Mehta, an associate professor of mechanical, materials, and aerospace engineering in WVU’s Benjamin M. Statler College of Engineering and Mineral Resources, said the project responds to a growing operational challenge as orbital traffic reaches what the university described as an all-time high. Geomagnetic storms are disturbances of Earth’s magnetic field and upper atmosphere triggered by solar eruptions or fast-moving streams of charged particles, known as solar wind.

They can disrupt radio signals, GPS navigation and satellites. Rather than providing only a warning that a storm will occur, the planned forecasting system is intended to communicate the probability and range associated with predicted outcomes. Mehta compared that need with everyday weather decisions.

If rain were certain, an observer might make one plan; if its chance were only 10%, the appropriate plan could be quite different. The project will bring together researchers from WVU, Ohio State University, George Mason University and the Southwest Research Institute, as well as the commercial satellite-tracking company LeoLabs. The Air Force Research Laboratory, the Japan Aerospace Exploration Agency and NASA will provide operational input.

The research teams will work in parallel throughout the four-year project rather than completing the component studies in sequence. WVU will lead two of the project’s four research thrusts. One will focus on storm forecasting in collaboration with Southwest Research Institute scientist Subhamoy Chatterjee, who will co-advise a WVU doctoral student.

The other will model how geomagnetic storms heat and expand the upper atmosphere, increasing atmospheric drag on satellites. Ohio State University will model how that drag changes satellite orbits and collision risk, while George Mason University will estimate the socioeconomic impact of space-weather disruptions. LeoLabs will supply radar-tracking data for use in the project’s modeling and validation work, and the three government and space-agency partners will contribute operational guidance.

During a geomagnetic storm, the upper atmosphere heats and expands, thickening the air through which satellites fly. The resulting drag slows satellites and pushes them away from their expected positions. Operators can temporarily lose track of satellites in these circumstances, raising the risk of collisions.

Moving a satellite is unlike steering a car around an obstacle: many spacecraft use low-thrust propulsion to conserve fuel, so changes in orbit can require hours or days of lead time. For most operators, the process also remains largely manual, which becomes especially demanding for constellations containing thousands of spacecraft. Mehta cited two historical incidents to illustrate the stakes.

In 1989, a major geomagnetic storm caused the U.S. military to temporarily lose track of more than 1,000 objects it was monitoring in orbit. In 2022, roughly 40 newly launched SpaceX satellites failed to raise their orbits after a space-weather event and were destroyed during reentry into the atmosphere. He described uncertainty as a thread running through the entire sequence linking solar activity, atmospheric drag, satellite orbits and collision risk.

It also extends to governments, businesses and consumers who depend on satellite services that may be degraded, temporarily interrupted or lost. Operators sometimes divert a satellite to avoid a collision and suspend its operations in the process. Mehta said many collision warnings ultimately prove to be false alarms because the underlying uncertainties have not been characterized correctly.

The planned project therefore seeks to improve not only the forecast of a geomagnetic storm but also the quantification of uncertainty along the chain from solar conditions to atmospheric changes, orbital motion and collision risk. The source did not report completed models, measured forecast performance or evidence that the initiative has already reduced false alarms or prevented satellite losses; those outcomes remain to be tested during the planned research. The scale of satellite operations is increasing.

According to Mehta, fewer than 2,000 total satellites were orbiting Earth in 2017, while Starlink now has more than 11,000 satellites in orbit. Technology companies including SpaceX and Google are pursuing plans to launch data centers into space, and Mehta said such facilities could eventually involve millions of satellites. Although the grant focuses on spacecraft, space weather can affect other services.

Scrambled radio signals may hinder communication during natural disasters and other emergencies, and geomagnetic storms have disrupted electrical power grids. The 1989 storm that affected military tracking also caused a nine-hour power outage in Quebec, affecting millions of people during winter. Beyond the technical research, the grant will fund educational work related to space weather.

