Volcanic Cloud Research Articles

Dynamics, Monitoring, and Forecasting of Tephra in the Atmosphere

AbstractExplosive volcanic eruptions inject hot mixtures of solid particles (tephra) and gasses into the atmosphere. Entraining ambient air, these mixtures can form plumes rising tens of kilometers until they spread laterally, forming umbrella clouds. While the largest clasts tend to settle in proximity to the volcano, the smallest fragments, commonly referred to as ash (≤2 mm in diameter), can be transported over long distances, forming volcanic clouds. Tephra plumes and clouds pose significant hazards to human society, affecting infrastructure, and human health through deposition on the ground or airborne suspension at low altitudes. Additionally, volcanic clouds are a threat to aviation, during both high‐risk actions such as take‐off and landing and at standard cruising altitudes. The ability to monitor and forecast tephra plumes and clouds is fundamental to mitigate the hazard associated with explosive eruptions. To that end, various monitoring techniques, ranging from ground‐based instruments to sensors on‐board satellites, and forecasting strategies, based on running numerical models to track the position of volcanic clouds, are efficiently employed. However, some limitations still exist, mainly due to the high unpredictability and variability of explosive eruptions, as well as the multiphase and complex nature of volcanic plumes. In the next decades, advances in monitoring and computational capabilities are expected to address these limitations and significantly improve the mitigation of the risk associated with tephra plumes and clouds.

Particle Concentrations and Sizes for the Onset of Settling‐Driven Gravitational Instabilities: Experimental Validation and Application to Volcanic Ash Clouds

AbstractSettling‐driven gravitational instabilities (SDGIs) can form at the base of buoyant particle‐laden suspensions, modulating particle sedimentation in various settings such as meteorological and volcanic clouds, fluvial plumes, magma chambers, submarine hydrothermal plumes, or industrial emissions. These instabilities result in the formation of rapidly descending currents called ‘fingers’ within which fine particles settle faster collectively than individually. This study investigates SDGI triggering conditions underneath volcanic ash clouds through analogue experiments considering sedimentation from aqueous particle suspensions. We confirm that the conditions for which SDGIs develop are controlled by two dimensionless numbers: Bf (ratio of the characteristic finger velocity to the individual particle settling velocity); and Bi (ratio of timescale for individual particle settling to that for collective settling controlled by inertial drag). SDGIs are triggered for values of Bf and Bi > 1 for which particles are fully coupled with the flow within fingers. Using these parameters, we produce a regime diagram for the 2010 eruption of Eyjafjallajökull (Iceland) that describes particle settling as a function of particle concentration and size. More studies are needed to produce a general regime diagram accounting for the evolution of SDGIs properties with eruption and atmospheric parameters. Nonetheless, our study confirms that fingers affect sedimentation from volcanic clouds with high ash volume fractions above 10−6 vol.%. Our validation of criteria predicting the onset of fingers due to SDGIs constitutes a step forward toward the incorporation of these collective settling processes in volcanic ash transport and dispersion models.

Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling

Abstract. ​​​​​​​Consideration of uncertainty in volcanic ash cloud forecasts is increasingly of interest, with an industry goal to provide probabilistic forecasts alongside deterministic forecasts. Simulations of volcanic clouds via dispersion modelling are subject to a number of uncertainties relating to the eruption itself (mass of ash emitted and when), parameterisations of physical processes, and the meteorological conditions. To fully explore these uncertainties through atmospheric dispersion model simulations alone may be expensive, and instead, an emulator can be used to increase understanding of uncertainties in the model inputs and outputs, going beyond combinations of source, physical, and meteorological inputs that were simulated by the dispersion model. We emulate the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model for simulations of the Raikoke 2019 eruption and use these emulators to compare simulated ash clouds to observations derived from satellites, constraining NAME source and internal parameters via history matching. We demonstrate that the effect of varying both meteorological scenarios and model parameters can be captured in this way with accurate emulation and using only a small number of runs per meteorological scenario. We show that accounting for meteorological uncertainty simultaneously with other uncertainties may lead to the identification of different sensitive model parameters and may lead to less constrained source and internal NAME parameters; however, through idealised experiments, we argue that this is a reasonable result and is properly accounting for all sources of uncertainty in the model inputs.

