Analysis of thermal infrared satellite measurements of umbrella clouds generated by volcanic eruptions suggests that asymptotic gravity current models of the temporal (t) radial (r) spreading (r ~tf, f < 1) of the umbrella-shaped intrusion do not adequately explain the observations. Umbrella clouds from 13 volcanic eruptions are studied using satellite data that have spatial resolutions of ~4–25 km2 and temporal resolutions of 1–60 minutes. The umbrella cloud morphology is evaluated using digital image processing tools in a Lagrangian frame of reference. At the onset of neutral buoyancy, the radial spreading is better explained by a stronger dependence on time of r ~ t, rather than t2/3, t3/4, or t2/9. This flow regime exists on the order of minutes and has not been observed previously in satellite data. This may be of significance as it provides a means to rapidly (within the first 2–3 observations) determine the volumetric eruption rate. A hyperbolic tangent model, r ~ tanh(t) is presented that matches the entire radial spreading time history and has a conserved torus-shaped volume in which the intrusion depth is proportional to sech(t). This model also predicts the observed radial velocities. The data and the model estimates of the volumetric flow rate for the 15 January 2022 Hunga eruption are found to be 3.6–5 × 1011 m3s−1, the largest ever measured.

In the last few decades, volcanic monitoring using remote sensing systems has become an essential tool to investigate the effects of volcanic activity on environment, climate, human health, and aviation, as well as to give insights into volcanic processes. Compared to satellite measurements, ground-based instruments offer continuous spatial and temporal coverage capable of providing high-resolution and high-sensitivity data.This work presents a new simplified prototype of a thermal infrared (TIR) system (named "VIRSO2"). The instrument comprises three cameras, one working in the visible spectrum and two in the TIR (8-14 mu m). In front of one of the two TIR cameras, an 8.7 mu m filter is placed. The system is designed for the detection of volcanic emission, geometry estimation, determining the columnar content of SO2 and ash, and SO2 flux retrievals. The retrieval procedures developed are detailed starting from the geometric characterisation with wind direction correction, the calibration by considering the effects of filter multireflections and temperature, and the SO2 mass by exploiting MODTRAN radiative transfer model (RTM) simulations. The SO2 flux is then computed by applying the traverse method, with the plume speed obtained from the wind speed at the crater altitude. As test cases, the measurements collected at Etna Volcano (Italy) on 1 April 2021 during a lava fountain episode and 30 August 2024 during a quiescent phase have been considered. The results show that the system can provide reliable information on plume detection, altitude, and SO2 flux.Its simplicity, its low cost, and the possibility of carrying out measurements at a safe distance from the vent during both the day and the night make this system ideal for real-time monitoring of volcanic emissions, thus helping to provide information on the state of activity of the volcano and therefore to mitigate the effect that these natural phenomena have on humans and the environment.

Ground-based infrared cameras can be used effectively and safely to provide quantitative information about small to moderate-sized volcanic eruptions. This study describes an infrared camera that has been used to measure emissions from the Mt. Etna and Stromboli (Sicily, Italy) volcanoes. The camera provides calibrated brightness temperature images in a broadband (8–14 µm) channel that is used to determine height, plume ascent rate and volcanic cloud/plume temperature and emissivity at temporal sampling rates of up to 1 Hz. The camera can be operated in the field using a portable battery and includes a microprocessor, data storage and WiFi. The processing and analyses of the data are described with examples from the field experiments. The updraft speeds of the small eruptions at Stromboli are found to decay with a timescale of ∼10 min and the volcanic plumes reach thermal equilibrium within ∼2 min. A strong eruption of Mt. Etna on 1 April 2021 was found to reach ∼9 km, with ascent speeds of 10–20 ms−1. The plume, mostly composed of the gases CO2, water vapour and SO2, became bent over by the prevailing winds at high levels, demonstrating the need for multiple cameras to accurately infer plume heights.

