arXiv subjects

D. Bor

Publications and source records attributed to D. Bor.

3 recordsLinked to original sources

arXiv2026

C-SWIM: A Coupled Space Weather Impact Model for Satellite Fleet Vulnerability and Economic Loss Under a 1-in-100-Year Solar Energetic Particle Event

Modern economies depend critically on satellite infrastructure, yet the aggregate economic consequences of extreme solar energetic particle (SEP) events have not been rigorously assessed. This study develops an integrated framework linking SEP hazard characterization, dynamic geomagnetic cutoff rigidity modeling, radiation dose transport, and fleet-wide failure probability estimation to macroeconomic impact analysis. Using extreme-value analysis of 160 SEP events over 27.4 years (1996-2025), failure probability is estimated for ~10,650 US operational satellites under orbital regime-dependent shielding assumptions. The assessment reveals that ~100 satellites (1.0%) are at Critical risk, concentrated in high-altitude low Earth orbit and highly elliptical orbit, while medium Earth orbit and geosynchronous orbit satellites fall in the Negligible class (P_fail < 10^-9) under the assumed radiation-hardened components and shielding. The expected capital loss across the ~$254B fleet totals ~$5.2B. Three failure scenarios, expanding from Critical satellites only (P_fail > 10^-2), to Critical and Elevated (P_fail > 10^-3), and to all satellites with non-negligible risk (P_fail > 10^-6), yield daily economic impacts of ~$70M, ~$270M, and ~$1.3B, respectively. Earth observation suffers up to 95.6% capacity loss in the worst case, while military services experience 16.1-20.4% disruption across scenarios. Results are first-order estimates: hardware failure counts are conservative because only total ionizing dose is modeled, and daily economic impacts represent upper bounds because operator response and recovery are not included.

physics.space-ph↗

arXiv2026

A Comparative Multi-Hazard Risk Assessment of the US High-Voltage Transmission Network

Modern economies depend critically on high-voltage power transmission networks. Yet this infrastructure is routinely disrupted by natural hazards ranging from earthquakes and floods to tornadoes and geomagnetic storms. Risk assessments have historically addressed hazards in isolation, leaving no common basis for comparing economic impacts across the full hazard portfolio. This study addresses this gap by developing an integrated framework linking hazard characterization, fragility modeling, and macroeconomic impact propagation. The framework is applied consistently across nine primary hazards and one compound freezing rain and wind gust hazard. Using national hazard datasets and a US high-voltage transmission network of over 13,000 line segments and 10,000 substations, we derive failure probabilities, expected damage, affected population, and downstream economic output losses. Among individual hazards, tropical cyclone wind produces the largest expected daily damage at $137 M/day, followed by lightning at $87 M/day, earthquake at $47 M/day, flood at $46 M/day, tornado at $42 M/day, and landslide at $34 M/day. Downstream economic output losses are largest for tornado at $4.93 B/day, followed by flood at $3.59 B/day and earthquake at $3.02 B/day. A 250-year geomagnetic storm produces $2.07 B/day, placing space weather within the range of major terrestrial hazards. The compound freezing rain and wind gust scenario produces the largest stress-test disruption, affecting 237.4 M people and yielding a modeled downstream output loss of $85.16 B/day. These results should be interpreted as first-order bounding estimates, with the compound scenario representing an upper-bound stress test. Overall, the framework establishes a consistent baseline for prioritizing investments in transmission network resilience.

econ.EM↗

arXiv2025

GIC--Related Observations During the May 2024 Geomagnetic Storm in the United States

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 ($\Delta \mathbf{B}$) 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 $\text{r}>0.8$ and a prediction efficiency between 0.4 and 0.7. The horizontal magnetic field perturbation from a baseline, $\Delta B_H$, computed by MAGE, SWMF, and OpenGGCM had a correlation r from $0.21$ to $0.65$. Two empirical relationships were considered: (1) how the correlation between measured GIC site pairs depended on differences in site separation distance, $\beta$ scaling factor (related to ground conductivity), and geomagnetic latitude; and (2) a regression model for the maximum $\mbox{GIC}$ magnitude at each site given the product of $\alpha$ (related to magnetic latitude) and $\beta$.

physics.space-ph↗