Edward J. Oughton

The proliferation of satellite megaconstellations in low Earth orbit represents a significant advancement in global broadband connectivity. However, we urgently need to understand the potential environmental impacts, particularly greenhouse gas (GHG) emissions associated with these constellations. This study addresses a critical gap in modeling current and future GHG emissions by developing an open‐source life cycle assessment methodology, applied to 12 launch vehicles and 24 megaconstellations. Our analysis reveals that fully deploying the 24 constellations would emit about 9.1 Mt CO ₂ . Propellant combustion during launch events (60.9%) and launcher production (31.6%) contribute most significantly to overall GHG emissions, accounting for 92.5% of modeled life cycle emissions. Reusing launch vehicles lowers production emissions per launch by 81%–97% relative to building a new vehicle for every flight, highlighting the environmental benefits of reusability in space technology. The findings underscore the importance of launch vehicle and satellite design choices to minimize potential environmental impacts. The Open‐source Rocket and Constellation Lifecycle Emissions (ORACLE) repository is freely available and aims to facilitate further research in this field. This study provides a critical baseline for policymakers and industry stakeholders to develop strategies for reducing the carbon footprint of the space industry, especially satellite megaconstellations.

Space debris fragments too small to track can still reach speeds capable of disabling a spacecraft. The number of such fragments remains uncertain, making it difficult to assess risks to satellites supporting Earth-critical services such as broadband and GPS. We combine radar detection with a Bayesian model to adaptively choose where a radar should point next, aiming to reduce uncertainty. We analyzed about 24 hours of data from each of the two European Incoherent Scatter Scientific Association (EISCAT) radars: the Tromsø ultra high frequency radar and the EISCAT Svalbard radar. We first used a Generalized Matched Filter to search for radar signals consistent with moving objects. Tests with 4,000 simulated object signals in raw voltage data led us to select cell-averaging Constant False Alarm Rate (CA-CFAR) detection. For external validation on another radar, we applied the CA-CFAR detector selected using the Tromsø recordings to the Svalbard recordings without retuning. The method found 155 events at Tromsø and 222 at Svalbard. Of these, 106 and 119, respectively, could not be confidently matched to cataloged objects. We used the Tromsø rate of unresolved events to calibrate a Bayesian population model informed by the Orbital Debris Engineering Model (ORDEM). With this model, we compared radar pointing schedules across 300 simulated worlds, giving each schedule 24 observations. Adaptive pointing with service weights reduced uncertainty at the final observation by 41.2%, 15.4%, and 5.35% relative to fixed, random, and preplanned schedules, respectively. The additional improvement over unweighted adaptive pointing was estimated at 1.55% (95% interval: -0.47% to 3.53%), a small difference that was not statistically significant. The simulations provide strong evidence that adaptive pointing can reduce uncertainty around hidden debris using the same number of observations, potentially improving the information used to assess risks to important satellite services.

Space debris fragments too small to track can still reach speeds capable of disabling a spacecraft. The number of such fragments remains uncertain, making it difficult to assess risks to satellites supporting Earth-critical services such as broadband and GPS. We combine radar detection with a Bayesian model to adaptively choose where a radar should point next, aiming to reduce uncertainty. We analyzed about 24 hours of data from each of the two European Incoherent Scatter Scientific Association (EISCAT) radars: the Tromsø ultra high frequency radar and the EISCAT Svalbard radar. We first used a Generalized Matched Filter to search for radar signals consistent with moving objects. Tests with 4,000 simulated object signals in raw voltage data led us to select cell-averaging Constant False Alarm Rate (CA-CFAR) detection. For external validation on another radar, we applied the CA-CFAR detector selected using the Tromsø recordings to the Svalbard recordings without retuning. The method found 155 events at Tromsø and 222 at Svalbard. Of these, 106 and 119, respectively, could not be confidently matched to cataloged objects. We used the Tromsø rate of unresolved events to calibrate a Bayesian population model informed by the Orbital Debris Engineering Model (ORDEM). With this model, we compared radar pointing schedules across 300 simulated worlds, giving each schedule 24 observations. Adaptive pointing with service weights reduced uncertainty at the final observation by 41.2%, 15.4%, and 5.35% relative to fixed, random, and preplanned schedules, respectively. The additional improvement over unweighted adaptive pointing was estimated at 1.55% (95% interval: -0.47% to 3.53%), a small difference that was not statistically significant. The simulations provide strong evidence that adaptive pointing can reduce uncertainty around hidden debris using the same number of observations, potentially improving the information used to assess risks to important satellite services.

Space weather events pose a growing threat to modern economies, yet their macroeconomic consequences remain underexplored. This study presents the first dedicated economic assessment of geomagnetic storm impacts on Aotearoa New Zealand, quantifying potential gross domestic product (GDP) losses across seven conservative disruption and mitigation scenarios due to an extreme coronal mass ejection (CME). The primary focus is on the damaging impacts of geomagnetically induced currents (GICs) on the electrical power transmission network. We support space weather mitigation investment decisions by providing a first‐order approximation of their potential economic benefits, using best‐in‐class scientific models, via a coupled physics‐engineering‐economic spatial modeling framework. Recognizing uncertainty in the economic interpretation of power outage impacts, we compare four different estimation methods. In the most severe unmitigated scenario, estimated GDP losses reach NZ$3.58 billion (0.98% of annual GDP). Targeted GIC‐informed scenarios still produce material losses, with no mitigation reaching up to NZ$1.48 billion (0.41% of annual GDP). Mitigation substantially reduces these impacts. Operational strategies, including optimized switching and islanding, achieve benefit‐cost ratios as high as 330 to 1, while physical protections such as GIC blocking devices produce returns up to 34.4 to 1. When also acknowledging additional unmodeled impacts, including multi‐billion losses in capital equipment and long‐term revenue, the economic rationale for pre‐emptive mitigation becomes even more pertinent.

