Volcanic emission estimates from the inversion of ACTRIS lidar observations and their use for quantitative dispersion modeling

Atmos. Chem. Phys., 25, 7343–7368, 2025 https://doi.org/10.5194/acp-25-7343-2025 © Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License. Research article Volcanic emission estimates from the inversion of ACTRIS lidar observations and their use for quantitative dispersion modeling Anna Kampouri1,2, Vassilis Amiridis1, Thanasis Georgiou1,3, Stavros Solomos4, Anna Gialitaki1,5, Maria Tsichla1,6, Michael Rennie7, Simona Scollo8, and Prodromos Zanis2 1Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing (IAASARS), National Observatory of Athens, 10560 Athens, Greece 2Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece 3Laboratory of Atmospheric Physics, School of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece 4Research Centre for Atmospheric Physics and Climatology, Academy of Athens, 10680 Athens, Greece 5Department of Physics and Astronomy, Earth Observation Science Group, University of Leicester, Leicester LE1 7RH, UK 6Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, Greece 7European Centre for Medium-Range Weather Forecasts, Reading RG2 9AX, UK 8Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, 95125 Catania, Italy Correspondence: Anna Kampouri ([email protected]) Received: 14 October 2024 – Discussion started: 3 January 2025 Revised: 28 March 2025 – Accepted: 28 March 2025 – Published: 14 July 2025 Abstract. Modeling the dispersion of volcanic particles following explosive eruptions is critical for aviation safety. To constrain the dispersion of volcanic plumes and assess hazards, calculations rely on the accurate characterization of the eruption’s source term, e.g., variation in emission rate and column height with time and the prevailing wind fields. This study introduces an inverse modeling framework that integrates a Lagrangian dispersion model with lidar observations to estimate emission rates of volcanic particles released during an Etna eruption. The methodology consists of using the FLEXPART model to generate source–receptor relationships (SRRs) between the volcano and the lidar system that observed the volcanic plume. These SRRs are then used to derive the emission rates based on observational data, including volcanic ash plume heights from the INGVEO observatory and PollyXT lidar retrievals. We leverage data from the ACTRIS PollyXT lidar that operates at the PANhellenic GEophysical observatory of Antikythera of the National Observatory of Athens (PANGEANOA). The inversion algorithm utilizes lidar observations and an empirical a priori emission profile to estimate the volcanic particle source strength, accounting for altitude and time of the plume’s evolution. Additionally, to study the impact that the wind fields have on volcanic ash forecasting, the experiment is repeated using fields that assimilate Aeolus wind lidar data. Our approach applied to the 12 March 2021 Etna eruption and accurately captures a dense aerosol layer between 8 and 12km above the PANGEA-NOA station. Results show a minimal difference of the order of 2% between the observed and the simulated ash concentrations. Furthermore, the structure of the a posteriori ash plume closely resembles the ash cloud image captured by the SEVIRI satellite above Antikythera island, highlighting the novelty of the inversion results. The presented inversion algorithm, coupled with Aeolus data, optimizes both the vertical emission distribution and Etna emission rates, advancing our understanding and preparedness for volcanic events. Published by Copernicus Publications on behalf of the European Geosciences Union. 7344 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations 1 Introduction Volcanic ash constitutes a significant hazard to aviation when it is emitted at aircraft cruising altitudes (9–11km), with potential consequences including aircraft engine failure (Guffanti et al., 2005), inaccurate readings of critical navigational instruments, and reduced visibility due to external aircraft corrosion (Clarkson and Simpson, 2017; ICAO, 2016). In the case of a volcanic eruption, urgent decisions are necessary to determine safe flight routes and to ensure that airborne aircraft land safely. While safety remains the top priority, the grounding and rerouting of flights lead to large financial losses, e.g., the 2010 eruption of Eyjafjallajökull in Iceland reportedly cost the airline industry over USD 1 billion (Mazzocchi et al., 2010; Oxford Economics, 2012). Information on volcanic ash dispersion after an eruption is provided to operators by specialized early warning systems (EWSs) operated by the Volcanic Ash Advisory Centers (VAACs) (Fearnley et al., 2018). These systems typically rely on deterministic volcanic ash transport and dispersion models (VATDM) to offer short-term forecasts of the volcanic ash cloud. Although VAACs specify the expected location of the ash cloud, usually they do not provide quantitative information about ash concentration. In the spotlight of the expected rise in the number of flights over volcanically active regions in the near future (as indicated by EUROCONTROL, 2022), the probability of encountering volcanic ash at aircraft cruising altitudes will proportionally increase. Consequently, the challenge is to minimize uncertainties in short-term forecasts of volcanic ash dispersion. The primary sources of uncertainties in deterministic transport models originate from the eruption source parameters, the various model parameterizations (such as wet deposition), and the driving meteorological conditions (Dacre et al., 2011; Prata and Lynch, 2019; Stohl et al., 2011). Typically, volcanic ash transport and dispersion models (VATDMs) require the specification of parameters about the volcanic event, including a vertical profile of ash emission rates, particle size distribution, and ash density (Harvey et al., 2020). The eruption start time can be estimated through satellite observations or by local volcano observatories. Various remote sensing techniques exist to estimate the height of the ash plume (Petersen et al., 2012). It should be mentioned that information that relies on observations from passive sensors has limited sensitivity to the ash layer height. Mass eruption rates are typically evaluated using empirical relationships based on observed plume heights (Mastin et al., 2009). However, these empirical relationships often fail to consider secondary factors influencing plume height, such as meteorological conditions. The long-range transport of volcanic particles is influenced by tropospheric and/or stratospheric winds, particularly the vertical wind shear, which is frequently inaccurately represented in numerous numerical weather prediction (NWP) models (Harvey et al., 2020; Houchi et al., 2010; Stoffelen et al., 2020). Moreover, volcanic particles can influence the planetary radiative balance through both direct and indirect effects, introducing significant uncertainties in plume dispersion and lifetime. The direct effect involves the scattering and absorption of solar and terrestrial radiation, where fine ash and sulfate aerosols contribute to surface cooling or atmospheric warming depending on particle composition, size distribution, and injection plume height (Robock, 2000; Sicard et al., 2025). The indirect effect relates to the role of volcanic particles in cloud microand macrophysical properties. Ash particles can act as cloud condensation nuclei (CCN), facilitating water droplet formation and, under specific pressure and temperature conditions, as ice nuclei (IN) (Guerrieri et al., 2023). These processes can alter cloud optical and microphysical properties, enhance cloud reflectivity, affect