​​​​​​​The 21 June 2019 Raikoke eruption (48° N, 153° E) generated one of the largest amounts of sulfur emission to the stratosphere since the 1991 Mt. Pinatubo eruption. Satellite measurements indicate a consensus best estimate of 1.5 Tg for the sulfur dioxide (SO2) injected at an altitude of around 14–15 km. The peak Northern Hemisphere (NH) mean 525 nm stratospheric aerosol optical depth (SAOD) increased to 0.025, a factor of 3 higher than background levels. The Volcano Response (VolRes) initiative provided a platform for the community to share information about this eruption which significantly enhanced coordination efforts in the days after the eruption. A multi-platform satellite observation subgroup formed to prepare an initial report to present eruption parameters including SO2 emissions and their vertical distribution for the modeling community. It allowed us to make the first estimate of what would be the peak in SAOD 1 week after the eruption using a simple volcanic aerosol model. In this retrospective analysis, we show that revised volcanic SO2 injection profiles yield a higher peak injection of the SO2 mass. This highlights difficulties in accurately representing the vertical distribution for moderate SO2 explosive eruptions in the lowermost stratosphere due to limited vertical sensitivity of the current satellite sensors (±2 km accuracy) and low horizontal resolution of lidar observations. We also show that the SO2 lifetime initially assumed in the simple aerosol model was overestimated by 66 %, pointing to challenges for simple models to capture how the life cycle of volcanic gases and aerosols depends on the SO2 injection magnitude, latitude, and height. Using a revised injection profile, modeling results indicate a peak NH monthly mean SAOD at 525 nm of 0.024, in excellent agreement with observations, associated with a global monthly mean radiative forcing of −0.17 W m−2 resulting in an annual global mean surface temperature anomaly of −0.028 K. Given the relatively small magnitude of the forcing, it is unlikely that the surface response can be dissociated from surface temperature variability.

. A total solar eclipse occurred on April 20, 2023, with the umbral shadow touching the Australian continent over the Ningaloo coastal region, near the town of Exmouth, Western Australia. Eclipse totality lasted similar to 1min, reaching totality at similar to 03:29 UTC and happened under cloudless skies. Here, we show that the speed of the Moon's shadow over the land surface can be estimated from 10 min sampling in both the infrared and visible bands of the Himawari-9 geostationary satellite sensor. The cooling of the land surface due to the passage of the Moon's shadow over the land is investigated, and temperature drops of 7 K to 15 K are found with cooling rates of 2 +/- 1.5mKs-1. By tracking the time of maximum cooling, the speed of the Moon's shadow was estimated from thermal data to be 2788 +/- 21kmh-1 and from the time of minimum reflectance in the visible data to be 2598 +/- 181kmh-1, with a notable time dependence. The methodology and analyses are new and the results compare favorably with NASA's eclipse data computed using Besselian elements.

The Hunga volcano (21.545° S, 178.393° E; also known as Hunga Tonga-Hunga Ha′apai) erupted on 15 January 2022, producing copious amounts of aerosols that reached high into the stratosphere, exceeding 30 km and settling into layers a few kilometres deep between 22 and 28 km. The Advanced Himawari Imager (AHI) on board the geostationary Himawari-8/9 platform at 140.7° E was able to monitor the eruption at 10 min intervals and 0.25 to 4 km2 spatial resolution within 16 spectral channels ranging from visible to infrared wavelengths and over a latitude–longitude field of view of ∼ ±75°. Here a new use of these data is proposed where the limb region of the field of view is exploited to detect aerosol layers extending vertically into the atmosphere. The analyses provide vertical profiles of scattered visible light and are compared to CALIOP space lidar measurements. Hunga aerosols are detected using the ratio of near-infrared reflectances at 1.61 and 2.25 µmm in the western limb from 22 January and in the eastern limb from 31 January 2022 up until the present time (December 2023). Between January and April 2022, the average zonal velocity is estimated to be ∼ −25 m s−1 (westwards) and the meridional velocity to be ∼ 0.2 m s−1 (northwards). The latitudinal spread is characterized by a gradual northerly movement of the main layer situated between 22 and 28 km in the first 60 d, and stagnation or slight southerly spread thereafter. There is a shallow maximum of the lower stratospheric aerosol between 10 and 20° S, and the aerosol loading during 2023 is elevated compared with the 3 months prior to the eruption. The Southern Hemisphere (0–30° S) tropical lower stratospheric aerosol e-folding time is estimated to be ∼ 12 months, but the decay is not uniform and has high variability. The current methodology does not provide quantitative estimates of the amount or type of aerosol, but based on the spectral properties of water and ice clouds the analysis suggests there is a strong liquid water content in the aerosol layers.

