Geology topics

Michael J. Pavolonis

Publications and source records attributed to Michael J. Pavolonis.

7 recordsLinked to original sources

USGS2023

Lightning rings and gravity waves: Insights into the giant eruption plumefrom Tonga’s Hunga Volcano on 15 January 2022

On 15 January 2022, Hunga Volcano in Tonga produced the most violent eruption in the modern satellite era, sending a water-rich plume at least 58 km high. Using a combination of satellite- and ground-based sensors, we investigate the astonishing rate of volcanic lightning (>2,600 flashes min −1 ) and what it reveals about the dynamics of the submarine eruption. In map view, lightning locations form radially expanding rings. We show that the initial lightning ring is co-located with an internal gravity wave traveling >80 m s −1 in the stratospheric umbrella cloud. Buoyant oscillations of the plume's overshooting top generated the gravity waves, which enhanced turbulent particle interactions and triggered high-current electrical discharges at unusually high altitudes. Our analysis attributes the intense lightning activity to an exceptional mass eruption rate (>5 × 10 9 kg s −1 ), rapidly expanding umbrella cloud, and entrainment of abundant seawater vaporized from magma-water interaction at the submarine vent.

Hunga Volcano↗

USGS2022

Eruption dynamics leading to a volcanic thunderstorm— The January 2020 eruption of Taal volcano, Philippines

Advances in global lightning detection have provided novel ways to characterize explosive volcanism. However, researchers are still at the early stages of understanding how volcanic plumes become electrified on different spatial and temporal scales. We deconstructed the phreatomagmatic eruption of Taal volcano (Philippines) on 12 January 2020 to investigate the origin of its powerful volcanic thunderstorm. Satellite analysis indicated that the water-rich plume rose >10 km high before creating lightning detected by Vaisala's global lightning data set (GLD360). Flash rates increased with plume heights and cloud expansion over time, producing >70 flashes min –1 . Photographs revealed a highly electrified region at the base of the umbrella cloud, where we infer strong convective updrafts and icy collisions enhanced the electrical activity. These findings inform a conceptual model with overlapping regimes of charge generation in wet eruptions—initially due to ash particle collisions near the vent, followed by thunderstorm-like electrification in icy regions of the upper plume. Despite the wide reach of Taal's ash cloud, most of the lightning occurred within 20–30 km of the volcano, producing thousands of hazardous cloud-to-ground flashes over a densely populated area. The eruption demonstrates that volcanic lightning can pose a hazard in its own right, embedded within the broader hazards of explosive volcanism in an urban setting.

Taal volcano↗

USGS2021

Progress in protecting air travel from volcanic ash clouds

The Eyjafjallajökull eruption of 2010 demonstrated the far-reaching impact of ash clouds and the vulnerability of our jet-based society to them, prompting a review of procedures to detect, warn, and forecast ash cloud hazards to aviation. The years since 2010 have seen marked improvements in satellite technology, more accurate ash-dispersion models that integrate simulations with observations, and more thoroughly vetted and harmonized procedures for forecasting and communicating hazards. In the coming decade, we expect these trends to continue, with formal warning products migrating from text and simplified maps to 4D data streams. This paper details some of these advancements and challenges ahead.

Bulletin of Volcanology↗

USGS2020

Forecasting, detecting, and tracking volcanic eruptions from space

Satellite monitoring of volcanic activity typically includes four primary observations: (1) deformation and surface change, (2) gas emissions, (3) thermal anomalies, and (4) ash plumes. These phenomena are imaged by remote sensing data that span the electromagnetic spectrum, from microwave to ultraviolet energy and including visible and infrared wavelengths. The primary uses of satellite data in volcanology are forecasting, detecting, and tracking eruptive activity. Eruptions are often preceded by a number of indicators that are detectable from space, including surface deformation, subtle increases in surface temperature, and elevated gas emissions. The first indications of eruption, especially at remote volcanoes, are often identified in satellite data by strong thermal anomalies and/or the presence of ash and gas in the atmosphere, the recognition of which can be automated for rapid eruption detection. Once an eruption is in progress, space-based imagery of all types can track activity over time, providing information on the emplacement of volcanic deposits, the presence and character of ash plumes, and potential changes in the character of the eruption, all of which aid hazards assessment. Activity at Agung volcano, Indonesia, during 2017–2019, offers an excellent example of the importance of remote sensing datasets for forecasting, detecting, and tracking eruptions. Challenges to exploiting current and future satellite data include ensuring regular acquisitions over active volcanoes and developing tools for automated analysis of the massive volume of imagery for volcano-related signals.