cloud lifetimes, and increase the uncertainties in radiative transfer. Additionally, volcanic ice clouds can hide possible ash layers and pose a severe threat to aviation safety. Atmospheric transport models often struggle to account for these complex interactions, leading to uncertainties in plume evolution, trajectory forecasts, and deposition estimates. Furthermore, the absence of significant physical processes, dependence on empirical relations, and data from previous eruptions further contribute to substantial uncertainties in estimates of the erupted mass. Over the past two decades, significant progress has been made in integrating remote sensing data into atmospheric transport models to enhance the forecasting of volcanic emissions and their dispersion. Satellite observations from both polar-orbiting and geosynchronous thermal infrared instruments have been used to retrieve ash mass loadings (Clarisse et al., 2010; Pavolonis et al., 2013; Prata and Prata, 2012). Additional sensors, including the Moderate Resolution Imaging Spectrometer (MODIS), Second Generation Spinning Enhanced Visible and Infra-Red Imager (SEVIRI), Atmospheric Infra-Red Sounder (AIRS), Ozone Monitoring Instrument (OMI), Multi-angle Imaging SpectroRadiometer (MISR), and CALIOP lidar on board the CALIPSO have provided valuable data on volcanic ash detection and retrievals (Eckhardt et al., 2008; Francis et al., 2012). A comprehensive discussion on the application of satellite remote sensing for volcanic ash monitoring in aviation hazard mitigation is provided by Prata (2009). In addition, ground-based lidar networks, such as the European Aerosol Research Lidar Network (EARLINET), have played a crucial role in validating the accuracy of transport model outputs and improving dispersion simulations by providing high-resolution vertical profiles of volcanic aerosols (Pappalardo et al., 2004). Advancements in atmospheric transport and dispersion modeling have further facilitated the integration of these observational datasets. Models like the Numerical Atmos. Chem. Phys., 25, 7343–7368, 2025 https://doi.org/10.5194/acp-25-7343-2025 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations 7345 Atmospheric-dispersion Modelling Environment (NAME; Jones et al., 2007) (which is used operationally by the London Volcanic Ash Advisory Centre, or LVAAC), the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Stein et al., 2015), and the FLEXible PARTicle (FLEXPART) dispersion model (Eckhardt et al., 2008; Kristiansen et al., 2010, 2012, 2014; Stohl et al., 2011) have been extensively used for volcanic ash forecasting, often incorporating satellite and lidar data to refine model inputs and improve predictive accuracy. The integration of remote sensing data into atmospheric transport models has been significantly advanced through inversion algorithms. In previous studies (Eckhardt et al., 2008; Kristiansen et al., 2010), inversion algorithms were developed using satellite column retrievals and tested to estimate the vertical distribution of sulfur dioxide (SO2) emission rates for quasi-instantaneous volcanic eruptions such as the 2007 Jebel at Tair and the 2008 Kasatochi eruptions. Seibert et al. (2011) examined the uncertainties of the various configurations for the 2008 Kasatochi case study and expanded the method to estimate the uncertainty of the retrieved source emissions (a posteriori uncertainties). The inversion algorithm was further used by Stohl et al. (2011) for volcanic ash emission rates as a function of altitude and time, while Kristiansen et al. (2012) improved the volcanic ash inversion techniques using various inputs to better constrain the 2010 Eyjafjallajökull eruption. Amiridis et al. (2023) demonstrated that volcanic ash early warning systems can be significantly enhanced by the assimilation of Aeolus wind fields. Notably, these improvements are most pronounced over under-sampled geographical regions, such as the Mediterranean Sea, as volcanoes are often situated in remote areas lacking surface-based observation networks. Moreover, the study indicates that the positive effect of Aeolus wind data assimilation is more pronounced in the middle and upper troposphere (mostly between 7 and 15km) compared to the lower troposphere. This may highlight under-sampling issues, since the in situ observations (like radiosondes) traditionally used for data assimilation exhibit lower vertical resolution in the upper troposphere (Rennie et al., 2021). Considering that volcanic ash plumes are typically injected into upper-tropospheric and lower-stratospheric heights, their transport is largely influenced by upper-tropospheric winds, hence accuracy in dispersion modeling is advanced from high-accuracy wind field assimilation. Building on these advancements, our study further investigates improvements in ash emission estimations by developing an inversion method that integrates Aeolus wind data, ground-based lidar observations, and transport model simulations. This approach aims to enhance the accuracy of volcanic emission source terms and to reduce uncertainties in dispersion forecasting. We specifically focus on the Etna eruption that occurred on 12 March 2021, coinciding with the investigations provided by Amiridis et al. (2023) and Kampouri et al. (2023). During this event, Aeolus had a close overpass to Etna, providing valuable observations around the volcano. Additionally, the transported volcanic plume was captured in the eastern region of the Mediterranean by the ground-based PollyXT lidar system of the PANhellenic GEophysical observatory of Antikythera of the National Observatory of Athens (PANGEANOA) in Greece, downwind of the Etna volcano. This allows for direct comparisons of observations against forecasts, with and without the assimilation of Aeolus data, denoted as “w” and “w/o” Aeolus, respectively (as indicated in the studies by Amiridis et al., 2023; Kampouri et al., 2023). 2 The case of the Etna volcanic eruption of 12–14 March 2021 2.1 Volcanic activity Mt. Etna in Italy, recognized as one of the most active volcanoes on Earth, has undergone significant volcanic activity, particularly since February 2021. During this period, the stratovolcano experienced numerous paroxysmal episodes, leading to frequent tephra and SO2emissions. A notable event occurred on 12 March 2021, marking one of the most powerful lava fountain episodes observed at the southeastern crater since 2020 (Calvari et al., 2021). The volcanic activity started with Strombolian-type eruptions at around 02:35UTC, escalating in both frequency and intensity until 07:35UTC, when surveillance cameras from the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV-OE) (Corradini et al., 2018; Scollo et al., 2019) captured the formation of a sustained lava fountain. Throughout the paroxysmal phase, the eruptive column gradually reached a height of 9kma.s.l.(Fig. 1). The variation in the eruption column was detected by the visual surveillance camera in the CUAD in Catania (ECV), calibrated by INGV-OE (Fig. 1). The volcanic plume drifted eastwards under the influence of the prevailing westerly winds dominant in the eastern Mediterranean region at the time. According to the Volcano Observatory Notice for Aviation (VONA) messages, the INGV-EO observatory (INGV, 2025; Corradini et al., 2018; Scollo et al., 2019) issued a red warning alert, from 06:18 to 08:44UTC, on 12 March 2021, when the strongest ash emission was observed, while an orange alert was issued at 12:30UTC when the lava fountain ceased, and the volcanic ash plume was dispersed into the atmosphere (Calvari et al., 2021). Additionally, the eruptive activity resulted in abundant tephra fallout, covering several towns on the eastern flank of the volcano crater, with a lava flow field expanding on the eastern and northeastern flank. In this study, the cloud heights reported by VONA are used as a priori information to initialize the volcanic ash dispersion simulations, conducted with the FLEXPART (flexible particle dispersion) Lagrangian model (Brioude et al., 2013; Pisso et al., 2019; Stohl et al., 2005). The FLEXPART ash transhttps://doi.org/10.5194/acp-25-7343-2025 Atmos. Chem. Phys., 25, 7343–7368, 2025 7346 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations Figure 1. Etna activity on 12 March 2021 as seen from INGV-OE. Ash plume images from an ECV calibrated camera monitored the explosive volcanic activity between 5 and 9 kma.s.l.