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.

The January 2022 Hunga Tonga–Hunga Ha’apai eruption was one of the most explosive volcanic events of the modern era 1 , 2 , producing a vertical plume that peaked more than 50 km above the Earth 3 . The initial explosion and subsequent plume triggered atmospheric waves that propagated around the world multiple times 4 . A global-scale wave response of this magnitude from a single source has not previously been observed. Here we show the details of this response, using a comprehensive set of satellite and ground-based observations to quantify it from surface to ionosphere. A broad spectrum of waves was triggered by the initial explosion, including Lamb waves 5 , 6 propagating at phase speeds of 318.2 ± 6 m s −1 at surface level and between 308 ± 5 to 319 ± 4 m s −1 in the stratosphere, and gravity waves 7 propagating at 238 ± 3 to 269 ± 3 m s −1 in the stratosphere. Gravity waves at sub-ionospheric heights have not previously been observed propagating at this speed or over the whole Earth from a single source 8 , 9 . Latent heat release from the plume remained the most significant individual gravity wave source worldwide for more than 12 h, producing circular wavefronts visible across the Pacific basin in satellite observations. A single source dominating such a large region is also unique in the observational record. The Hunga Tonga eruption represents a key natural experiment in how the atmosphere responds to a sudden point-source-driven state change, which will be of use for improving weather and climate models.

The January 2022 Hunga Tonga–Hunga Ha’apai eruption was one of the most explosive volcanic events of the modern era, producing a vertical plume which peaked > 50km above the Earth. The initial explosion and subsequent plume triggered atmospheric waves which propagated around the world multiple times. A global-scale wave response of this magnitude from a single source has not previously been observed. Here we show the details of this response, using a comprehensive set of satellite and ground-based observations to quantify it from surface to ionosphere. A broad spectrum of waves was triggered by the initial explosion, including Lamb waves5,6 propagating at phase speeds of 318.2+/-6 ms-1 at surface level and between 308+/-5 to 319+/-4 ms-1 in the stratosphere, and gravity waves propagating at 238+/-3 to 269+/-3 ms-1 in the stratosphere. Gravity waves at sub-ionospheric heights have not previously been observed propagating at this speed or over the whole Earth from a single source. Latent heat release from the plume remained the most significant individual gravity wave source worldwide for > 12 hours, producing circular wavefronts visible across the Pacific basin in satellite observations. A single source dominating such a large region is also unique in the observational record. The Hunga Tonga eruption represents a key natural experiment in how the atmosphere responds to a sudden point-source-driven state change, which will be of use for improving weather and climate models.