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Volcanic clouds pose significant threats to air traffic, human health, and economic activity, making early detection and monitoring crucial. Accurate determination of eruptive source parameters is crucial for forecasting and implementing preventive measures. This review article aims to identify the most common remote sensing methods for monitoring volcanic clouds. To achieve this, we conducted a systematic literature review of scientific articles indexed in the Web of Science database published between 2010 and 2022, using multiple query strings across all fields. The articles were reviewed based on research topics, remote sensing methods, practical applications, case studies, and outcomes using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our study found that satellite-based remote sensing approaches are the most cost-efficient and accessible, allowing for the monitoring of volcanic clouds at various spatial scales. Brightness temperature difference is the most commonly used method for detecting volcanic clouds at a specified temperature threshold. Approaches that apply machine learning techniques help overcome the limitations of traditional methods. Despite the constraints imposed by spatial and temporal resolution and optical limitations of sensors, multiplatform approaches can overcome these limitations and improve accuracy. This study explores various techniques for monitoring volcanic clouds, identifies research gaps, and lays the foundation for future research.

Volcanic clouds detection applying machine learning techniques to GNSS radio occultations

Volcanic clouds detection is a challenge especially when meteorological clouds are present in the same area. Several algorithms have been developed to detect and monitor volcanic clouds by using satellite instruments based on different remote sensing techniques. This work aims at classifying volcanic clouds based on atmospheric profiles retrieved by the GNSS (Global Navigation Satellite Systems) radio occultation technique. We collocated the radio occultations with the volcanic cloud detection from AIRS (Atmospheric InfraRed Sounder) and IASI (Infrared Atmospheric Sounding Interferometer) for 11 big eruptions happening in the period 2008–2015 resulting in about 15000 profiles. We created an archive with the collocations and a corresponding number of profiles in “non-volcanic” environment in the same area and on the same period of the year. A support vector machine algorithm was applied to the archive in order to classify the clouds and to distinguish the volcanic clouds from the other types. The model performances are promising: the GNSS radio occultations are able to distinguish the volcanic clouds with an accuracy higher than 80% when the eruption occurs at high latitudes. The performances of the model are affected by the number of collocations used for the training. Nowadays, the number of radio occultations is higher than in the period considered in this research, making this work a pioneering study for a future operational product.

Enhancing detection of volcanic ash clouds from space with convolutional neural networks

Volcanic ash cloud detection is a crucial component of volcano monitoring and a valuable tool for investigating ash cloud dispersion, which is paramount for enhancing the safety of human settlements and air traffic. The latest generation of high-resolution satellite sensors (e.g., EUMETSAT MSG Spinning Enhanced Visible and InfraRed Imager, SEVIRI) provides radiometric estimates for monitoring volcanic clouds on a global scale efficiently and timely. However, these radiometric intensities are not always discriminative enough to detect volcanic ash clouds due to the spectral limitations of these instruments and the complex nature of some volcanic clouds, such as low concentration resulting in an averaged detected radiometric estimate comparable to the background. Here, we evaluate the ability of a Convolutional Neural Network (CNN) to detect and track the dispersion of volcanic ash clouds into the atmosphere, exploiting a variety of spatial and spectral intensity information mainly coming from SEVIRI Ash RGB images. We train a deep CNN model through transfer learning, and demonstrate that the trained models overcome the limitations of algorithms based solely on pixel intensity, whether traditional or machine learning, resulting in increased performance compared to other methods. We illustrate the operation of this model using the paroxysmal explosive events that occurred at Mt. Etna between 2020 and 2022.