(a) Weak ash plume at 06:30UTC, with an upper part aligning more vertically. (b) Strong vertical plume at 08:00UTC, which shifted eastward. (c) Strong ash plume at 09:00UTC, with a lower and more diluted cloud caused by the lava flow expanding eastward. (d) Decrease in the explosive activity after 10:00UTC. Figures are taken from the INGV-OE automatic system described in Corradini et al. (2018) and Scollo et al. (2019). port model is driven by wind fields simulated by the WRF regional meteorological model (version 4) (Skamarock et al., 2019), which, in turn, derives initial and boundary conditions from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) (ECMWF, 2021a) global model (for additional information, see Sect. 3.3). 3 Methods and data The inverse method employed in this study to estimate volcanic ash emissions integrates a priori information on ash emissions, ground-based lidar observations, and simulations with a dispersion model, resulting in improved ash emission estimates. Figure A1 presents a schematic workflow outlining the methodology followed in this study, providing a clear overview of the steps involved in our approach. In this section, we describe the datasets and methods employed in the inverse modeling process. 3.1 PANGEA-NOA ground-based data (lidar PollyXT) The PANGEA-NOA observatory established its first operations in June 2018 on the remote island of Antikythera, Greece. The atmospheric circulation pattern at PANGEAAtmos. Chem. Phys., 25, 7343–7368, 2025 https://doi.org/10.5194/acp-25-7343-2025 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations 7347 NOA favors the transport of air masses carrying an abundance of different aerosol types such as windblown Sahara dust, Etna volcanic aerosols, smoke from wildfires, and anthropogenic pollution from major cities. Hence, this coastal site constitutes an ideal place to study natural aerosols under the prevailing background conditions of the eastern Mediterranean. The Mediterranean region, particularly its eastern basin, serves as a confluence of air masses originating from Europe, Asia, and Africa. In this region, anthropogenic emissions from large urban centers interact with natural emissions from the Saharan and Middle Eastern deserts, smoke from frequent wildfires, and volcanic particles from eruptions, notably from Mt. Etna and Icelandic volcanoes. Additionally, the atmosphere over the eastern Mediterranean contains background marine aerosols and pollen particles from oceanic and vegetative sources. Aerosols exert a variety of effects on the regional weather and climate, impacting solar radiation, visibility, and human health, and they pose significant concerns for aviation safety (WMO, 2024). The eastern Mediterranean is characterized by a Mediterranean climate, with hot, dry summers and mild, wet winters. This seasonal variability is driven primarily by the interaction between mid-latitude westerlies and subtropical high-pressure systems (Lensky et al., 2018). During winter, the region experiences the frequent passage of extratropical cyclones originating from the North Atlantic and Mediterranean storm tracks, bringing precipitation and colder temperatures. In contrast, summer conditions are dominated by the expansion of the subtropical height, leading to stable atmospheric conditions and minimal rainfall (ECMWF, 2010). Synoptic-scale circulation in the eastern Mediterranean plays a crucial role in shaping weather patterns and atmospheric dynamics. The atmospheric circulation over the eastern Mediterranean is dominated by persistent northerly and westerly winds, favoring the advection of volcanic products from Etna to Greece (Kampouri et al., 2020; Scollo et al., 2013). Research has identified several dominant synoptic types that influence the region, including cyclonic systems, anticyclonic patterns, and blocking heights (Rousi et al., 2014). These circulation patterns significantly impact the transport of aerosols, moisture, and pollutants, affecting regional air quality and climate variability. Furthermore, the region’s proximity to large-scale circulation features such as the subtropical jet stream and the African monsoon system contributes to complex seasonal interactions (Lensky et al., 2018). Currently, a PollyXT lidar system (Baars et al., 2017; Engelmann et al., 2016) and a sun–sky photometer of CIMEL Electronique (Giles et al., 2019; Goloub et al., 2007) operate continuously at PANGEA-NOA to provide profiles and columnar aerosol properties with high accuracy and resolution. PollyXT is a multi-wavelength Raman polarization lidar with 24/7 remote operation capability. The system operates in 355, 532, and 1064nm and is equipped with 12 detectors to measure light elastically and inelastically (at 387, 407, and 607nm), backscattered from atmospheric constituents. Polarization capability also enables the detection and vertical separation of non-spherical (e.g., volcanic ash, dust) from spherical aerosols (e.g., smoke, pollution, marine particles). The CIMEL sun–sky photometer measures direct solar and sky radiance at several wavelengths (340, 380, 440, 500, 675, 870, 1020, and 1640nm) to derive column-integrated aerosol optical and microphysical properties (Dubovik et al., 2006). Observations from both sensors are of strong interest for Pan-European and global networks such as the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS-RI), the European Aerosol Research Lidar Network (EARLINET), and the global AErosol RObotic NETwork (AERONET: https://aeronet.gsfc.nasa.gov/, last access: 27 June 2025). In all of these networks, measurements taken at PANGEA-NOA are submitted on a regular basis. 3.1.1 Ash mass calculations using remote sensing data Volcanic ash mass estimates were derived from a combination of PollyXT lidar measurements and sun photometer observations. First, the lidar measurements were averaged over the 3 h period when the volcanic layer was observed above Antikythera, and the standardized EARLINET algorithm Single Calculus Chain (SCC) (D’Amico et al., 2015) was used to derive the particle backscatter coefficient (βp) and particle linear depolarization ratio (δp) profiles. These profiles were then used to disentangle the contribution of large, non-spherical ash particles from the observed volcanic plume and to calculate the ash mass concentration with the POlarization-LIdar PHOtometer Networking (POLIPHON) method (Ansmann et al., 2012; Mamouri and Ansmann, 2017), tailored for Etna ash, as described in Kampouri et al. (2020). More specifically, the following equation was used: ma=ρa×cv,a(λ)×βp,a(h,λ)×Sp,a(h,λ),(1) where mis the mass concentration, aindicates an aerosol type, ρrepresents the particle mass density (for volcanic ash particles, this is 2.6±0.6 gm−3following the study of Ansmann et al., 2011a), λis the wavelength, cv(λ) is the socalled volume-to-extinction conversion factor (derived from sun photometer measurements), his the height above ground, and Sp(λ,h) is the ratio of the particle extinction to particle backscatter coefficient (lidar ratio). As the macalculation is sensitive to the aerosol type, under the simultaneous presence of multiple aerosol components in the atmospheric column, a decomposition of the total particle backscatter coefficient βpis needed prior to the mass concentration calculation. In POLIPHON, this decomposition is supported for up to two aerosol types, one exhibiting large particle depolarization ratio values (usually dust https://doi.org/10.5194/acp-25-7343-2025 Atmos. Chem. Phys., 25, 7343–7368, 2025 7348 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations Table 1. Parameters used for lidar profile decomposition and mass concentration calculation. ρα[µmcm−3]cν,α,532nm δp,α,532nm(h)Sp,α,532nm(h) [sr] Ash particles 2.6±0.6 0.6±0.1 0.36±0.02 50±10 Sulfates 1.5±0.3 0.18±0.02 0.05±0.01 60±20 or volcanic ash) and one that does not (marine, continental, or tropospheric smoke and their mixtures). To separate the contribution of the depolarizing (βp,d(h,λ)) and the nondepolarizing (βp,nd(h,λ)) aerosol component from the total particle backscatter coefficient, we apply the following equations: βp,d(h,λ)=βp(h,λ) ×(δp(h,λ)−δp,nd(h,λ))(1+δp,d(h,λ)) (δp,d(h,λ)−δp,nd(h,λ))(1+δp(h,λ)) (2) βp,nd(h,λ)=βp(h,λ)−βp,d(h,λ).(3) PollyXT lidar signals are sensitive to aerosol particles in the radius range from about 50nm to a few micrometers (Weitkamp, 2005). For FLEXPART, the size range considered for volcanic ash particles is between 5 and 21 µm in diameter and thus within the range that is detectable by PollyXT. Uncertainties in the ash mass concentration calculation using the POLIPHON method arise from the input parameter errors that propagate into Eq. (1) and are expected to be in the order of ∼40% (Ansmann et al., 2011b). The technique has been validated against synergistic retrievals that combine multi-wavelength lidar and sun–sky radiometer observations (sensitive up to 15µm in particle radius (Lopatin et al., 2013, 2021)) for dust and volcanic ash particles and has been found to perform well (Konsta et al., 2021; Wagner et al., 2013). In Table 1, we summarize the values and uncertainties of the parameters used as input for the above. The lidar ratio of coarse-mode volcanic ash at 532nm is reported to range between 40 and 60sr in the literature (see for example Groß et al., 2012; Table 3 for particle extinction and backscatter values in Floutsi et al., 2023; and Gasteiger et al., 2011). For the fine-mode aerosols, we use a mean value of 60 sr, following the values reported in the literature for particles of a sulfuring nature (see for example Floutsi et al., 2023; Müller et al., 2007). We also account for a lidar ratio retrieval uncertainty of ∼30% to capture the measurement range (Ansmann et al., 2012; Giannakaki et al., 2015; Groß et al., 2013). The particle density values ρfollow from the OPAC model for the coarse-mode mineral component and the water-soluble component for ash and sulfate particles, respectively (Hess et al., 1998; Koepke et al., 2015). For the water-soluble component, we assume values at a relative humidity of 0%, which is considered representative of the altitudes of the volcanic layers. The coarse-mode component is not considered to be hydrophilic. Finally, the extinctionto-mass conversion factors cv are taken from Ansmann et al. (2011a) for ash and fine-mode particles, respectively. 3.2 Aeolus high-spectral-resolution lidar (HSRL) data Aeolus, the wind mission of the European Space Agency (ESA), carried the world’s first high-spectral-resolution Doppler wind lidar in space (Stoffelen et al., 2006; StraumeLindner et al., 2021). Launched in August 2018, Aeolus’s aim was to retrieve horizontal wind profiles in the troposphere and lower stratosphere. The mission’s primary objective was to showcase this innovative technology in space to enhance weather forecasts and to advance our understanding of atmospheric dynamics, particularly in the tropics. Additionally, Aeolus aimed to contribute valuable insights into the intricate interactions between the atmospheric constituents, water cycles, and broader climate system (Rennie et al., 2021; Straume-Lindner et al., 2021). Aeolus wind data demonstrated notable quality and coverage, leading to substantial enhancements in NWP forecasts, particularly within the tropics and Southern Hemisphere. The improvement in wind forecast ranges from 0.5% to 2%, in terms of root mean square error, maintains a significant impact even in medium-range weather forecasting. The most substantial impact was observed at approximately 100hPa in the tropics, particularly over the eastern Pacific Ocean. This is attributed, in part, to the tropics having a relatively limited coverage of high-quality radiosonde wind profiles. Additionally, the wind field in the tropics is less constrained by temperature information from other satellites (Rennie et al., 2021). Furthermore, Aeolus had the capability to retrieve aerosol and cloud profiles, offering valuable data for assimilation or evaluation in volcanic ash dispersion modeling. It is essential to note, however, that these retrievals face limitations due to the absence of a dedicated lidar channel for detecting crosspolarized light returns (with respect to the emitted radiation). This absence is particularly crucial for capturing the backscattered light from non-spherical particles like volcanic ash. Consequently, caution is advised when utilizing Aeolus observations in such cases. Despite this limitation, the Aeolus mission demonstrated its efficacy in enhancing wind forecasts, particularly over under-sampled regions, such as the tropics (Rennie et al., 2021). Similarly, Aeolus can be used over under-sampled remote areas with active volcanoes, contributing to improved simulations of volcanic ash dispersion following eruptions. Atmos. Chem. Phys., 25, 7343–7368, 2025 https://doi.org/10.5194/acp-25-7343-2025 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations 7349 Table 2. Configuration of the PP schemes for the WRF-ARW simulations. PP Schemes References Microphysics (MP) Thompson Thompson et al. (2008) Surface layer (SFL) Monin–Obukhov (Janjic Eta) Janjic (2002) Planetary boundary layer (PBL) Mellor–Yamada–Janjic (MYJ) Janjic (2003) Cumulus parameterization (CUM) Tiedtke Zhang et al. (2011) Longwave and shortwave radiation (RAD) Rapid radiative transfer model (RRTM) Iacono et al. (2008) Land surface (LSM) NOAH Chen and Dudhia (2001) 3.3 FLEXPART-WRF model setup To perform meteorological simulations over the study region of the eastern Mediterranean, the Advanced Research WRF model (version 4) (Skamarock et al., 2019) is used. The spatial resolution of the model is 12km×12 km for a total of 351×252 grid points and 31 vertical levels (up to 50hPa). The simulation period starts on 12 March 2021 at 00:00UTC (6 h earlier than the FLEXPART runs to accommodate the model’s 12 h spin-up) and ends on 14 March 2021 at 18:00UTC, with hourly outputs. Table 2 summarizes the physics parameterization (PP) schemes for the WRF-ARW simulations. In the context of this study, two versions (ECMWF, 2021a) of the initial and boundary condition fields from the IFS were utilized. These fields, provided at a spatial resolution of 0.125°×0.125°, with 137 vertical model levels, serve as inputs for the WRF-ARW regional model. One version incorporates assimilated Aeolus Rayleigh-clear and Mie-cloudy horizontal line-of-sight (HLOS) L2B wind profiles (referred to as the “w” Aeolus experiment), while the other version is without Aeolus data (referred to as the “w/o” Aeolus experiment). The initial conditions without Aeolus assimilation adhere to the model setup utilized in the observing system experiments (OSEs) conducted by Stoffelen et al. (2006). The WRF-ARW runs rely on initial and boundary