The team plans to develop teaching materials, engage K-12 students in science and engineering and train the next generation of scientists and engineers in the field. The researchers also intend to release the resulting framework as open-source software, with the stated aim of encouraging innovation across the satellite industry. Mehta founded the ASSIST Lab—Astrodynamics, Space Science, and Space Technology—shortly after joining WVU in 2018.

He also leads the WVU Center for Innovation in Space Exploration and Research, established in 2023, which focuses on space situational and domain awareness, space safety and sustainability. Mehta’s previous NSF-supported work has included modeling Earth’s magnetosphere with artificial intelligence and machine learning, along with experiments on plasma-atmosphere interactions related to space-weather forecasting. He is a member of the National Oceanic and Atmospheric Administration’s Space Weather Advisory Group and previously served on the NASA Space Weather Council.

The new initiative will connect those areas of experience with the operational problem described in the grant announcement: developing forecasts that specify the likelihood, timing and potential severity of solar-storm effects for satellite operators. Its planned contribution is a framework for propagating uncertainty through forecasting, atmospheric-drag modeling, orbit prediction, collision-risk assessment and estimates of socioeconomic consequences.

Subject of Research: Probabilistic space-weather forecasting for satellite operations and orbital safety

Article Title: WVU researcher leads $2.7M effort to forecast space weather risks to satellites

Article References: WVU researcher leads $2.7M effort to forecast space weather risks to satellites. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: space weather, solar storms, geomagnetic storms, satellite safety, atmospheric drag, orbital forecasting, collision risk, machine learning, satellite tracking, National Science Foundation, researcher, leads

The core problem the project addresses is a translation gap. Space weather forecasters can already issue probabilistic warnings about geomagnetic storms, but satellite operators, insurers, and downstream service providers have limited tools for converting those probabilities into concrete operational and financial consequences. Oughton’s team, working with collaborators at West Virginia University, The Ohio State University, and the Southwest Research Institute, intends to build an end-to-end framework that carries uncertainty from the Sun all the way to society. The goal is a systems-level capability in which a probabilistic forecast of storm occurrence, timing, intensity, and driver type can be propagated through atmospheric physics, orbital mechanics, and economic models to yield quantified estimates of risk and loss.

The physics at stake is well understood but notoriously difficult to quantify. When a coronal mass ejection or a high-speed solar wind stream strikes Earth’s magnetosphere, energy is deposited into the upper atmosphere, heating and expanding the thermosphere. Satellites in Low Earth Orbit, roughly 200 to 2,000 kilometers above the surface, suddenly encounter denser air, and atmospheric drag increases sharply. During extreme historical events, such as the February 2022 incident in which dozens of newly launched Starlink satellites were lost to a modest geomagnetic storm, operators have seen orbital decay accelerate far faster than nominal models predicted. For Very Low Earth Orbit systems, which fly even lower to improve latency and imaging resolution, the drag problem is amplified because the neutral atmosphere is the dominant environmental hazard.

Under the project, the West Virginia University team led by Piyush Mehta will generate calibrated forecasts of geomagnetic storm occurrence, timing, intensity, and driver type. The distinction between driver types matters because coronal mass ejections and co-rotating interaction regions produce different storm profiles, with different onset speeds and durations, and therefore different drag signatures. Rather than issuing a single deterministic forecast, the framework will treat storm forcing as a stochastic process, producing bounded estimates of how a satellite’s drag response will evolve. This uncertainty-aware approach acknowledges a fundamental limitation of space weather prediction: even the best models cannot specify exactly how much energy will reach the upper atmosphere or how the thermosphere will respond at a given altitude and latitude.

Those stochastic drag estimates then feed into the operational layer of the framework. Mrinal Kumar’s group at The Ohio State University will contribute artificial intelligence methods for propagating uncertainty into the quantities that operators actually manage: orbital migration, degraded operational modes, conjunction-management burden, and orbit-correction demand. Conjunction management, the process of assessing and avoiding close approaches between space objects, is already a growing workload for constellation operators, and a major storm can inflate both the collision-avoidance burden and the frequency of station-keeping maneuvers simultaneously. By modeling these effects together rather than in isolation, the project aims to capture the compounding stress that a single storm can place on ground teams, propulsion budgets, and satellite lifetimes.