A One‐Dimensional Volcanic Plume Model for Predicting Ash Aggregation

AbstractDuring explosive volcanic eruptions, volcanic ash is ejected into the atmosphere, impacting aircraft safety and downwind communities. These volcanic clouds tend to be dominated by fine ash (<63 μm in diameter), permitting transport over hundreds to thousands of kilometers. However, field observations show that much of this fine ash aggregates into clusters or pellets with faster settling velocities than individual particles. Models of ash transport and deposition require an understanding of aggregation processes, which depend on factors like moisture content and local particle collision rates. In this study, we develop a Plume Model for Aggregate Prediction, a one‐dimensional (1D) volcanic plume model that predicts the plume rise height, concentration of water phases, and size distribution of resulting ash aggregates from a set of eruption source parameters. The plume model uses a control volume approach to solve mass, momentum, and energy equations along the direction of the plume axis. The aggregation equation is solved using a fixed pivot technique and incorporates a sticking efficiency model developed from analog laboratory experiments of particle aggregation within a novel turbulence tower. When applied to the 2009 eruption of Redoubt Volcano, Alaska, the 1D model predicts that the majority of the plume is over‐saturated with water, leading to a high rate of aggregation. Although the mean grain size of the computed Redoubt aggregates is larger than the measured deposits, with a peak at 1 mm rather than 500 μm, the present results provide a quantitative estimate for the magnitude of aggregation in an eruption.

Interactive Stratospheric Aerosol Microphysics‐Chemistry Simulations of the 1991 Pinatubo Volcanic Aerosols With Newly Coupled Sectional Aerosol and Stratosphere‐Troposphere Chemistry Modules in the NASA GEOS Chemistry‐Climate Model (CCM)

AbstractWe have coupled the GEOS‐Chem tropospheric‐stratospheric chemistry mechanism and the Community Aerosol and Radiation Model for Atmospheres (CARMA), a sectional aerosol microphysics module, within the NASA Goddard Earth Observing System Chemistry‐Climate Model (GEOS CCM) in order to simulate the interactions between stratospheric chemistry and aerosol microphysics. We use observations of the 1991 Mount Pinatubo volcanic cloud to evaluate this new version of the GEOS CCM. The GEOS‐Chem chemistry module is used to simulate the oxidation of sulfur dioxide (SO2) more realistically than assuming hydroxyl radical (OH) fields are constant, as OH concentrations in the plume decrease dramatically in the weeks following the eruption. CARMA simulates sulfate aerosols with dynamic microphysical and optical properties. The CARMA‐calculated aerosol surface area is coupled to the chemistry module from GEOS‐Chem for the calculation of heterogeneous chemistry. We use a set of observational and theoretical constraints for Pinatubo to evaluate the performance of this new version of the GEOS CCM. These simulations are specifically compared with satellite and in‐situ observations and provide insights into the connections between the gas‐phase chemistry and the aerosol microphysics of the early plume and how they impact the climatic and chemical changes following a large volcanic eruption. A second, smaller eruption is also included in these simulations, the 15 August 1991, eruption of Cerro Hudson in Chile, which we find essential in explaining the aerosol optical depth in the Southern Hemisphere in 1991.

Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic Ash

AbstractThe way volcanic clouds evolve is very sensitive to the initial spatial 3D distributions of volcanic materials, which are often unknown. In this study, we conducted inverse modeling of the Mt. Pinatubo cloud using total ozone mapping spectrometer 2D mapping of Aerosol Index and SO2 loading during the first three post‐eruption days to estimate the time‐dependent emissions profiles and initial 3D spatial distributions of volcanic ash and SO2. We account for aerosol radiative feedback and dynamic lofting of volcanic ash in the inversion calculations for the first time. This resulted in a lower ash injection height (by 1.5 km for ash) than without ash radiative feedback. The Pinatubo eruption ejected ≈77% of fine ash at 12–23 km, ≈65% of SO2 at 18–25 km. In contrast with previous studies, which suggested that all volcanic materials were emitted above the tropopause, a significant fraction of SO2 (5.1 of 15.5 Mt) and fine ash (37.2 of 66.5 Mt) were ejected in the troposphere, where SO2 quickly oxidized into sulfate aerosol that is short‐lived in the troposphere. This explains the early presence of sulfate aerosols in the plume and why the models can reproduce the observed volcanic aerosols' optical depth (AOD), assuming lower‐than‐observed SO2 emission in the stratosphere. Despite the quicker than in observations build‐up of sulfate AOD, in a month after the eruption, the evolution of the Pinatubo AOD simulated using the obtained ash and SO2 initial distributions converges with the available stratospheric aerosol and gas experiment observations.