conditions generated from ECMWF-IFS, with boundary conditions updated at 6-hour intervals. Sea surface temperature (SST) analysis data, obtained from the Copernicus Marine Environment Monitoring Service (CMEMS) at a spatial resolution of 1/12° supplement these simulations. The WRFARW model configuration utilized in this study is consistent with that employed in the study of Amiridis et al. (2023). The volcanic ash plume transport simulations were done with the Lagrangian particle dispersion model FLEXPART (Brioude et al., 2013; Pisso et al., 2019; Stohl et al., 2005) in a forward mode. These simulations rely on hourly meteorological fields from the WRF-ARW model, initiated with IFS datasets. The use of 1-hourly WRF meteorological fields at a 12km ×12 km spatial resolution allows for a more detailed representation of the volcanic plume dispersion. The initial simulations, in which we used an a priori emission profile for the eruption emissions taken from VONA alerts (from now on referred to as “a priori volcanic ash plume transport”), were initiated at the reported start time of the eruption at 07:00UTC on 12 March 2021 and were completed at 00:00UTC on 14 March 2021, with a total of 100000 particles released in each forecast. The model layers were divided into 18 layers with 1km vertical resolution in the range extending from 1 to 18km above ground level (a.g.l.). We estimate the a priori mass eruption rate (MER) for ash particles following Mastin et al. (2009) and Scollo et al. (2019) by inverting the observed plume heights over the Etna summit crater from the VONA reports and field observations, as observed by the INGV observatory, using the 1D plume model of Degruyter and Bonadonna (2012). The initial injection height in the model is set to the altitude of the Etna summit craters (3.3kma.s.l.) up to 9 kma.s.l., based on the VONA reports (Corradini et al., 2018; Scollo et al., 2019) and field observations. Also, the gravitational particle settling (Näslund and Thaning, 1991) was determined assuming spherical particles with a density of 2450kgm−3. The particle density value used in the FLEXPART model differs slightly from the density used in Table 1 (2.6±0.6gcm−3) due to differences in shape assumptions, size distributions, and literature sources referenced in various calculations. The size distribution of volcanic ash particles was described using four size bins (3, 5, 9, and 21µm in diameter), as these cover the size distribution relevant to long-range transport (≤25µm diameter) (Beckett et al., 2022; Dacre et al., 2011; Durant et al., 2010). To derive the source–receptor relationships (SRRs), the FLEXPART-WRF model was used, once again in a forward mode (see Appendix A, Fig. A2), considering the same four ash size bins as those used in the a priori volcanic ash plume transport. The SRR model data, which represent all potential dispersion scenarios of the ash plume, are compared with the lidar retrievals at PANGEA-NOA. For each grid point in the considered domain, the FLEXPART ash column loadings released from one particular emission time and height are matched with the corresponding time and grid point of the lidar ash mass retrieval. The FLEXPART SRRs were driven by the same hourly meteorological fields from the WRF-ARW model, utilizing both control and assimilated datasets (ECMWF, 2021) to quantitatively evaluate the impact of data assimilation. Subsequently, these SRRs were used to initialize the inversion alhttps://doi.org/10.5194/acp-25-7343-2025 Atmos. Chem. Phys., 25, 7343–7368, 2025 7350 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations gorithm, constrained by the PollyXT ground-based lidar measurements of volcanic particles. It was assumed that the ash emissions occurred between the ground and 16kma.g.l.over the Etna volcano. The total height range was discretized into 79 layers of 200m thickness. For each layer, 150 000 unit mass particle traces were uniformly released along a vertical line source every 2 h (from 04:00 to 06:00UTC until 12:00 to 14:00 UTC). Additionally, the model layers were divided into 74: 70 layers between 200m and 14 km, with a vertical resolution of 200m; 3 layers between 14 and 16 kma.g.l.(per 1km); and another layer from 22 to 50kma.g.l.These model-derived column values represent source–receptor relationships, since they were obtained with a unit mass as the source. The actual mass released at each level is determined through the inversion. Following the inversion, a single, longer “posteriori” simulation over the period 12 to 14 March 2021 was made, releasing 200000 particles according to the estimated emission profile. The output from this simulation was produced at the same vertical and horizontal resolution as the a priori FLEXPART simulation. 3.4 Inversion algorithm The inversion method employed here for ash source estimations is based on a cost function minimization approach. Similar work has been done by Eckhardt et al. (2008), Kristiansen et al. (2010), and Stohl et al. (2011). In these studies, an inversion algorithm was developed to calculate the vertical distribution of sulfur dioxide and the ash emission rates for instantaneous volcanic eruptions. Satellite retrievals, typically of ash column loading, have been combined in those analyses with VATDM simulations using inversion techniques to provide time-evolving estimates of these significant quantities. In satellite retrieval techniques, numerous advantages exist where estimates of ash cloud top height and ash column loading are typically available (Francis et al., 2012; Pavolonis et al., 2013). Additionally, MER can be estimated through empirical relationships under specific assumptions, which are especially useful when satellite images are unavailable or limited, such as during the early stages of an eruption (Pouget et al., 2013; Prata et al., 2022). However, direct retrievals of the vertical distribution within the eruption column are not feasible. Ground-based and airborne radar observations, which are sensitive to larger particles and can penetrate optically thick plumes, provide a complementary source of information to retrieve near-source plume properties such as mass eruption rate and column height. The present study brings together (i) the inverse modeling by initiating the inversion simulations with mass concentrations derived from ground-based lidar observations downwind, combined with the source–receptor relationships calculated from the FLEXPART-WRF model, and (ii) the integration of Aeolus meteorological wind fields (ECMWF, 2021a) into the FLEXPART-WRF model (for more detail, see Sect. 3.3). The overarching goal is to optimize both the vertical emission distribution and the ash emission rates near the source, following the volcanic eruption. From the inversion scheme, a total ash emission profile of the eruption is obtained, which can be utilized to generate robust ash forecasts constrained by lidar observations. We perform the inversion using a Bayesian approach to provide the best estimate of the emissions profile for fine ash (with particles 3, 5, 9, and 21µm in diameter) that can be transported over long distances. We follow the general concept of source–receptor relationships (Seibert and Frank, 2004), where the relations between each measurement and a potential source of the emission is calculated (here using FLEXPART-WRF) and stored as the source–receptor matrix (SRM) for each vertical level and for four ash size bins (as described in Sect. 3.3). The n=79 unknowns (source elements) are put into a state vector xwhile the observed values mare put into a vector yo, where the subscript “o” stands for the PollyXT lidar observations. Then, the state vector can be calculated from the inversion of a forward model