The final link in the chain is socio-economic. Subhamoy Chatterjee at the Southwest Research Institute will work with Oughton to connect operational disruptions to satellite service degradation, embedded sectoral dependencies, economic losses, and operator mitigation decisions. This is where the framework departs most clearly from traditional space weather research. Communication outages, navigation errors, and Earth-observation gaps do not remain in orbit; they cascade into sectors such as aviation, maritime transport, agriculture, finance, and emergency response, all of which now depend on satellite infrastructure. By embedding these dependencies into the model, the researchers intend to produce estimates of economic consequence that decision-makers outside the space sector can act upon, whether they are insurers pricing risk, regulators drafting resilience requirements, or governments planning for critical-infrastructure protection.

The timing of the award is significant. The number of active satellites has grown dramatically over the past decade, driven by commercial broadband constellations and the falling cost of launch, and many of these spacecraft operate in the drag-sensitive regime where space weather matters most. At the same time, the Sun progresses through its natural activity cycle, and the current period of high activity has delivered repeated reminders that even moderate storms can perturb operations. A severe storm on the scale of historical extremes could, by many expert assessments, cause widespread and prolonged disruption. Yet quantitative, uncertainty-aware estimates of what such a storm would do to today’s mega-constellations, and to the economies that rely on them, remain scarce. The project’s framework is designed to fill precisely that gap.

Artificial intelligence plays a central role throughout. Machine learning models can assimilate vast streams of solar wind, magnetospheric, and thermospheric data and learn relationships that are difficult to capture in first-principles physics codes, which are computationally expensive and still imperfect. But AI predictions are only useful for high-stakes operational decisions if their uncertainty is quantified and calibrated, which is why the project emphasizes uncertainty-aware methods rather than black-box forecasting. The researchers aim to produce forecasts whose stated confidence levels can be trusted, so that an operator deciding whether to lower a satellite’s orbit, safe-mode a spacecraft, or postpone a launch can weigh the true probability of adverse outcomes rather than a falsely precise point estimate.

The funding structure reflects the collaborative nature of the research. Each institution receives its own NSF award, with Oughton’s $499,837 supporting the George Mason component of the work. The linked awards to Mehta at West Virginia University, Kumar at The Ohio State University, and Chatterjee at the Southwest Research Institute formalize a partnership that spans space physics, computer science, and risk economics. This interdisciplinary composition is arguably essential: no single field currently owns the full chain from solar eruptive event to societal loss, and the project’s explicit ambition is to stitch those links together into a single, coherent modeling pipeline.

If the project succeeds, its outputs could reshape how the space industry and its regulators think about space weather risk. Instead of qualitative warnings that a storm is coming, operators and policymakers would have a defensible, probabilistic picture of how a forecast storm would propagate through satellite orbits, operational workloads, service availability, and economic losses, along with a structured way to evaluate mitigation options before the storm arrives. As humanity’s dependence on orbital infrastructure deepens, tools that make the invisible hazard of space weather measurable, and its consequences insurable and governable, may prove to be among the most consequential investments in the resilience of the digital age.

Subject of Research: AI-enabled, uncertainty-aware characterization of space weather risks to Low Earth Orbit satellites and their socio-economic impacts

Article Title: Oughton to receive funding for project on space weather satellite risk characterization and socio-economic impacts

Article References: Oughton to receive funding for project on space weather satellite risk characterization and socio-economic impacts. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: space weather, geomagnetic storms, satellites, Low Earth Orbit, artificial intelligence, uncertainty quantification, atmospheric drag, National Science Foundation, George Mason University, satellite operations, socio-economic impacts, conjunction management