Volcanic Clouds Characterization of the 2020–2022 Sequence of Mt. Etna Lava Fountains Using MSG-SEVIRI and Products’ Cross-Comparison

From December 2020 to February 2022, 66 lava fountains (LF) occurred at Etna volcano (Italy). Despite their short duration (an average of about two hours), they produced a strong impact on human life, environment, and air traffic. In this work, the measurements collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board Meteosat Second Generation (MSG) geostationary satellite, are processed every 15 min to characterize the volcanic clouds produced during the activities. In particular, a quantitative estimation of volcanic cloud top height (VCTH) and ash/ice/SO2 masses’ time series are obtained. VCTHs are computed by integrating three different retrieval approaches based on coldest pixel detection, plume tracking, and HYSPLIT models, while particles and gas retrievals are realized simultaneously by exploiting the Volcanic Plume Retrieval (VPR) real-time procedure. The discrimination between ashy and icy pixels is carried out by applying the Brightness Temperature Difference (BTD) method with thresholds obtained by making specific Radiative Transfer Model simulations. Results indicate a VCTH variation during the entire period between 4 and 13 km, while the SO2, ash, and ice total masses reach maximum values of about 50, 100, and 300 Gg, respectively. The cumulative ash, ice, and SO2 emitted from all the 2020–2022 LFs in the atmosphere are about 750, 2300, and 670 Gg, respectively. All the retrievals indicate that the overall activity can be grouped into 3 main periods in which it passes from high (December 2020 to March 2021), low (March to June 2021), and medium/high (June 2021 to February 2022). The different products have been validated by using TROPOspheric Monitoring Instrument (TROPOMI) polar satellite sensor, Volcano Observatory Notices for Aviation (VONA) bulletins, and by processing the SEVIRI data considering a different and more accurate retrieval approach. The products’ cross-comparison shows a generally good agreement, except for the SO2 total mass in case of high ash/ice content in the volcanic cloud.

Volcanic Cloud Detection and Retrieval Using Satellite Multisensor Observations

Satellite microwave (MW) and millimetre-wave (MMW) passive sensors can be used to detect volcanic clouds because of their sensitivity to larger volcanic particles (i.e., size bigger than 20 µm). In this work, we combine the MW-MMW observations with thermal-infrared (TIR) radiometric data from the Low Earth Orbit (LEO) spectroradiometer to have a complete characterisation of volcanic plumes. We describe new physical-statistical methods, which combine machine learning techniques, aimed at detecting and retrieving volcanic clouds of two highly explosive eruptions: the 2014 Kelud and 2015 Calbuco test cases. For the detection procedure, we compare the well-known split-window methods with a machine learning algorithm named random forest (RF). Our work highlights how the machine learning method is suitable to detect volcanic clouds using different spectral signatures without fixing a threshold. Moreover, the RF model allows images to be automatically processed with promising results (90% of the area correctly identified). For the retrieval procedure of the mass of volcanic particles, we consider two methods, one based on the maximum likelihood estimation (MLE) and one using the neural network (NN) architecture. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands. Summing the MW-MMW and TIR estimates, we obtain the following masses: 1.11 ± 0.40 × 1011 kg (MLE method) and 1.32 ± 0.47 × 1011 kg (NN method) for Kelud; 4.48 ± 1.61 × 1010 kg (MLE method) and 4.32 ± 1.56 × 1010 kg (NN method) for Calbuco. This work shows how machine learning techniques can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near-source volcanic clouds.

Observed Atmospheric Features for the 2022 Hunga Tonga Volcanic Eruption from Joint Polar Satellite System Science Data Products