Mthat connects yoand x, as follows: yo=M(x)+ey,(4) implying a linear relationship in which yois a vector of spatiotemporal lidar measurements; Mis the n×mSRM calculated by FLEXPART-WRF, describing the sensitivity of each observation to a unit release rate; eyrepresents lidar measurement errors, which are not accounted for in the algorithm; and xis the ash emission vector to be estimated. M(x) is equivalent to running a VATDM with xas the input release profile. Since Mis calculated using such a model, it inherits the biases that are inevitable in VATDMs. As a result, it may diverge from the true dispersion and may not necessarily align with the observations on the left-hand side of Eq. (4), even if it is the true release profile (Fang et al., 2022). Given that the problem is underdetermined, the solution of the linear inverse problem in Eq. (4) is not straightforward, and further assumptions are needed. The most common are assumptions imposed on the unknown emission vector xsuch as the non-negativity of its elements, smoothness of the emission (Fang et al., 2022), or measurement/emission sparsity (Li et al., 2018) (e.g., the assumption that the emission element remains zero unless other evidence is present in measured and modeled data). Under these assumptions, the problem in Eq. (4) can be solved by minimizing the distance between the left and the right sides of the equation. To enhance the stability of the inversion outcome, a priori emissions are also used, representing our best estimate of xbefore the observations are made (see Sect. 3.4.1). Including an explicit a priori source vector xa, we can express the equation as follows: M(x−xa)≈yo−Mxa,(5) Atmos. Chem. Phys., 25, 7343–7368, 2025 https://doi.org/10.5194/acp-25-7343-2025 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations 7351 and as an abbreviation M˜ x≈˜ y.(6) The inversion scheme presented here is done by minimizing a cost function C, which comprises the following system of equations: C1=yo−MTx(7) C2=x−xa(8) C3=Dx.(9) C1quantifies the difference between the modeled data and the observations, C2is the deviation from the a priori estimations, and C3imposes a smoothness regularization term. The cost function Cfirst calculates the misfit C1between the profiles at the receptor points, as observed by the lidar (yo) and the data as modeled by FLEXPART (MTx). The second term C2(Eq. 8) accounts for the difference between the a posteriori estimates of the emission rates xand the a priori estimates xa. (For details on the calculation of the a priori vector, see Sect. 3.4.1.) To enforce smoothness in the vertical profile of emissions, a regularization parameter C3is introduced, derived from a discrete second-order difference operator D(Eq. 9). Drepresents a tridiagonal matrix where the main diagonal elements are equal to −2, elements of the diagonals above and below are equal to 1 (discrete representation of the second derivative), and is a regularization parameter that determines the weight of this smoothness constraint relative to the other two terms. The final mass emission rates are obtained by minimizing the total cost function Cusing a standard optimization routine with the a priori emission rates as the initial guess. This approach ensures that the calculated ash emission rates are consistent with both the observed data and the a priori emission estimates, while also favoring a smooth vertical distribution of emissions. The inversion scheme presented in this study is not limited to Mt. Etna but can be applied to other volcanic eruptions worldwide, provided that suitable observational data are available. The methodology relies on ground-based lidar measurements, dispersion modeling (FLEXPART-WRF), and an inversion algorithm to estimate volcanic ash emissions. Therefore, it can be adapted to different volcanic settings where lidar observations or other remote sensing data like satellite-based lidars (CALIPSO, EarthCARE) and geostationary satellites (SEVIRI) are available to constrain the source term. Additionally, the approach can be extended to a regional or global scale by integrating multiple observation sites from lidar networks such as ACTRIS/EARLINET or incorporating additional satellite data. This would allow for improved ash emission estimates for various volcanic eruptions worldwide. Furthermore, the use of high-resolution wind field data (such as Aeolus or future wind lidar missions) can enhance the accuracy of dispersion forecasts in different geographic regions (possibly lacking sufficient information from radiosondes), making the methodology widely applicable for volcanic ash monitoring and forecasting. 3.4.1 A priori source emissions xa To constrain the variability of the retrieved parameters and to enhance the stability of the inversion outcome, a priori emissions are also used in the inversion scheme. We determine the a priori MER for ash particles following the approach outlined by Scollo et al. (2019) by inverting the observed plume heights over the Etna summit crater from VONA reports and field observations as observed by the INGV observatory, using the 1D plume model (Degruyter and Bonadonna, 2012) as described in Sect. 3.3. Additionally, the London VAAC employs the same empirical relationship between observed plume heights and eruptive mass, as proposed by Mastin et al. (2009), assuming a uniform vertical ash distribution. The column heights of the ash plume from 12 March 2021 were obtained from the ECV calibrated camera operated by INGV-OE (Calvari et al., 2021; Corradini et al., 2018; Scollo et al., 2019) during the time period of 06:30 to 10:30UTC (see Table A1). The ash plume height reached up to 9.0kma.s.l.In order to calculate the a priori emissions, the data were resampled at ∼2h intervals, specifically at 06:00 (from 06:30 to 07:45UTC), 08:00 (from 08:00 to 09:45UTC), and 10:00 UTC (from 10:00 to 10:30 UTC). During the initial hours of the eruption (06:30–07:45UTC), the ash plume was weak (Fig. 1a and Table A1) with an average column injection height of 5.8km, resulting in an estimated MER of approximately 12000 kgs−1according to the equation by Mastin et al. (2009) (Table 3). After 07:45UTC, a stronger plume formed, extending vertically above the vent (Fig. 1b and Table A1). The ash plume exceeded the ECV camera field of view (e.g., more than 9.0– 9.5kma.s.l.) and was particularly strong between 08:00 and 09:45UTC (Fig. 1c and Table A1). The MER during this period averaged 58800kgs−1, with a mean plume height of 10kma.s.l.The standard deviation of the mean MER indicates considerable inconsistency in the emissions, as the MER can change rapidly during an eruption due to fluctuations in the eruptive dynamics, such as the collapse of the eruption column (Table 3). The ash plume height began to decrease several minutes after the lava fountain ceased (Fig. 1d and Table A1), with its disappearance becoming evident only after 10:15UTC (Fig. 1d). The MER during this phase (10:00–10:30UTC) was approximately 6300 kgs−1 (Table 3). The maximum plume elevation was not recorded by the ECV camera due to its limited field of view (approx. 9.0–9.5kma.s.l.as noted by Scollo et al., 2014). However, according to SEVIRI aboard the geostationary Meteosat Second Generation satellite, the volcanic ash cloud top height (ACTH) between 08:15 and 08:45UTC was estimated at 11.5kma.s.l.