Planetary Dynamics and Forbush Decreases: Long-Lead Environmental Regimes in Space Weather

Research Pathways Memo

Angel Edwards Santos
The Mindforge Research Institute (TMRI)
December 2025

Executive Summary

The Question: Can we identify environmental conditions that precede Forbush Decrease events before conventional space weather tools detect them?

Our Hypothesis: Forbush Decreases (rapid drops in cosmic ray intensity) happen when two conditions align: (1) a solar eruption (CME), and (2) a specific background state of the Sun-Earth system that makes it especially vulnerable to cosmic ray disruption. Traditional monitoring focuses only on condition #1. We hypothesize that the Planetary Dynamics Index (PDI) can detect condition #2 days to weeks in advance.

What We Found: Analysis of 31 Forbush Decrease events (2010-2024) shows that certain PDI configurations appear more frequently in the 7-14 days before events than during baseline periods. This suggests PDI captures something about heliospheric "setup" that makes the system conducive to strong cosmic ray drops when solar drivers arrive.

What This Means: If validated, this provides a fundamentally different approach to space weather context. Instead of reacting to solar eruptions, we could identify windows when the coupled system is in a vulnerable state, even before eruptions occur.

What We're Not Claiming: This is not a forecast system. We don't predict when CMEs will happen or guarantee that FDs will occur. We're mapping background environmental states and their statistical relationship to outcomes.

1. The Core Hypothesis

1.1 What Are Forbush Decreases?

Forbush Decreases are sudden drops (typically 5%+) in cosmic ray intensity measured at ground level. They happen when:

A coronal mass ejection (CME) launches from the sun
The CME and its associated magnetic structure reach Earth
The CME temporarily "sweeps out" or shields galactic cosmic rays

They matter because they indicate major changes in:

Radiation environment for satellites and polar aviation
Geomagnetic disturbance patterns affecting power systems
Background noise for communications and navigation

1.2 The Current Detection Gap

Existing tools have two time horizons:

Short-range (1-3 days): Once a CME is observed leaving the sun, models predict if/when it will reach Earth. Lead time depends on CME speed and model accuracy.

Zero-range (real-time): Neutron monitor networks detect cosmic ray drops as they happen, providing confirmation but no advance warning.

The Gap: What if the Sun-Earth system goes through slower "setup" processes that determine how strongly a given CME will affect cosmic rays when it arrives? Traditional monitoring doesn't look for these background states.

1.3 Our Research Question

We ask: Does the Planetary Dynamics Index (PDI), a composite framework tracking multiple aspects of the Sun-Earth system, encode information about these background vulnerability states?

Specific testable claim: Certain PDI configurations should be statistically over-represented in the days to weeks before Forbush Decreases compared to baseline periods.

2. The Planetary Dynamics Index (PDI)

2.1 What PDI Measures

PDI is a composite framework developed by TMRI to track the coupled Sun-Earth-magnetosphere system. It combines:

Geomagnetic activity patterns (not just current disturbance, but recent history and recovery profiles)
Solar energy input context (longer-term trends in radiation and particle flux)
Features of the near-Earth electromagnetic environment derived from public observations
Gravitational and tidal timing (lunar/solar cycles)

Important: PDI construction details (formulas, weights, thresholds) are proprietary. We describe only the conceptual structure and behavior.

2.2 Why PDI Might Work for This

The key insight is that cosmic ray modulation depends on large-scale heliospheric magnetic structure. The same processes that drive geomagnetic activity and modify the near-Earth environment also shape how cosmic rays propagate through the heliosphere.

By treating the system as a multi-component state vector rather than individual time series, PDI might capture:

Whether the magnetosphere is in a "relaxed" vs "stressed" configuration
Whether preceding events have established multi-day coupling patterns
Whether background conditions favor strong vs weak cosmic ray response to incoming CMEs

Analogy: Imagine weather forecasting. Rain requires both moisture and atmospheric instability. Traditional tools watch for storms (the trigger). We're trying to detect when atmospheric moisture is high (the precondition). Both matter, but they operate on different timescales.

3. What We Observed

3.1 Data and Methods

Event catalog: 31 documented Forbush Decreases from 2010-2024, identified using public neutron monitor data (primarily Oulu, Finland) and cross-referenced with NOAA space weather reports.

PDI data: Daily PDI values over the same period, derived from public space weather and geophysical data streams.

Analysis approach: For each FD event, we examined PDI behavior in the 30 days before onset. We then compared these "pre-event" PDI patterns to randomly selected baseline periods to see if certain configurations were over-represented.

Validation split: We held out 2021-2024 as a test period to check if patterns from 2010-2020 held in unseen data.

3.2 The Patterns We Found

Pattern 1: Extended geomagnetic sequences
Certain combinations of sustained (not spiking) geomagnetic activity followed by partial recovery appeared more often in the 7-14 days before larger Forbush Decreases. This wasn't just "activity is high"; it was specific temporal sequences.

Pattern 2: Multi-week regime drifts
Some PDI components showed slow (week-to-month) drift patterns where the system gradually moved into configurations that were statistically more common before FD clusters.

Pattern 3: Multi-component alignment
When multiple PDI components simultaneously entered specific ranges (not just one component being elevated), this configuration predicted elevated FD probability in the following 7-14 days.