The Joint Polar Satellite System (JPSS) mission has provided over ten years of high-quality data products for environment forecasting and monitoring through the current Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. Particularly, the sensor data record (SDR) and the derived environmental data record (EDR) products from the Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), and the Ozone Mapping and Profiler Suite (OMPS) offer an unprecedented opportunity to observe severe weather and environmental events over the Earth. This paper presents the observations about atmospheric features of the Hunga Tonga Volcanic eruption of January 2022, e.g., the gravity wave, volcanic cloud, and aerosol (sulfate) plume phenomena, by using the ATMS, CrIS, OMPS, and VIIRS SDR and EDR products. Powerful gravity waves ringing through the atmosphere after the eruption of the Hunga Tonga volcano are discovered at two CrIS upper sounding channels (670 cm−1 and 2320 cm−1) in the deviations of the observed brightness temperature (O) from the simulated baseline brightness temperature (B) using the Community Radiative Transfer Model (CRTM), i.e., O—B. A similar pattern is also observed in the ATMS global maps at channel 15, whose peak weighting function is around 40 km, showing the atmospheric disturbance caused by the eruption that reached 40 km above the surface. The Tonga volcanic cloud (plume) was also captured by the OMPS SO2 EDR product. The gravity wave features were also captured in the native resolution image of the S-NPP VIIRS I-5 band nighttime observations. In addition, the VIIRS Aerosol Optical Depth (AOD) captured and tracked the volcanic aerosol (sulfate) plume successfully. These discoveries demonstrate the scientific potential of the JPSS SDR and EDR products in monitoring and tracking the eruption of the Hunga Tonga volcano and its severe environmental impacts. This paper presents the atmospheric features of the Hunga Tonga volcano eruption that is uniquely captured by all four advanced sensors onboard JPSS satellites, with different spectral coverages and spatial resolutions.

The effect of ash, water vapor, and heterogeneous chemistry on the evolution of a Pinatubo-size volcanic cloud

Abstract. We employ the ECHAM5/MESSy2 atmospheric chemistry general circulation model (EMAC) that incorporates calculations of gas-phase and heterogeneous chemistry coupled with the ozone cycle and aerosol formation, transport, and microphysics to calculate the 1991 Pinatubo volcanic cloud. We considered simultaneous injections of SO2, volcanic ash, and water vapor. We conducted multiple ensemble simulations with different injection configurations to test the evolution of SO2, SO42-, ash masses, stratospheric aerosol optical depth, surface area density (SAD), and the stratospheric temperature response against available observations. We found that the volcanic cloud evolution is sensitive to the altitude where volcanic debris is initially injected and the initial concentrations of the eruption products that affect radiative heating and lofting of the volcanic cloud. The numerical experiments with the injection of 12 Mt SO2, 75 Mt of volcanic ash, and 150 Mt of water vapor at 20 km show the best agreement with the observation aerosol optical depth and stratospheric temperature response. Volcanic water injected by eruptive jet and/or intruding through the tropopause accelerates SO2 oxidation. But the mass of volcanic water retained in the stratosphere is controlled by the stratospheric temperature at the injection level. For example, if volcanic materials are released in the cold point above the tropical tropopause, most of the injected water freezes and sediments as ice crystals. The water vapor directly injected into the volcanic cloud increases the SO42- mass and stratospheric aerosol optical depth by about 5 %. The coarse ash comprises 98 % of the ash injected mass. It sediments within a few days, but aged submicron ash could stay in the stratosphere for a few months providing SAD for heterogeneous chemistry. The presence of ash accelerates the SO2 oxidation by 10 %–20 % due to heterogeneous chemistry, radiative heating, lofting, and faster dispersion of volcanic debris. Ash aging affects its lifetime and optical properties, almost doubling the ash radiative heating. The 2.5-year simulations show that the stratospheric temperature anomalies forced by radiative heating of volcanic debris in our experiments with the 20 km injection height agree well with observations and reanalysis data. This indicates that the model captures the long-term evolution and climate effect of the Pinatubo volcanic cloud. The volcanic cloud's initial lofting, facilitated by ash particles' radiative heating, controls the oxidation rate of SO2. Ash accelerates the formation of the sulfate layer in the first 2 months after the eruption. We also found that the interactive calculations of OH and heterogeneous chemistry increase the volcanic cloud sensitivity to water vapor and ash injections. All those factors must be accounted for in modeling the impact of large-scale volcanic injections on climate and stratospheric chemistry.