(Calvari et al., 2021). This higher SEVIRIhttps://doi.org/10.5194/acp-25-7343-2025 Atmos. Chem. Phys., 25, 7343–7368, 2025 7358 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations 5 Conclusions and discussion The present study presented an inversion method to estimate the volcanic emission rate profile with a Lagrangian particle dispersion model and a ground-based lidar system. The technique was applied to the case study of the explosive eruption of Mt. Etna, Italy, on 12 March 2021. To assess the impact of Aeolus wind assimilation in volcanic ash dispersion forecasts, the simulation was repeated twice: once with Aeolus data assimilated (“w” experiment) and once without (“w/o”). The volcanic aerosol layers observed above the PANGEANOA station in Antikythera, along with the clear-sky conditions in the days after the eruption, made this an ideal test case. Important conclusions from our work are as follows: The PollyXT lidar system of PANGEA-NOA detected a dense aerosol layer between 8 and 12km, with the volcanic ash plume primarily concentrated between 9 and 11km. FLEXPART simulations, both a priori (with an empirical emission profile) and a posteriori (with the emission profile produced by the inversion algorithm) were conducted to derive the modeled plume’s vertical distribution and concentration. The a priori “w” Aeolus simulation showed a broader dispersion of the ash plume, potentially due to the overestimation of the a priori ash emissions obtained by inverting the observed plume heights from the VONA reports, whereas the a posteriori simulation, based on the inversion results, produced a more refined and consistent ash plume profile, confined to a smaller area, mostly around Antikythera and southern Greece, which was closely similar to the ash cloud observed by the SEVIRI satellite. In terms of ash mass concentration, the a priori profile with Aeolus wind data assimilated shows a good spatiotemporal agreement with the lidar retrievals but exhibited a slight vertical shift of 1km with respect to the observed ash mass peaks (Amiridis et al., 2023) along with a misfit in mass concentrations of about 50µgm−3, a critical factor for aviation safety. In contrast, the a posteriori ash mass concentrations demonstrate a better agreement with the observations above PANGEA-NOA when Aeolus winds are assimilated. The maximum ash mass concentration is found close to 255µgm−3at 9.8 km, closely matching the peak observed by the lidar, depicting a minimal difference of the order of 2 % between the observed and the a posteriori simulated ash mass concentrations. In contrast, the difference between the lidar observations and the a priori ash simulations ranged from 28% to 40 %. This consistency highlights the robustness of the new inversion algorithm and the significant improvement in the vertical distribution and the ash mass concentration. However, additional independent datasets, such as groundbased, satellite remote sensing data, or airborne in situ measurements along the plume’s trajectory, would further enhance the validation of this methodology and should be considered in future studies. To further assess the reliability of the retrieved emissions, a Monte Carlo error propagation analysis was conducted, introducing normally distributed perturbations to the lidar measurements. With this method, the standard deviation of the retrieved emissions at each height level was estimated. The results indicate that the inversion output remained highly stable, with minimal variation across Monte Carlo realizations, suggesting that the single-station observational setup does not introduce significant uncertainty. To enhance the sensitivity of the inversion framework and provide a more comprehensive uncertainty assessment, multiple lidar stations or complementary remote sensing techniques are essential. The accuracy of the FLEXPART a posteriori simulation is highly dependent on the precision of the driving meteorological fields (“w” Aeolus wind fields), as well as on volcano source parameters such as the plume height and mass eruption rates, which are refined through the inversion process (a posteriori MER). The advantages of Aeolus wind assimilation for global NWP models have been well documented, particularly by Rennie et al. (2021), who demonstrated significant improvements in wind field representation, especially in the tropics and Southern Hemisphere. Further enhancements in wind forecasts were observed in the study of Amiridis et al. (2023), where regional NWP models benefited from Aeolus wind assimilation. Our case study validates these findings, showing that the assimilation of Aeolus wind profiles leads to a significant improvement in the estimation of volcanic emission rates in the vertical distribution, optimizing the agreement between lidar observations and a posteriori model simulation. Real-time applications, such as those of VAACs, demand a rapid response to volcanic ash hazards. Once the plume is detected and initial data from lidar systems become available, the presented method can quickly provide the necessary information to calculate the current and future position and extent of the plume within a few hours. This underscores the imperative for high-quality, rapidly accessible data, such as that provided by organized ground-based lidar networks employing standardized algorithms and procedures, such as those used by EARLINET, a key component of the ACTRIS infrastructure. However, their applicability to the proposed methodology depends on the operation of a backscatter-depolarization lidar, which constitutes the primary requirement. In cases where direct measurements of essential parameters, such as lidar ratios, are unavailable, values from the scientific literature can be used. A more advanced configuration, incorporating Raman lidar capabilities, would enhance the accuracy of retrieved backscatter and lidar ratio coefficients. Additionally, for daytime measurements, a co-located sun photometer would facilitate the direct estimation of the conversion factors required in the inversion process. Beyond ground-based applications, the methodology is also applicable to spaceborne aerosol lidars, which provide vertical profiles of the backscatter coefficient and particle linear depolarization ratio, both fundamental parameters for the inversion process. Atmos. Chem. Phys., 25, 7343–7368, 2025 https://doi.org/10.5194/acp-25-7343-2025 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations 7359 Furthermore, the methodology presented herein can be applied to current or future satellite missions that employ lidar measurements (e.g., the EarthCARE mission). While passive satellites offer near-global coverage of ash cloud measurements within minutes to hours, ground-based or satellite lidar systems provide more accurate direct retrievals of the vertical distribution within the ash plume column. Our methodology is broadly applicable and efficient enough for real-time implementation. It can supply ash forecasting models with an objectively derived quantitative source term, leading to improved forecasts that are critical for the aviation sector. These enhanced forecasts provide more effective emergency responses, ensuring safer and more efficient flight operations during volcanic eruptions, while at the same time minimizing the risk of accidents and the financial impact of flight cancelations. Appendix A The SRR for a size distribution of volcanic ash particles with four size bins (3, 5, 9, and 21µm diameter), derived from the FLEXPART model using the “w” Aeolus assimilated wind fields, indicate that the volcanic emissions observed above the PANGEA-NOA observatory (receptor – yaxis) on 12 March 2021 (from 18:30 to 21:30UTC) at the height range of 6–12km mostly originate from release heights between 5 and 11.5km above the Etna volcano (source – xaxis) (Fig. A2a, c, and e). These source release heights are consistent with the observed emissions above the PANGEANOA station, particularly when the particle release time was 06:00–08:00 and 08:00–10:00UTC. The source heights for the fine particles align well with the eruptive column heights, as reported