of the Planetary Boundary Layer in the Swiss Alps Using Remote Sensing and In Situ Measurements. Bound.-Layer Meteorol. 2014, 151, 317–334. [Google Scholar] [CrossRef][Green Version]
  84. Nyeki, S.; Eleftheriadis, K.; Baltensperger, U.; Colbeck, I.; Fiebig, M.; Fix, A.; Kiemle, C.; Lazaridis, M.; Petzold, A. Airborne Lidar and In-Situ Aerosol Observations of an Elevated Layer, Leeward of the European Alps and Apennines. Geophys. Res. Lett. 2002, 29, 33-1–33-34. [Google Scholar] [CrossRef]
  85. Bukowiecki, N.; Weingartner, E.; Gysel, M.; Coen, M.C.; Zieger, P.; Herrmann, E.; Steinbacher, M.; Ga¨ggeler, H.W.; Baltensperger, U. A Review of More than 20 Years of Aerosol Observation at the High Altitude Research Station Jungfraujoch, Switzerland (3580 m Asl). Aerosol Air Qual. Res. 2016, 16, 764–788. [Google Scholar] [CrossRef]
  86. Cristofanelli, P.; Landi, T.C.; Calzolari, F.; Duchi, R.; Marinoni, A.; Rinaldi, M.; Bonasoni, P. Summer Atmospheric Composition over the Mediterranean Basin: Investigation on Transport Processes and Pollutant Export to the Free Troposphere by Observations at the WMO/GAW Mt. Cimone Global Station (Italy, 2165 m a.s.l.). Atmos. Environ. 2016, 141, 139–152. [Google Scholar] [CrossRef]
  87. Gallagher, J.P.; McKendry, I.G.; Macdonald, A.M.; Leaitch, W.R. Seasonal and Diurnal Variations in Aerosol Concentration on Whistler Mountain: Boundary Layer Influence and Synoptic-Scale Controls. J. Appl. Meteorol. Climatol. 2011, 50, 2210–2222. [Google Scholar] [CrossRef]
  88. Hamburger, T.; Matisāns, M.; Tunved, P.; Ström, J.; Calderon, S.; Hoffmann, P.; Hochschild, G.; Gross, J.; Schmeissner, T.; Wiedensohler, A.; et al. Long-Term in Situ Observations of Biomass Burning Aerosol at a High Altitude Station in Venezuela – Sources, Impacts and Interannual Variability. Atmos. Chem. Phys. 2013, 13, 9837–9853. [Google Scholar] [CrossRef]
  89. Mallet, M.; Dulac, F.; Formenti, P.; Nabat, P.; Sciare, J.; Roberts, G.; Pelon, J.; Ancellet, G.; Tanré, D.; Parol, F.; et al. Overview of the Chemistry-Aerosol Mediterranean Experiment/Aerosol Direct Radiative Forcing on the Mediterranean Climate (ChArMEx/ADRIMED) Summer 2013 Campaign. Atmos. Chem. Phys. 2016, 16, 455–504. [Google Scholar] [CrossRef]
  90. Di Biagio, C.; Formenti, P.; Balkanski, Y.; Caponi, L.; Cazaunau, M.; Pangui, E.; Journet, E.; Nowak, S.; Andreae, M.O.; Kandler, K.; et al. Complex Refractive Indices and Single-Scattering Albedo of Global Dust Aerosols in the Shortwave Spectrum and Relationship to Size and Iron Content. Atmos. Chem. Phys. 2019, 19, 15503–15531. [Google Scholar] [CrossRef]
  91. Ryder, C.L.; McQuaid, J.B.; Flamant, C.; Rosenberg, P.D.; Washington, R.; Brindley, H.E.; Highwood, E.J.; Marsham, J.H.; Parker, D.J.; Todd, M.C.; et al. Advances in Understanding Mineral Dust and Boundary Layer Processes over the Sahara from Fennec Aircraft Observations. Atmos. Chem. Phys. 2015, 15, 8479–8520. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Multiple requests from the same IP address are counted as one view.