Extreme Heights of 15 January 2022 Tonga Volcanic Plume and Its Initial Evolution Inferred from COSMIC-2 RO Measurements

The Hunga Tonga–Hunga Ha’apai underwater volcano (20.57° S, 175.38° W) violently erupted on 15 January 2022. The volcanic cloud’s top height and initial evolution are delineated by using the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-2 radio occultation (RO) measurements. The bending angle (BA) anomaly over the Tonga volcanic plume (within 200 km of the eruption center) at 5:17 UTC on 15 January showed a prominent peak at higher stratospheric heights. The top of the BA anomaly revealed that negative to positive change occurred at ~38 km, indicating the first height where the RO line-of-sight encountered the volcanic plume. The BA anomaly further revealed an increase of ~50% at ~36.1 km, and confirmed that the volcanic plume reached above ~36 km. Furthermore, the evolution of BA perturbations within 24 h after the initial explosion is also discussed herein. From collocated RO profiles with the volcanic plume, we can find a clear descent of the peak altitude of the BA perturbation from ~36.1 km to ~29 km within 24 h after the initial eruption. The results from this study will provide some insights into advancing our understanding of volcanic cloud dynamics and their implementation in volcanic plume modeling.

Real-time probabilistic assessment of volcanic hazard for tephra dispersal and fallout at Mt. Etna: the 2021 lava fountain episodes

Starting from February 2021, Mt. Etna (Italy) experienced a period of intense explosive activity with 17 lava fountain episodes between 16 February and 1 April 2021. During the eruptive cycle, the Istituto Nazionale di Geofisica e Vulcanologia-Osservatorio Etneo (INGV-OE) issued 62 alert notifications known as VONAs (Volcano Observatory Notice for Aviation) to inform the aeronautical authorities about the volcanic activity. We present an automated VONA-based workflow aimed at real-time assessment of the volcanic hazard due to tephra fallout at Mt. Etna. When a VONA reporting tephra emission is issued by INGV-OE, numerical simulations accounting for atmospheric and eruptive uncertainties are automatically initialized to produce probabilistic hazard maps of tephra fallout and atmospheric dispersal. We applied the workflow to three lava fountains that occurred during the 2021 eruptive cycle. To test the modelling results, we compared the simulated ground load with field data, and the extent and position of the simulated volcanic cloud with the observed or estimated volcanic cloud from the Toulouse Volcanic Ash Advisory Center. Overall, we found a good match between simulated and observed quantities (tephra loads and volcanic cloud position), especially when accurate information on eruptive conditions (column height and duration) are supplied by the VONAs. Finally, through a statistical analysis, we found that column height and wind field are fundamental in determining tephra ground accumulation. For this reason, these parameters should be constrained by observational data as accurately as possible when performing numerical simulations, especially in the line of developing operational workflows.

Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case

Abstract. Accurate automatic volcanic cloud detection by means of satellite data is a challenging task and is of great concern for both the scientific community and aviation stakeholders due to well-known issues generated by strong eruption events in relation to aviation safety and health impacts. In this context, machine learning techniques applied to satellite data acquired from recent spaceborne sensors have shown promising results in the last few years. This work focuses on the application of a neural-network-based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. A classification of meteorological clouds and of other surfaces comprising the scene is also carried out. The neural network has been trained with MODIS (Moderate Resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallajökull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the comparable latitudes of the eruptions permit an extension of the approach to SLSTR, thereby overcoming the lack in Sentinel-3 products collected in previous mid- to high-latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared to RGB visual inspection and BTD (brightness temperature difference) procedures. Moreover, the comparison between the ash cloud obtained by the neural network (NN) and a plume mask manually generated for the specific SLSTR images considered shows significant agreement, with an F-measure of around 0.7. Thus, the proposed approach allows for an automatic image classification during eruption events, and it is also considerably faster than time-consuming manual algorithms. Furthermore, the whole image classification indicates the overall reliability of the algorithm, particularly for recognition and discrimination between volcanic clouds and other objects.

Eruption chronology of the December 2021 to January 2022 Hunga Tonga-Hunga Ha’apai eruption sequence

The 15 January 2022 eruption of Hunga Tonga-Hunga Ha’apai, and the preceding eruptions on 19 December 2021 and 13 January 2022, were remarkable, partly because the eruptions generated extensive umbrella clouds, regions where the volcanic clouds spread laterally. Here we use satellite remote sensing to evaluate the umbrella cloud tops’ heights, longevities, water contents, and volumetric flow rates. We identified two umbrella clouds at distinct elevations on 15 January 2022. Specifically, after 05:30 UTC, the strong westward propagation of an upper umbrella cloud at 31 km ± 3 km enabled the visibility of the lower umbrella cloud at 17 km ± 2 km. The satellite-derived volumetric flow rate for 15 January 2022 was ~5.0 × 1011 m3 s−1, nearly two orders of magnitude higher than the volumetric flow rates estimated for the 19 December 2021 and 13 January 2022 eruptions. Finally, we found that the umbrellas on all three dates were ice-rich.