Remote Sensing in Earth Systems Sciences↗

USGS2019

Globally detected volcanic lightning and umbrella dynamics during the 2014 eruption of Kelud, Indonesia

Volcanic lightning shows considerable promise as a monitoring and research tool to characterize explosive eruptions. Its key strengths are rapid and remote detection, because the radio signals produced by lightning can propagate thousands of km at the speed of light. Despite these tantalizing properties, the scientific work on volcanic lightning has only recently started gaining momentum. Much more is needed to understand what lightning reveals about the evolution of an eruption in near-real time. Here we examine the timing and energy release of lightning generated by the eruption of Kelud volcano in Indonesia on 13 February 2014, as detected by the World Wide Lightning Location Network (WWLLN). The eruption column reached at least 26 km above sea level, representing the highest plume since the advent of global lightning networks in the last decade. Therefore, it provides valuable constraints on the electrification of end-member, sustained Plinian columns. We investigate the lightning in context with satellite images, photographs, and other published studies. Results show that the earliest satellite-detected activity was a thermal anomaly at ~15:46 UTC, corresponding to a directed blast at the onset of eruption (and only a few lightning strokes). Following a brief pause, the eruption produced a sustained column and umbrella cloud that spread outward into the tropical stratosphere. Rates of umbrella expansion provide an average mass eruption rate (MER) in the range of 8 × 10 7 –1 × 10 8 kg s −1 . A more nuanced picture emerges from the time-varying MERs (determined between each satellite pass), which show rapid intensification during the first hour of eruption, followed by constant MER for about an hour, and waning toward the end (after ~17:50 UTC). At this stage, decreasing flux into the umbrella cloud coincides with column instability and formation of pyroclastic density currents, as recorded by photos from the ground ~17:45 UTC. We infer that some of the erupted mass partitioned into ground-hugging currents, leading to a lower apparent MER. Interestingly, there is not a 1:1 correlation between lightning intensity and MER over the course of eruption. Stroke rates increase sharply within the first 30–40 min (during rapid intensification of the plume), and then drop below 2 strokes per min once the MER remains constant. This suggests that electrification was controlled by the rate of increase in MER—in other words, the acceleration of particles out of the vent. We also show that lightning stroke-rates and energies are greatest within 50 km of the vent, even when the ash cloud extends >200 km downwind, indicating that lightning was focused in the regions of highest particle concentration and turbulence. Overall, we conclude that abrupt changes in lightning rates are clearly linked to changes in eruption behavior, and that rapid detection could aid monitoring efforts to characterize eruption rates or styles.

Journal of Volcanology and Geothermal Research↗

USGS2017

Volcanic cloud evolution: Characteristics, observational capabilities and challenges

Volcanic clouds composed of solid particles, volcanic gases, and related aerosols evolve from the time of eruption until the cloud constituents are removed from the atmosphere. While airborne, they have the potential to cause damage to aircraft, ranging from acute encounters that can lead to an immediate hazard to flight safety, to chronic wear on aircraft components, to benign encounters where no observable impacts occur. We highlight the evolution of cloud properties through three stages: Stage 1 (recent), through Stage 2 (intermediate), to Stage 3 (final) and comment on the current observational capabilities and challenges of detection and characterization of volcanic clouds.

Conference Paper↗

USGS2012

A robust method to forecast volcanic ash clouds

Ash clouds emanating from volcanic eruption columns often form trails of ash extending thousands of kilometers through the Earth's atmosphere, disrupting air traffic and posing a significant hazard to air travel. To mitigate such hazards, the community charged with reducing flight risk must accurately assess risk of ash ingestion for any flight path and provide robust forecasts of volcanic ash dispersal. In response to this need, a number of different transport models have been developed for this purpose and applied to recent eruptions, providing a means to assess uncertainty in forecasts. Here we provide a framework for optimal forecasts and their uncertainties given any model and any observational data. This involves random sampling of the probability distributions of input (source) parameters to a transport model and iteratively running the model with different inputs, each time assessing the predictions that the model makes about ash dispersal by direct comparison with satellite data. The results of these comparisons are embodied in a likelihood function whose maximum corresponds to the minimum misfit between model output and observations. Bayes theorem is then used to determine a normalized posterior probability distribution and from that a forecast of future uncertainty in ash dispersal. The nature of ash clouds in heterogeneous wind fields creates a strong maximum likelihood estimate in which most of the probability is localized to narrow ranges of model source parameters. This property is used here to accelerate probability assessment, producing a method to rapidly generate a prediction of future ash concentrations and their distribution based upon assimilation of satellite data as well as model and data uncertainties. Applying this method to the recent eruption of Eyjafjallajökull in Iceland, we show that the 3 and 6 h forecasts of ash cloud location probability encompassed the location of observed satellite-determined ash cloud loads, providing an efficient means to assess all of the hazards associated with these ash clouds.

Journal of Geophysical Research D: Atmospheres↗