SPACE/COSMOS
Better volcano eruption predictions on Earth--and Venus--thanks to Mauna Loa study
University of Pittsburgh
image:
(a) NERZ channelized lava flow front evolution obtained through Planet SuperDoves (PS), Landsat 8 (LS), and Sentinel 2 (S2) scenes (Table 1). The base map is a pre-eruption greyscale hillshade image from 3DEP (Table 1). (b) NERZ channelized lava flow front distance from the vent and calculated flow front areal coverage rate in km2/day.
view moreCredit: Courtesy of Ian Flynn/University of Pittsburgh
When Mauna Loa erupted in 2022, the largest lava flow headed on a path headed directly toward Daniel K. Inouye State Highway 200, also known as Saddle Road, a critical route that carries many residents from their homes on one side to their jobs on the other.
No one could accurately predict whether the lava would continue to flow and eventually block the highway, or stop short, sparing the road.
However, when the volcano next erupts scientists will be better able to monitor the eruption in real-time and make more accurate predictions about where the lava will flow and when the volcano might erupt. These advances are thanks to the availability of satellite data from public and private sources as well as machine learning algorithms developed at Pitt with help from a colleague in Italy, as highlighted in a recent publication in the Journal of Volcanology and Geothermal Research.
During the 13-day Mauna Loa eruption, Ian Flynn, research assistant professor in the Department of Geology and Environmental Science in Pitt’s Kenneth P. Dietrich School of Arts and Sciences, wasworking in the lab of Professor Michael Ramsey.
At the time, more data from privately launched satellites was becoming available to researchers. Ramsey wondered if those new sources could be combined with traditional government satellites to make better predictions. “He asked if I could map the lava flow in real time and actually see the flow-front advancing toward the only road that cuts across the island,” Flynn said.
He could. He was able to watch as the lava made its way toward the Saddle Road. “The concern was that lava was making a beeline toward the road,” Flynn said. “It stopped about 1.5 miles from the road.”
The best way to keep people safe in the event of an eruption, however, is to know as soon as possible before lava begins running down hillsides.
Every volcano has its own personality
Researchers already knew that increased heat and seismic activity are indicators of an upcoming eruption, but how hot? How much activity? How early? These questions are difficult to answer in general.
Working with a colleague, Dr. Claudia Corradino, from the Italian National Institute of Geophysics and Volcanology (INGV) the team was able to use a machine learning algorithm to identify a thermal increase one month before the start of the eruption. While this signal that an eruption was coming was identified after the eruption ended, any new insights into how a volcano behaves prior to erupting adds to scientists’ ability to predict when they’ll occur for the next eruption.
“Every volcano has its own personality,” Flynn said. “Yes, it’s cheesy, but it’s the truth. They’re all different.” His research has been focused on Mauna Loa for years, trying to decode how those changes relate to its eruptions.
Combining public and private data did just that. But Flynn thought there might be more useful information to extract. Particularly, the thickness of the lava flow. He reached out to Dr. Shashank Bhushan, a colleague working at NASA’s Goddard Space Flight Center.
Bhushan had done similar work with glaciers. “I reached out and asked, ‘can we use this methodology that you apply to glaciers and adapt it lava flows?’ He said, ‘I don’t know. Let’s try.’” It did work, and it gave Flynn and collaborators another tool to understand the eruption.
“Getting visible data helped us understand where it’s going,” Flynn said, but that data is two dimensional. “Now we can also generate flow thickness and understand how much material is coming out.” That information is key to understanding if an eruption has just begun or if it’s waning. It can also be analyzed in terms of the thermal trends to understand how the lava is cooling over time.
“One, if it’s still hot, it’s still a hazard. You don’t want someone walking along something that’s still degassing dangerous chemicals,” he said. And knowing when the lava cooled can help researchers more accurately analyze the lava’s composition.
And then there’s Venus
When we search for active lava flows on other planets, knowing how long it takes for lava to cool on Earth will help us to better understand what’s happening if we see a hot flow on Venus,” he said. Depending on the environmental conditions, rates of cooling should be different. “Knowing how lava cools enables scientists to better constrain our models when we find active volcanoes on other planets.”