Characterization of Volcanic Cloud Components Using Machine Learning Techniques and SEVIRI Infrared Images.

Volcanic explosive eruptions inject several different types of particles and gasses into the atmosphere, giving rise to the formation and propagation of volcanic clouds. These can pose a serious threat to the health of people living near an active volcano and cause damage to air traffic. Many efforts have been devoted to monitor and characterize volcanic clouds. Satellite infrared (IR) sensors have been shown to be well suitable for volcanic cloud monitoring tasks. Here, a machine learning (ML) approach was developed in Google Earth Engine (GEE) to detect a volcanic cloud and to classify its main components using satellite infrared images. We implemented a supervised support vector machine (SVM) algorithm to segment a combination of thermal infrared (TIR) bands acquired by the geostationary MSG-SEVIRI (Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager). This ML algorithm was applied to some of the paroxysmal explosive events that occurred at Mt. Etna between 2020 and 2022. We found that the ML approach using a combination of TIR bands from the geostationary satellite is very efficient, achieving an accuracy of 0.86, being able to properly detect, track and map automatically volcanic ash clouds in near real-time.

Volcanic effects on climate: recent advances and future avenues

Volcanic eruptions have long been studied for their wide range of climatic effects. Although global-scale climatic impacts following the formation of stratospheric sulfate aerosol are well understood, many aspects of the evolution of the early volcanic aerosol cloud and regional impacts are uncertain. In the last twenty years, several advances have been made, mainly due to improved satellite measurements and observations enabling the effects of small-magnitude eruptions to be quantified, new proxy reconstructions used to investigate the impact of past eruptions, and state-of-the-art aerosol-climate modelling that has led to new insights on how volcanic eruptions affect the climate. Looking to the future, knowledge gaps include the role of co-emissions in volcanic plumes, the impact of eruptions on tropical hydroclimate and Northern Hemisphere winter climate, and the role of eruptions in long-term climate change. Future model development, dedicated model intercomparison projects, interdisciplinary collaborations, and the application of advanced statistical techniques will facilitate more complex and detailed studies. Ensuring that the next large-magnitude explosive eruption is well observed will be critical in providing invaluable observations that will bridge remaining gaps in our understanding.

Methodology to obtain highly resolved SO<sub>2</sub> vertical profiles for representation of volcanic emissions in climate models

Abstract. In this study we describe a methodology to create high-vertical-resolution SO2 profiles from volcanic emissions. We demonstrate the method's performance for the volcanic clouds following the eruption of Sarychev in June 2009. The resulting profiles are based on a combination of satellite SO2 and aerosol retrievals together with trajectory modelling. We use satellite-based measurements, namely lidar backscattering profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite instrument, to create vertical profiles for SO2 swaths from the Atmospheric Infrared Sounder (AIRS) aboard the Aqua satellite. Vertical profiles are created by transporting the air containing volcanic aerosol seen in CALIOP observations using the FLEXible PARTicle dispersion model (FLEXPART) while preserving the high vertical resolution using the potential temperatures from the MERRA-2 (Modern-Era Retrospective analysis for Research and Application) meteorological data for the original CALIOP swaths. For the Sarychev eruption, air tracers from 75 CALIOP swaths within 9 d after the eruption are transported forwards and backwards and then combined at a point in time when AIRS swaths cover the complete volcanic SO2 cloud. Our method creates vertical distributions for column density observations of SO2 for individual AIRS swaths, using height information from multiple CALIOP swaths. The resulting dataset gives insight into the height distribution in the different sub-clouds of SO2 within the stratosphere. We have compiled a gridded high-vertical-resolution SO2 inventory that can be used in Earth system models, with a vertical resolution of 1 K in potential temperature, 61 ± 56 m, or 1.8 ± 2.9 mbar.