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

This study introduces the C-SWIM framework to quantify the vulnerability of approximately 10,650 US satellites and the resulting macroeconomic losses from a 1-in-100-year solar energetic particle event, estimating that while only about 1% of satellites face critical failure risks, the worst-case scenario could cause up to 95.6% capacity loss in Earth observation services and daily economic impacts reaching $1.3 billion.

The Gist

Imagine the Earth is surrounded by an invisible, shifting force field—a magnetic shield that usually keeps the planet safe from a constant rain of high-energy particles from the sun. This paper, titled C-SWIM, acts like a sophisticated "damage control simulator" to ask a scary question: What happens if a "once-in-a-century" solar storm hits our satellite fleet?

Here is the story of the paper, broken down into simple concepts and analogies.

1. The Setup: The "1-in-100-Year" Storm

The researchers looked at 27.4 years of data on solar storms (from 1996 to 2025). They used a statistical tool (like a weather forecaster looking at the worst storms in history) to predict what a truly massive, "1-in-100-year" solar storm would look like.

• The Analogy: Think of this like a hurricane. We know Category 1 and 2 storms happen often. We've seen a few Category 4s. But we want to know what a "Category 6" storm looks like, even if we've never seen one. They built a "perfect storm" template based on the worst data they have, specifically the famous "Bastille Day" storm from 2000, and cranked it up to the extreme.

2. The Target: The US Satellite Fleet

The study focused on about 10,650 operational US satellites.

• The Analogy: Imagine a massive parking lot with 10,000 cars. Most are parked in a safe, covered garage (low orbits near the equator). Some are parked on a high, exposed hill (high orbits or polar routes). The researchers wanted to see which cars would get crushed if a giant hailstorm hit.

3. The Mechanism: How the Storm Breaks Things

The sun shoots out protons (tiny, fast particles). Usually, Earth's magnetic field acts like a bouncer, stopping these particles from entering certain areas.

• The "Bouncer" Analogy: During a normal day, the bouncer (Earth's magnetic field) keeps the protons out of the lower latitudes. But during a massive storm, the bouncer gets tired and the "force field" shrinks. Suddenly, particles that were previously blocked can slip through and hit satellites that are usually safe.
• The Damage: These particles don't just knock the satellites over; they slowly poison the electronics inside them (like radiation poisoning). Over time, or during a massive hit, the electronics fail, and the satellite dies.

4. The Results: Who Gets Hurt?

The study found that the damage is highly uneven. It's not a blanket disaster; it's a targeted strike.

• The "Safe Zone" (GEO & MEO): The big, expensive communication and GPS satellites (orbiting high above the equator) are like tanks. They are built with heavy armor (radiation-hardened parts) and thick shielding. Even though the storm hits them hard, they survive. Result: GPS and most TV/Internet satellites remain safe.
• The "Danger Zone" (High LEO & HEO): The satellites in high-altitude low Earth orbit (like some Earth observation cameras) and highly elliptical orbits (like some military early-warning satellites) are the victims. They are often built with cheaper, off-the-shelf computer parts (like the ones in your laptop) and have less armor.
  • The Finding: About 100 satellites (roughly 1% of the fleet) are in the "Critical" danger zone. They are likely to die.
  • The Cost: The total value of the fleet is about $254 billion. The study estimates a "expected loss" (accounting for the chance of failure) of about $5.2 billion. Most of this loss comes from those 100 vulnerable satellites, not the expensive ones in the "safe zone."

5. The Economic Ripple Effect: The "Domino" Impact

The paper doesn't just count broken satellites; it asks, "What happens to the economy if these services stop?" They used a model that tracks how one industry's failure hurts others (like a domino effect).

They tested three scenarios:

1. Likely Scenario (The "Bad Day"): Only the most critical 100 satellites fail.
   • Impact: About $70 million lost per day.
   • Who hurts? Military surveillance takes a hit.
2. Moderate Scenario (The "Worse Day"): A few more satellites fail, including some weather satellites.
   • Impact: About $270 million lost per day.
   • Who hurts? Weather forecasting and Earth observation (like taking photos of the planet) start to fail.
3. Worst-Case Scenario (The "Catastrophe"): Any satellite with any risk of failure dies.
   • Impact: About $1.3 billion lost per day.
   • Who hurts? Earth observation services lose 95% of their capacity. The financial sector, manufacturing, and government services take massive hits because they rely on data from these satellites.

