Hybrid physical–machine learning detection of polar vortex and stratospheric warmings

Integrating Space Weather and Atmospheric Physics: A (Δ±1)+ Machine Learning Pipeline for Polar Vortex Event Detection

Perez Pulido, C.J. · ISHEA Institute · Nature Communities Post · February 2026
DOI: 10.17605/OSF.IO/WXBDA | ISHEA Institute – Universal Coherence Division

The Question

Can space weather variables — energetic particle flux from solar activity — provide meaningful early signals for extreme stratospheric events like sudden stratospheric warmings (SSWs) and polar vortex disruptions?

This is not a new question. What is new is the approach: combining a physically motivated energy-state index (Δ±1) with gradient-boosted machine learning to capture the non-linear couplings that conventional dynamical models struggle to parameterize.

The Δ±1 Physical Index

The Δ±1 index provides a summary measure of planetary energetic state, integrating proton flux, electron flux, thermal gradients, and atmospheric chemistry into a single bounded variable:

\Delta E_{system} = \sum_i w_i E_{p,i} + \sum_j v_j E_{e,j} - k \nabla T

• Weights were derived independently from PCA on the 1998–2016 training period, with leave-one-year-out validation.
• The index is physically interpretable at every step — it does not assert deterministic causality, but tracks energetic coupling between the magnetosphere, ionosphere, and stratosphere.

The Hybrid Pipeline

• The Δ±1 index captures the physical signal.
• XGBoost captures residual non-linear interactions missed by the physical model.
• Together, they outperform either approach alone.

Data sources (1998–2024):

• NOAA-POES: energetic particle flux
• MetOp: atmospheric chemistry and temperature profiles
• GOES: solar proton events
• CSES: ionospheric parameters

Extreme events: n = 47 documented (~1.5% frequency)

Current Status: Demonstration Phase (synthetic data; observational validation pending)

Key Methodological Findings

Pipeline validated on synthetic data calibrated to the statistical structure of the observational record.
Primary results are from synthetic data. Application to observational datasets is the next validation step.

Feature importance (mean gain, synthetic):

• Δ±1 index: 40–45%
• Proton flux: 25–30%
• Electron flux: 15–20%
• Ozone: 10%
• Jet stream velocity: 5%

This hierarchy matches the physical hypothesis: energetic particle flux and integrated Δ±1 state dominate, while chemical and dynamical variables play supporting roles.

Why This Matters

Sudden stratospheric warmings are among the most impactful atmospheric events in the mid-latitude climate system. A 7–14 day lead time warning with meaningful skill would directly support:

• Seasonal forecasting
• Energy system planning
• Cold-weather risk assessment

Current operational models (ECMWF, NOAA) capture large-scale circulation dynamics well. The Δ±1 + ML framework complements them by adding sensitivity to energetic particle–chemistry coupling that dynamical models do not explicitly represent.

Next Steps

• Apply pipeline to hindcast 1998–2024 observational datasets
• Rolling-window prospective validation protocol
• Real ROC-AUC, PR-AUC, Brier score on observational data
• Prospective real-time validation during Solar Cycle 25 (2025–2035) as decisive test

Solar Cycle 25 is already underway and showing elevated activity, providing a natural prospective validation window over the coming years.

Open Questions for the Community

• What are the most robust observational proxies for energetic particle precipitation effects on stratospheric ozone?
• How should class imbalance be handled optimally for rare stratospheric events in ML frameworks?
• Are there existing hindcast datasets that would accelerate observational validation?

All scripts and synthetic data are publicly available for independent replication and methodological critique.

Resources

• Full paper (Main): DOI 10.17605/OSF.IO/WXBDA
• Supplementary material: DOI 10.17605/OSF.IO/WXBDA
• ISHEA Institute program overview: DOI 10.17605/OSF.IO/JBNGT
• Related: Integrating Planetary Energy Flows and Solar Cycles — DOI 10.17605/OSF.IO/GY83R

Perez Pulido, C.J. · ISHEA Institute · Nature Communities · February 2026 · DOI: https://doi.org/10.17605/OSF.IO/NV8ZW