In June 2019, the Raikoke volcano, Kuril Islands, emitted 0.4–1.8×109 kg of very fine ash and 1–2×109 kg of SO2 up to 14 km into the atmosphere. The eruption was characterized by several eruption phases of different duration and height summing up to a total eruption length of about 5.5 h. Resolving such complex eruption dynamics is required for precise volcanic plume dispersion forecasts. To address this issue, we coupled the atmospheric model system ICON-ART (ICOsahedral Nonhydrostatic with the Aerosols and Reactive Trace gases module) with the 1D plume model FPlume to calculate the eruption source parameters (ESPs) online. The main inputs are the plume heights for the different eruption phases that are geometrically derived from satellite data. An empirical relationship is used to derive the amount of very fine ash (particles <32 µm), which is relevant for long-range transport in the atmosphere. On the first day after the onset of the eruption, the modeled ash loading agrees very well with the ash loading estimated from AHI (Advanced Himawari Imager) observations due to the resolution of the eruption phases and the online treatment of the ESPs. In later hours, aerosol dynamical processes (nucleation, condensation, and coagulation) explain the loss of ash in the atmosphere in agreement with the observations. However, a direct comparison is partly hampered by water and ice clouds overlapping the ash cloud in the observations. We compared 6-hourly means of model and AHI data with respect to the structure, amplitude, and location (SAL method) to further validate the simulated dispersion of SO2 and ash. In the beginning, the structure and amplitude values for SO2 differed largely because the dense ash cloud leads to an underestimation of the SO2 amount in the satellite data. On the second and third day, the SAL values are close to zero for all parameters (except for the structure value of ash), indicating a very good agreement of the model and observations. Furthermore, we found a separation of the ash and SO2 plume after 1 d due to particle sedimentation, chemistry, and aerosol–radiation interaction. The results confirm that coupling the atmospheric model system and plume model enables detailed treatment of the plume dynamics (phases and ESPs) and leads to significant improvement of the ash and SO2 dispersion forecast. This approach can benefit the operational forecast of ash and SO2 especially in the case of complex and noncontinuous volcanic eruptions like that of Raikoke in 2019.

The Raikoke volcano emitted about 0.4-1.8 x 10⁹ kg of ash and 1-2 x 10⁹ kg of SO2 up to 15 km into the atmosphere. However, the eruption was characterized by several puffs of different time periods and eruption heights. Here, we use the ICON-ART model in a model setup in which we resolve the phases of the Raikoke eruption. We calculated the eruption source parameters (ESPs) online by coupling ICON-ART to the 1-D plume model FPlume. The input heights for the different eruption phases needed for FPlume are geometrically derived from GEOS-17 satellite data. An empirical relationship is used to derive the amount of very fine ash (particles <32µm) which is relevant for long range transport in the atmosphere. In the first hours during and after the eruption, the modeled ash loading agrees very well with the observed ash loading from Himawari-8 due to the resolution of the eruption phase and the online calculation of the ESPs. In later hours, aerosol dynamical processes (nucleation, condensation, coagulation) explain the loss of ash in the atmosphere in agreement with the observations. However, a direct comparison is partly hampered by water and ice clouds overlapping the ash cloud in the observations. In case of SO2, we compared 6-hourly means of model and Himawari data with respect to the structure, amplitude, and location (SAL-method). In the beginning, the structure and amplitude values differed largely because the dense ash cloud directly after the eruption leads to an underestimation of the SO2 amount in the satellite data. On the second and third day, the SAL values are close to zero for all parameters indicating a good agreement of model and observations. We argue that representing the plume phases and ESPs in ICON-ART by FPlume enhances ash and SO2 predictability in the first days after the eruption, especially in case of non-continuous volcanic eruptions like the Raikoke eruption 2019.

A correct and reliable forecast of volcanic plume dispersion is vital for aviation safety. This can only be achieved by representing all responsible physical and chemical processes (sources, sinks, and interactions) in the forecast models. The representation of the sources has been enhanced over the last decade, while the sinks and interactions have received less attention. In particular, aerosol dynamic processes and aerosol–radiation interaction are neglected so far. Here we address this gap by further developing the ICON-ART (ICOsahedral Nonhydrostatic – Aerosols and Reactive Trace gases) global modeling system to account for these processes. We use this extended model for the simulation of volcanic aerosol dispersion after the Raikoke eruption in June 2019. Additionally, we validate the simulation results with measurements from AHI (Advanced Himawari Imager), CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), and OMPS-LP (Ozone Mapping and Profiling Suite-Limb Profiler). Our results show that around 50 % of very fine volcanic ash mass (particles with diameter d<30 µm) is removed due to particle growth and aging. Furthermore, the maximum volcanic cloud top height rises more than 6 km over the course of 4 d after the eruption due to aerosol–radiation interaction. This is the first direct evidence that shows how cumulative effects of aerosol dynamics and aerosol–radiation interaction lead to a more precise forecast of very fine ash lifetime in volcanic clouds.