The “Gannon Storm” in May 2024 was the largest space weather event experienced in 20 years, generating auroras at latitudes as low as 35°, visible in Florida and Mexico. Such events pose significant operational challenges for critical infrastructure operators, particularly those managing electricity transmission networks, satellite constellations, and aviation systems. Substantial progress has been made in understanding space weather and the potential exposure of infrastructure assets to severe events. Yet, we have seen very little evaluation of the types of current mitigation strategies used to reduce our shared vulnerability to this activity, motivating this study. First, we filled an important literature gap by undertaking a review of the range of space weather mitigation strategies available. Then, we contacted 313 critical infrastructure operators (50 power, 227 satellite, 36 aviation) for participation in an anonymous online survey and/or interview receiving 55 unique responses (18% response rate). We developed a time‐sensitive data set, through conducting qualitative interviews with 33 operators, capturing operational decisions made during peak solar activity. The results identified 91 potential mitigation decisions across the three sectors and found that 74% of these were undertaken. This is one of the first substantial studies of space weather mitigation activities and moves beyond the traditional focus on physical and economic impacts.

Space weather risk assessment is constrained by the lack of available asset information needed to model geomagnetically induced currents (GICs) in electricity transmission infrastructure. We propose a systematic method that enables risk analysts to collect their own open‐source substation data. Using a web browser platform for annotation, we convert OpenStreetMap (OSM) substation locations into high‐resolution, component‐level mappings of electricity transmission assets. We convert an initial 1,313 high‐voltage substations to 52,273 components using low‐altitude, satellite, and Street View imagery accessed through Google Earth, identifying 7,949 transformers. Compared to the OSM baseline, this approach provides detailed insights on voltage levels and substation configurations. We then construct a geospatial GIC network for the Tennessee Valley Authority (TVA) region, comparing May 2024 results with the University of Illinois Urbana‐Champaign 150‐bus (UIUC150) synthetic network and with measured ground GICs at 13 monitoring devices. The transformer types at unannotated substations and the grounding resistances are unknown, so we sample both across a Monte Carlo ensemble. This gives a median TVA 95th‐percentile peak ground GIC of 29.1 A, with a 90% confidence interval of 23.9–36.1 A. The UIUC150 network yields a 95th‐percentile peak ground GIC of 35.8 A under the same forcing, falling within this interval, and the modeled time series broadly capture the temporal morphology of the geomagnetic storm at the monitoring sites. This method shows promise for spatially explicit, screening‐level GIC assessment without requiring access to operator data.

Space weather poses an important but under‐quantified threat to society. While severe geomagnetic storms are recognized as potential global catastrophes, their socio‐economic impacts remain poorly quantified. We present a novel physics‐engineering‐economic framework that links geophysical drivers to power grid geoelectric fields, transformer vulnerability, and macroeconomic consequences. Using the United States as an example, we estimate daily economic losses for a 250‐year geomagnetic storm from transformer thermal heating of 1.81 billion USD (95 percent confidence interval: 1.65 to 1.96 billion USD), disrupting power for approximately 5.1 million people and 135,000 businesses. These estimates are conservative lower bounds, reflecting only transformer thermal heating effects and excluding voltage collapse, cascading failures, and restoration costs. The true societal risk could be substantially higher. Nonetheless, this contribution provides the first nationwide end‐to‐end coupling from space physics to potential macroeconomic loss, with quantified uncertainties. Our results demonstrate that coupled socio‐economic modeling of space weather is feasible and essential, and the framework is scalable and transferable, offering a template for assessing space weather risk to critical infrastructure in other countries.

The May 2024 geomagnetic storm was one of the most severe in the past 20 years. Understanding how large geomagnetic disturbances (GMDs) impact geomagnetically induced currents (GICs) within electrical power grid networks is key to ensuring their resilience. We have assembled and synthesized a large and unique set of GMD‐related data, compared model predictions with measurements, and identified empirical relationships for GICs in the contiguous United States for this storm. Measurement data include GIC data from 47 sites and magnetometer data from 17 sites. Model data include GIC computed by the Tennessee Valley Authority (TVA) power system operators at 4 sites, GIC computed using a reference model at 47 sites, and the difference in the surface magnetic field from a baseline computed at 12 magnetometer sites from three global magnetospheric models—the Multiscale Atmosphere‐Geospace Environment Model (MAGE), Space Weather Modeling Framework (SWMF), and Open Geospace General Circulation Model (OpenGGCM). GIC measured and computed by TVA had a correlation coefficient and a prediction efficiency between 0.4 and 0.7. The horizontal magnetic field perturbation from a baseline, , computed by MAGE, SWMF, and OpenGGCM had a correlation r from 0.21 to 0.65. Two empirical relationships were considered: (a) how the correlation between measured GIC site pairs depended on differences in site separation distance, scaling factor (related to ground conductivity), and geomagnetic latitude; and (b) a regression model for the maximum magnitude at each site given the product of (related to magnetic latitude) and .