6. The "Plain Language" Summary

If a massive solar storm hits once in a century:

• GPS and most TV satellites will be fine because they are built like tanks.
• About 100 satellites (mostly high-flying ones used for spying, weather, and Earth photos) will likely be destroyed.
• The financial hit could range from $70 million to $1.3 billion per day, depending on how many satellites actually break.
• The biggest victims would be Earth observation (taking pictures of the planet) and military surveillance, while commercial internet and GPS would barely notice.

What the Paper Doesn't Say

• It does not say the internet will go down globally (because the communication satellites are safe).
• It does not say GPS will stop working (because the GPS satellites are safe).
• It does not predict when this will happen, only what would happen if it did.
• It assumes the satellites fail permanently. In reality, operators might put them in "safe mode" to survive, which would cause temporary outages rather than permanent death, meaning the real-world damage might be slightly less than the worst-case numbers.

In short, the paper is a warning that while our "heavy armor" satellites are safe, our "lightweight" satellites in specific orbits are vulnerable, and losing them would be a very expensive headache for the economy, particularly for weather and military services.

Technical Summary

Technical Summary: 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

Problem Statement
Modern economies rely heavily on satellite infrastructure, yet the aggregate economic consequences of extreme Solar Energetic Particle (SEP) events remain under-assessed. While the physics of radiation effects on individual components and correlations between anomalies and geomagnetic indices are well-studied, a rigorous framework linking physical SEP exposure to fleet-wide failure probabilities and subsequent macroeconomic impacts is absent. Existing economic risk quantifications often rely on scenario-based approaches without explicit uncertainty propagation or direct coupling to probabilistic geophysical hazard models. This study addresses the gap by developing an integrated framework to quantify the vulnerability of the US operational satellite fleet and the resulting economic losses under a 1-in-100-year SEP event.

Methodology
The study employs the C-SWIM (Coupled Space Weather Impact Model) framework, which integrates four distinct methodological stages:

1. SEP Hazard Characterization:

   • Data: The model utilizes 27.4 years of proton flux observations (1996–2025) from the SEPEM Reference Data Set and GOES-16, identifying 160 SEP events.
   • Statistical Modeling: A Generalized Pareto Distribution (GPD) is fitted to peak fluxes and event fluences using the method of moments (MoM) to estimate a 1-in-100-year return level.
   • Template: The Bastille Day 2000 event serves as the baseline storm template, scaled linearly to match the 1-in-100-year return levels derived from the GPD analysis.

2. Satellite Orbital Exposure:

   • Fleet Definition: The study assesses 10,650 US operational satellites across Low Earth Orbit (LEO), Medium Earth Orbit (MEO), Geosynchronous Orbit (GEO), and Highly Elliptical Orbit (HEO).
   • Propagation: Trajectories are propagated over a 72-hour window using SGP4/SDP4 for TLE-equipped satellites and numerical integration for others.
   • Geomagnetic Shielding: Cutoff rigidity ($R_c$) is computed dynamically using the OTSO (Oulu Open-source geomagneToSphere propagation tool) particle tracing code, which solves the relativistic Lorentz force equation through the IGRF-13 and Tsyganenko 2001 (T01) magnetic field models.
   • Machine Learning Surrogate: To enable fleet-scale inference, a ResNetMLP model is trained on OTSO outputs to predict $R_c$ with a coefficient of determination of 0.9972, allowing for rapid assessment of particle access fractions ($f_{access}$) for the entire fleet.