Volcanic clouds' archive for volcanic eruptions with VEI ≥ 4 according to the Global Volcanism Program of Smithsonian in the period 2006-2018.Archive of SO2 retrievals from AIRS, IASI (A&B), GOME-2 (A&B) sensors, CALIOP trajectories and RO profiles collocated with the volcanic cloud.

Explosive volcanic eruptions can generate ash and SO2 clouds rising to the stratosphere and dispersing on a global scale. Such volcanic features are at the origin of many hazards including aircraft engine damages, ash fallouts, acid rains, short-term climate changes and health threats. It is thus crucial to monitor volcanic clouds altitude and dispersion over time in order to prevent these hazards. In the past decades, satellite monitoring techniques have proven to be efficient at detecting volcanic aerosols in the atmosphere. In particular the detection of SO2 (e.g. IASI, AIRS, GOME-2) spatial and temporal dispersion and altitude (e.g. CALIOP). However, satellite data are scattered amongst the different institutes and agencies acquiring and processing them, and their retrieval is time-consuming. In this study, we are building a whole new database gathering SO2 volcanic cloud altitude and dispersion data of 12 VEI 4 volcanic eruptions from 2008 to 2019. The spatial and temporal dispersion is retrieved from AIRS, IASI and GOME-2 sensors, as well as from collocated backscatter data of CALIOP sensor. Cloud altitude estimations are retrieved based on IASI, CALIOP and Global Navigation Satellite System (GNSS) radio occultation (RO) data when available. Besides, GNSS RO atmospheric profiles collocated with the other sensors at 12h temporal window and 0.2° spatial window, will be included. For the first time a dataset gathering several of the primary sensors used to monitor volcanic clouds and new ones will be freely available. Such new tool provides direct access to volcanic clouds data, and enables to perform original analysis and comparisons between different techniques. Applications for this dataset will impact many fields of volcanology and atmospheric physics, including but not restricted to volcanic clouds dispersal numerical modelling and volcanic aerosol impact on the atmosphere and climate. In fact, the collocation with GNSS RO will allow the study of the atmospheric structure with high vertical resolution.

We present a multi-sensor archive collecting spatial and temporal information about volcanic SO2 clouds generated by the 11 largest eruptions of this century. The detection and monitoring of volcanic clouds are an important topic for aviation management, climate issues and weather forecasts. Several studies focusing on single eruptive events exist, but no archive available at the moment combines quantitative data from as many instruments. We archived and collocated the SO2 vertical column density estimations from three different satellite instruments (AIRS, IASI and GOME-2), atmospheric parameters as vertical profiles from the Global Navigation Satellite Systems (GNSS) Radio Occultations (RO), and the cloud-top height and aerosol type from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Additionally, we provide information about the cloud-top height from three different algorithms and the atmospheric anomaly due to the presence of the cloud. The dataset is gathering 206 d of SO2 data, collocated with 44 180 backscatter profiles and 64 764 radio occultation profiles. The modular structure of the archive allows an easy collocation of the datasets according to the users' needs, and the cross-comparison of the datasets shows different consistency of the parameters estimated with different sensors and algorithms, according to the sensitivity and resolution of the instruments. The data described here are published with a DOI at https://doi.org/10.5880/fidgeo.2020.016 (Tournigand et al., 2020a).