from the INGV-OE calibrated cameras (Fig. 1). Additionally, the inversion algorithm was utilized with the FLEXPART SRR only for these two release times. In contrast, the SRR using “w/o” Aeolus assimilated wind fields shows that the volcanic particles arriving above the PANGEA station at heights of 8–10km (receptor – yaxis) are few and originate from release heights of around 8–11km above Etna (source – xaxis) and only when the particle release time was 04:00–06:00UTC (Fig. A2b, d, and f). This release time is not accurate, as the eruption actually began at 06:00 UTC according to the VONA messages from INGV-EO. https://doi.org/10.5194/acp-25-7343-2025 Atmos. Chem. Phys., 25, 7343–7368, 2025 7360 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations Figure A1. Workflow of the methodology. Figure A2. Source–receptor sensitivities for the fine particles (3, 5, 9, and 21µm diameter) “w” Aeolus assimilated winds (a,c, and e) and “w/o” Aeolus simulation (b,d, and f). The horizontal axis “x” depicts the particle release height (km) above Etna, and the vertical axis “y” is the altitude above PANGEA that the emissions observed on 12 March 2021 (18:30 to 21:30UTC). Atmos. Chem. Phys., 25, 7343–7368, 2025 https://doi.org/10.5194/acp-25-7343-2025 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations 7361 Table A1. Volcanic ash plume heights (m) during the eruption activity from 06:30 to 10:30UTC, as recorded by the ECV camera operated by INGV-EO (second column) and adjusted heights incorporating SEVIRI satellite observations where applicable (third column). Time (UTC) Height (m) from ECV Height (m) incorporating camera (INGV-EO) SEVIRI observations 06:30 4000 4000 06:45 5500 5500 07:00 5500 5500 07:15 6000 6000 07:30 6500 6500 07:45 7000 7000 08:00 7500 7500 08:15 <9000 11500 08:30 <9000 11500 08:45 <9000 11500 09:00 <9000 10000 09:15 <9000 9500 09:30 <9000 9500 09:45 9000 9000 10:00 6500 6500 10:15 5000 5000 10:30 4500 4500 Figure A3. Wind speed (ms−1) in WRF 18h forecasts. Horizontal winds (a) “w” Aeolus assimilation, (b) “w/o” Aeolus assimilation, and (c) wind speed differences (“w” – “w/o” Aeolus assimilation) at 100hPa (∼16km). https://doi.org/10.5194/acp-25-7343-2025 Atmos. Chem. Phys., 25, 7343–7368, 2025 7362 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations Figure A4. Wind speed (ms−1) in WRF 18h forecasts. Horizontal winds (a) “w” Aeolus assimilation, (b) “w/o” Aeolus assimilation, and (c) wind speed differences (“w” – “w/o” Aeolus assimilation) at 200hPa (∼12km). Figure A5. Wind speed (ms−1) in WRF 18h forecasts. Horizontal winds (a) “w” Aeolus assimilation, (b) “w/o” Aeolus assimilation, and (c) wind speed differences (“w” – “w/o” Aeolus assimilation) at 500hPa (∼5.5km). Atmos. Chem. Phys., 25, 7343–7368, 2025 https://doi.org/10.5194/acp-25-7343-2025 A. Kampouri et al.: Volcanic emission estimates from the inversion of ACTRIS lidar observations 7363 Code availability. The inversion algorithm was written with Python programming language version 3.12 (https://www.python. org/, last access: 27 June 2025) and can be obtained from the co-author Anna Kampouri ([email protected]) upon request. The WRF model code is publicly available, has a digital object identifier (https://doi.org/10.5065/D6MK6B4K, UCAR, 2025; Skamarock et al., 2019), and can be obtained via GitHub (https://github.com/ wrf-model/WRF, last access: 27 June 2025). The FLEXPARTWRF model code is publicly available and can be obtained from https://git.nilu.no/flexpart/flexpart-wrf (Dingwell, 2025; Brioude et al., 2013) input. The code used for data processing was written with Python programming language version 3.12 (https://www.python. org/, last access: 2 July 2025) and can be obtained via GitHub at https://github.com/NOA-ReACT/Aeolus_Volcano_2023 (last access: 2 July 2025; https://doi.org/10.5281/zenodo.15805552, Kampouri, 2025). The SCC algorithm used here is developed within the EARLINET/ACTRIS community, and its source code is not publicly available. Instead, it is used internally to process raw lidar signals into quality-assured Level 2 optical property profiles, which are made publicly available via the EARLINET data portal (https://data.earlinet.org/, last access: 10 July 2025). The retrievals and the aerosol lidar optical properties are available from the coauthor Anna Gialitaki ([email protected]) upon request. Data availability. The Aeolus L2A wind data can be downloaded from https://apps.ecmwf.int/mars-catalogue/?class=rd& expver=hkv (ECMWF, 2021b, a). The lidar data from the PollyXT system at the PANGEA-NOA station are available in ascii format through Zenodo under the following DOI: https://doi.org/10.5281/zenodo.15805552 (Kampouri, 2025). These data were derived using the Single Calculus Chain (SCC; https://scc.imaa.cnr.it) algorithm (a signal-analysis tool for raw lidar data processing developed within EARLINET (https://www.earlinet.org/, last access: 3 July 2025) and ACTRIS (https://www.actris.eu/, last access: 3 July 2025)). SCC products (L2 optical property profiles) are publicly available through the EARLINET data portal (https://github.com/actris-ares/ actris-earlinet-2024_annual_collection/tree/main, ACTRIS EARLINET, 2024). The WRF and FLEXPART-WRF models’ simulation results are also available from the co-author Anna Kampouri ([email protected]) upon request. Author contributions. AK conceptualized the paper along with VA, PZ, and StS. All authors wrote parts of the paper corresponding to their work and respective results. AK performed the FLEXPART and WRF runs and the inversion algorithm with the support of StS, PZ, and TG. AG and MT provided the PollyXT lidar retrievals. SiS provided the INGV-OE camera material and synergistic datasets for the analysis. MR provided the ECMWF-IFS datasets (“w” and “w/o” Aeolus assimilation). All authors provided corrections and suggestions to eventually shape the research, analysis, and final paper. AK supervised and directed the whole project. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Acknowledgements. Anna Kampouri and the authors affiliated to the National Observatory of Athens acknowledge the support from the following research projects: the PANORAMA project, funded by the European Union (grant agreement 101182795); the PANGEA4CalVal project, funded by the European Union (grant agreement 101079201); the Hellenic Foundation for Research and Innovation (project abbreviation: StratoFIRE, project number: 3995); the e-shape project, under the European Union’s Horizon 2020 research and innovation program (grant agreement 820852); the ACTRIS research infrastructure; and data and services obtained from the PANhellenic GEophysical Observatory of Antikythera (PANGEA) of NOA. Additionally, Anna Kampouri and Thanasis Georgiou acknowledge support from ESA in the framework of the “Enhancing Aeolus L2A for depolarizing targets and impact on aerosol research and NWP” project (4000139424/22/I-NS). Anna Kampouri acknowledges the support from Ioanni Binietoglou, Antoni Gkika, and Emmanouil Proestaki for their invaluable assistance and insightful discussions throughout the development of this work. Additionally, the authors thank the two anonymous reviewers for their helpful comments and suggestions for improving the quality of the paper. Financial support. This research has been supported by the Hellenic Foundation for Research and Innovation (Project Acronym: StratoFIRE, grant no. 3995). Review statement. This paper was edited by Stelios Kazadzis and reviewed by two anonymous referees. References ACTRIS: PollyXT lidar data at PANGEA station, National Observatory of Athens [data set], https://polly.tropos.de/calendar/ location/38, last access: 3 July 2025. 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