Validation result: When we tested these patterns on the held-out 2021-2024 period, the statistical relationships held. The same PDI configurations that preceded events in the training period also preceded events in the test period.

3.3 What This Tells Us

These patterns suggest that PDI captures something real about heliospheric preconditioning. The system appears to move through distinguishable background states, and some states are more "FD-ready" than others.

What we're not saying:

We're not claiming PDI predicts CMEs
We're not claiming every "vulnerable state" produces an FD
We're not claiming this is deterministic

What we are saying:

PDI configurations encode probabilistic information about FD likelihood
This information appears days to weeks before conventional detection
The relationship validates across independent time periods

4. Why This Matters

4.1 A Different Kind of Context

Current space weather monitoring is event-driven: wait for a CME, then estimate arrival time and impact. PDI offers regime-based context: understand when the background system is in a state that makes events more likely or more severe.

These approaches are complementary:

CME models: Tell you when a specific driver is coming
PDI context: Tells you how primed the system is to respond

Combining them could give operators more complete situational awareness.

4.2 Potential Applications (If Further Validated)

Power grid operations: Extended preparation windows for geomagnetic disturbance potential

Satellite operations: Better context for risk assessment and safe-mode decisions

Aviation (polar routes): Enhanced radiation exposure forecasting

Scientific operations: Improved scheduling for cosmic ray experiments and space missions

4.3 What's Required for Operational Use

This research identifies statistical patterns. Turning it into an operational tool requires:

Validation on larger event catalogs
Testing across different solar cycle phases
Integration with existing forecast chains
Operational threshold determination
Performance tracking in real-time

TMRI's role is establishing that these patterns exist and are stable. Operational deployment is separate work.

5. Relationship to Mindforge Intelligence

TMRI is a research institute focused on understanding planetary dynamics and their relationship to Earth systems. Our work involves developing theoretical frameworks, analyzing historical patterns, and publishing scientific findings.

Mindforge Intelligence Systems is a separate commercial entity that develops applied products and services. Where TMRI research suggests viable applications, Mindforge Intelligence may pursue commercial development with appropriate validation and operational design.

This paper describes TMRI research only. It should not be interpreted as describing, validating, or promising any specific commercial service or system performance.

6. Next Research Steps

6.1 Event Catalog Extension

Current analysis uses 31 events over 15 years. Next steps:

Extend to full solar cycle coverage (1990-2024, ~90 events)
Include magnitude data beyond just binary event/non-event
Cross-validate with multiple neutron monitor stations

6.2 Mechanism Investigation

Why do these PDI patterns precede FDs? Candidates to investigate:

Heliospheric magnetic field topology evolution
Solar wind speed and density regimes
Magnetospheric boundary conditions
Multi-day coupling efficiency patterns

6.3 Cross-Cycle Validation

Test whether PDI-FD relationships are stable across:

Solar maximum vs minimum
Different solar cycles with different characteristics
Geographic/seasonal variations

6.4 Open Benchmarking

Where possible without exposing proprietary methods:

Publish standardized event catalogs
Propose evaluation protocols
Enable independent testing of alternative approaches

7. Conclusion

We observe that certain Planetary Dynamics Index configurations appear statistically more often in the 7-14 days before Forbush Decrease events than during baseline periods. This relationship validates in held-out data, suggesting PDI encodes real information about heliospheric background states.

This work establishes a hypothesis: that the coupled Sun-Earth system moves through distinguishable regimes, and some regimes are more conducive to strong cosmic ray modulation when solar drivers arrive. If correct, this provides a fundamentally different approach to space weather context - one based on background vulnerability states rather than just event detection.

Further research is needed to:

Extend validation across larger event catalogs
Understand physical mechanisms
Test operational viability

TMRI's contribution is demonstrating that these patterns exist and are statistically stable. What gets built from this research, and how it gets validated for operational use, is separate work.

References

Belov, A. (2008). Forbush Effects and their Connection with Solar, Interplanetary and Geomagnetic Phenomena. Proceedings of the International Astronomical Union, 4(S257).
Cane, H.V. (2000). Coronal Mass Ejections and Forbush Decreases. Space Science Reviews, 93, 55-77.
Richardson, I.G. & Cane, H.V. (2011). Galactic Cosmic Ray Intensity Response to Interplanetary Coronal Mass Ejections/Magnetic Clouds in 1995-2009. Solar Physics, 270, 609-627.
Lingri, D. et al. (2016). Solar Activity Parameters and Associated Forbush Decreases During the Minimum Between Cycles 23-24 and the Ascending Phase of Cycle 24. Solar Physics, 291, 1025-1041.
Oulu Neutron Monitor Database. University of Oulu, Finland. http://cosmicrays.oulu.fi/
NOAA Space Weather Prediction Center. Space Weather Data and Reports. https://www.swpc.noaa.gov/

Contact: research@mindforgeinstitute.org

About TMRI: The Mindforge Research Institute conducts research on planetary dynamics and their relationship to Earth systems. Our mission is to identify patterns in natural phenomena that could advance scientific understanding or inform future applications.

Suggested Citation: Edwards Santos, A. (2025). Planetary Dynamics and Forbush Decreases: Long-Lead Environmental Regimes in Space Weather. The Mindforge Research Institute Research Pathways Memo.