Current Earth Observation (EO) satellites provide excellent spatial, temporal and spectral coverage for passive measurements of atmospheric volcanic emissions. Of particular value for ash detection and quantification are the geostationary satellites that now carry multispectral imagers. These instruments have multiple spectral channels spanning the visible to infrared (IR) wavelengths and provide 1 × 1 km2 to 4 × 4 km2 resolution data every 5–15 min, continuously. For ash detection, two channels situated near 11 and 12 μ m are needed; for ash quantification a third or fourth channel also in the infrared is useful for constraining the height of the ash cloud. This work describes passive EO infrared measurements and techniques to determine volcanic cloud properties and includes examples using current methods with an emphasis on the main difficulties and ways to overcome them. A challenging aspect of using satellite data is to design algorithms that make use of the spectral, temporal (especially for geostationary sensors) and spatial information. The hyperspectral sensor AIRS is used to identify specific molecules from their spectral signatures (e.g., for SO2) and retrievals are demonstrated as global, regional and hemispheric maps of AIRS column SO2. This kind of information is not available on all sensors, but by combining temporal, spatial and broadband multi-spectral information from polar and geo sensors (e.g., MODIS and SEVIRI) useful insights can be made. For example, repeat coverage of a particular area using geostationary data can reveal temporal behaviour of broadband channels indicative of eruptive activity. In many instances, identifying the nature of a pixel (clear, cloud, ash etc.) is the major challenge. Sophisticated cloud detection schemes have been developed that utilise statistical measures, physical models and temporal variation to classify pixels. The state of the art on cloud detection is good, but improvements are always needed. An IR-based multispectral cloud identification scheme is described and some examples shown. The scheme is physically based but has deficiencies that can be improved during the daytime by including information from the visible channels. Physical retrieval schemes applied to ash detected pixels suffer from a lack of knowledge of some basic microphysical and optical parameters needed to run the retrieval models. In particular, there is a lack of accurate spectral refractive index information for ash particles. The size distribution of fine ash (1–63 μ m, diameter) is poorly constrained and more measurements are needed, particularly for ash that is airborne. Height measurements are also lacking and a satellite-based stereoscopic height retrieval is used to illustrate the value of this information for aviation. The importance of water in volcanic clouds is discussed here and the separation of ice-rich and ash-rich portions of volcanic clouds is analysed for the first time. More work is required in trying to identify ice-coated ash particles, and it is suggested that a class of ice-rich volcanic cloud be recognized and termed a ‘volcanic ice’ cloud. Such clouds are frequently observed in tropical eruptions of great vertical extent (e.g., 8 km or higher) and are often not identified correctly by traditional IR methods (e.g., reverse absorption). Finally, the global, hemispheric and regional sampling of EO satellites is demonstrated for a few eruptions where the ash and SO 2 dispersed over large distances (1000s km).

The 2014–2015 Bárðarbunga fissure eruption at Holuhraun in central Iceland was distinguished by the high emission of gases, in total 9.6 Mt SO2, with almost no tephra. This work collates all ground-based measurements of this extraordinary eruption cloud made under particularly challenging conditions: remote location, optically dense cloud with high SO2 column amounts, low UV intensity, frequent clouds and precipitation, an extensive and hot lava field, developing ramparts, and high-latitude winter conditions. Semi-continuous measurements of SO2 flux with three scanning DOAS instruments were augmented by car traverses along the ring-road and along the lava. The ratios of other gases/SO2 were measured by OP-FTIR, MultiGAS, and filter packs. Ratios of SO2/HCl = 30–110 and SO2/HF = 30–130 show a halogen-poor eruption cloud. Scientists on-site reported extremely minor tephra production during the eruption. OPC and filter packs showed low particle concentrations similar to non-eruption cloud conditions. Three weather radars detected a droplet-rich eruption cloud. Top of eruption cloud heights of 0.3–5.5 km agl were measured with ground- and aircraft-based visual observations, web camera and NicAIR II infrared images, triangulation of scanning DOAS instruments, and the location of SO2 peaks measured by DOAS traverses. Cloud height and emission rate measurements were critical for initializing gas dispersal simulations for hazard forecasting.