As more data becomes available, not only do Flynn and his colleagues continue to learn more about the Mauna Loa eruption, they learn more about the kinds of information they’ll need to know about other volcanoes. There won’t likely be a one-size-fits-all solution to predicting eruptions for all volcanoes, but there may be a way to find a unique solution for predicting eruptions at individual sites.
Mauna Loa may be the most active volcano in the world, but others can be just as—if not more—threatening to people living nearby. Each has its own personality, and each may need its own, tailor-made monitoring system.
Journal
Journal of Volcanology and Geothermal Research
Method of Research
Computational simulation/modeling
Subject of Research
Not applicable
Article Title
Satellite data synergy for volcano monitoring: The 2022 Mauna Loa eruption
Why stars spin down, or up, before they die
Magnetic fields in the convection zone drives the rotation evolution of massive stars
Kyoto University
image:
Illustration of the inner regions of a massive star during its final oxygen (green) and silicon (teal) shell burning phase, before the collapse of the iron core (indigo). The strength and geometry of the magnetic field, combined with the properties of convection in the oxygen region can cause the rotation rate to speed up or slow down.
view moreCredit: KyotoU / Lucy McNeill
Kyoto, Japan -- From birth to death, stars generally slow by 100 to 1000 times their initial rotation rates; in other words, they spin down. The Sun's total angular momentum has declined as material is gradually blown off at the surface as solar wind. By observing this, astronomers have theorized the interaction between magnetic fields and plasma flow to be the most efficient way to spin down stars.
Why and how this happens has long interested astronomers, and recently an observational technique called astroseismology, which measures a star's natural oscillation frequencies, has made it possible to measure the internal rotation rates and magnetic fields of other stars in our galaxy. From this huge population, a picture of how stellar rotation decreases with stellar age has emerged, one that suggests that current theory is insufficient to explain the dramatic decrease in rotation.
Fascinated by astroseismology and by other researchers' 3D simulations of the solar convective zone, a team of researchers at Kyoto University was inspired to investigate how magnetic fields affect rotation inside massive stars..
"Our coauthors in Australia and the UK have already performed 3D magnetohydrodynamic simulations for massive stars before core-collapse. We suspected that the flow inside the massive star’s convective zone may evolve analogously with the solar convective zone," says team leader Ryota Shimada.
With a 3D simulation of a massive star, the researchers were able to directly investigate the complex interplay between violent convection, rotation, and magnetic fields. They confirmed that the internal rotation and magnetic field coevolve akin to the solar dynamo: the energy process that sustains our Sun's magnetic field. With these equations in hand, the team was able to mathematically predict the evolution of the star's internal rotation in time.
Their simulation reveals that the speed and direction of convective motions were influenced by rotation and magnetic fields over short timescales, which in turn changes the rotation, causing it to spin down or -- in some cases -- up. The team was able to formulate the interaction between convection, rotation, and magnetic fields as a model for radial transport of angular momentum outwards and inwards, showing that this transport in later burning phases is directly related to the geometry of the magnetic field.
"We were surprised to discover that some configurations of the magnetic fields actually spin the core up, suggesting that the final spin rate will be unique to the star's properties," says co-author Lucy McNeill. "Slow rotation might even be forbidden in some classes of massive stars."
Their discovery of magnetic angular momentum transport during advanced burning phases suggests that the theory developed to describe rotation in solar-type stars may be universal. Next, the team plans to create stellar evolution simulations depicting the whole lifetimes of various low to high mass stars to predict their rotation rates during various evolutionary stages.
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The paper "Angular momentum transport in the convection zone of a 3D MHD simulation of a rapidly rotating core-collapse progenitor" appeared on 27 April 2026 in The Astrophysical Journal, with doi: 10.3847/1538-4357/ae53da
About Kyoto University
Kyoto University is one of Japan and Asia's premier research institutions, founded in 1897 and responsible for producing numerous Nobel laureates and winners of other prestigious international prizes. A broad curriculum across the arts and sciences at undergraduate and graduate levels complements several research centers, facilities, and offices around Japan and the world. For more information, please see: http://www.kyoto-u.ac.jp/en
Journal
The Astrophysical Journal
Method of Research
Computational simulation/modeling
Subject of Research
Not applicable
Article Title
Angular momentum transport in the convection zone of a 3D MHD simulation of a rapidly rotating core-collapse progenitor
Article Publication Date
27-Apr-2026
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