3. Vulnerability Assessment:

   • Dose Transport: The effective fluence is calculated by scaling the 1-in-100-year fluence by the particle access fraction. This spectrum is transported through regime-dependent aluminum shielding using the SHIELDOSE-2 methodology within the IRENE framework.
   • Total Dose: The total dose environment ($D_{total}$) combines the SEP contribution with the accumulated trapped radiation dose (modeled via AP9/AE9) over the satellite's operational lifetime.
   • Failure Probability: Failure probability ($P_{fail}$) is derived by convolving the total dose distribution with lognormal device failure dose distributions (based on Xapsos et al., 2017). Satellites are classified into five vulnerability categories (Critical to Negligible) based on $P_{fail}$.
   • Assumptions: Due to proprietary data constraints, the study assumes regime-dependent shielding thicknesses and component hardness (e.g., Commercial/COTS for LEO, Radiation-Hardened for MEO/GEO).

4. Economic Impact Assessment:

   • Cost Modeling: Satellite replacement costs are estimated using a hybrid approach (public contract data, parametric models, and regime defaults).
   • Scenarios: Three failure scenarios are defined based on $P_{fail}$ thresholds:
     • Case 1 (Likely): Critical satellites ($P_{fail} > 10^{-2}$).
     • Case 2 (Moderate): Critical + Elevated ($P_{fail} > 10^{-3}$).
     • Case 3 (Worst Case): All non-negligible risk ($P_{fail} > 10^{-6}$).
   • Macro-Analysis: Service disruptions are mapped to economic sectors using an Input-Output (IO) framework (Ghosh inverse) to calculate direct and indirect daily economic losses.

Key Results

• Fleet Vulnerability: Under the 1-in-100-year SEP scenario, approximately 103 satellites (1.0% of the fleet) are classified as Critical, with an additional 36 as Elevated. These failures are concentrated in high-altitude LEO (>1000 km, high inclination) and HEO.
• Orbital Regime Disparity:
  • HEO: 12 of 13 assets are Critical, contributing $4.46B (86%) of the total expected loss.
  • High-Altitude LEO: 91 Critical satellites contribute $0.70B (14%) of the expected loss.
  • MEO and GEO: Despite comprising the majority of fleet value ($191.7B), these satellites fall into the Negligible risk class ($P_{fail} < 10^{-9}$) due to radiation-hardened components and heavy shielding.
• Economic Impact:
  • Expected Capital Loss: The total expected loss across the ~$254B fleet is $5.16B ±$0.88B.
  • Daily Economic Loss:
    • Case 1: ~$71M/day (driven by military/HEO assets).
    • Case 2: ~$269M/day (weather and Earth observation services begin to fail).
    • Case 3: ~$1.28B/day (Earth observation suffers ~95.6% capacity loss; Finance and Real Estate become the largest impacted sectors).
• Service Disruption: Earth Observation (EO) is the most vulnerable service, while GPS (MEO) and commercial communications (LEO/GEO) show high resilience due to constellation redundancy and radiation hardening.

Significance and Claims
The paper claims to provide the first integrated framework linking probabilistic SEP hazard characterization, dynamic geomagnetic shielding, radiation dose transport, and macroeconomic impact analysis for a satellite fleet. Key contributions include:

1. Methodological Integration: The study replaces traditional radiation design margin approaches with a physics-based failure probability ($P_{fail}$) pipeline that explicitly accounts for orbital environment, shielding, and component hardness.
2. Risk Stratification: It demonstrates that failure risk is highly stratified, concentrated in specific orbital regimes (HEO and high-altitude LEO) rather than the high-value GEO platforms that dominate fleet replacement costs.
3. Economic Quantification: By translating physical failure probabilities into sectoral economic losses via IO analysis, the study offers decision-makers a range of outcomes (from $70M to $1.3B daily) tied to explicit assumptions about failure thresholds.
4. Policy Alignment: The framework aligns with the National Space Weather Strategy's objectives for infrastructure protection and impact-based products, providing actionable data for fleet operators, insurers, and policymakers.

Limitations and Scope
The authors explicitly state that these results are first-order estimates. The analysis considers only Total Ionizing Dose (TID), excluding Single-Event Effects (SEEs) like upsets or latch-ups which could cause temporary service interruptions. The study assumes permanent failure for the economic scenarios, whereas operators may mitigate damage via safe modes. Furthermore, the model treats the fleet as static and does not account for real-time operator responses or satellite-specific shielding data, which are generally proprietary. Consequently, the daily economic impacts represent upper bounds.