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arxiv: 2507.07009 · v5 · pith:N2FRFWKWnew · submitted 2025-07-09 · ⚛️ physics.space-ph

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

L. A. Wilkerson , R. S. Weigel , D. Thomas , D. Bor , E. J. Oughton , C. T. Gaunt , C. C. Balch , M. J. Wiltberger
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A. Pulkkinen
This is my paper

Pith reviewed 2026-05-19 06:33 UTC · model grok-4.3

classification ⚛️ physics.space-ph
keywords geomagnetically induced currentsGICgeomagnetic stormpower gridsspace weathermagnetic field modelingempirical relationshipsMay 2024
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The pith

Data from the May 2024 geomagnetic storm enable empirical relationships for predicting GIC magnitudes and site correlations across the US power grid.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper assembles measurements of geomagnetically induced currents from 47 sites and magnetic field data from 17 magnetometers during the severe May 2024 storm. Comparisons show that GIC values computed by TVA operators correlate strongly with observations, while horizontal magnetic field changes from three global models correlate more modestly. Using these observations, the authors develop empirical formulas that relate GIC site-pair correlations to physical separation, a conductivity-related beta factor, and latitude, plus a regression to estimate peak GIC from an alpha-beta product.

Core claim

Measurements and model outputs from the May 2024 storm demonstrate that GIC computed by TVA matches observations with correlation above 0.8, global model Delta B_H correlations range from 0.21 to 0.65, and GIC properties follow two empirical relationships dependent on site separation, beta scaling for conductivity, geomagnetic latitude, and the alpha-beta product.

What carries the argument

The beta scaling factor, which proxies ground conductivity differences between sites, combined with alpha as a magnetic latitude factor in regression models for maximum GIC and inter-site correlations.

If this is right

  • TVA local GIC computations match measurements with r greater than 0.8 for this intense storm.
  • Global magnetospheric models yield Delta B_H correlations between 0.21 and 0.65, indicating limits in current space weather predictions.
  • GIC correlations between sites decrease with greater separation distance and vary systematically with beta and latitude.
  • Maximum GIC magnitude at a site can be estimated by regression on the product of alpha and beta.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The empirical relationships could be applied to future storms to test whether the conductivity and latitude effects remain consistent.
  • Grid operators might use beta scaling values to prioritize monitoring or hardening at sites with higher predicted GIC risk.
  • Adding data from additional sites or different geomagnetic events would help determine how generally the alpha-beta regression holds.
  • These relationships could be incorporated into operational forecasting to improve estimates of induced currents during space weather events.

Load-bearing premise

The beta scaling factor accurately represents differences in ground conductivity between sites, and the 47 GIC sites plus 17 magnetometer sites adequately represent conditions across the contiguous United States.

What would settle it

A future geomagnetic storm where measured GIC site-pair correlations deviate significantly from the derived dependence on separation distance, beta scaling, and latitude would challenge the empirical relationships.

Figures

Figures reproduced from arXiv: 2507.07009 by A. Pulkkinen, C. C. Balch, C. T. Gaunt, D. Bor, D. Thomas, E. J. Oughton, L. A. Wilkerson, M. J. Wiltberger, R. S. Weigel.

Figure 1
Figure 1. Figure 1: Solar wind conditions from the DSCOVR (Deep Space Climate Observatory) [PITH_FULL_IMAGE:figures/full_fig_p012_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: GIC and magnetometer locations and locations where model estimates are [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: GIC times series from 15 sites from TVA with 40 A baseline offsets sorted by [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: GIC data from 32 sites from NERC’s ERO portal with 40 A baseline offsets [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: ∆BH measurements from 15 sites from NERC’s ERO portal and 7 sites from TVA with 400 nT baseline offsets sorted by geomagnetic latitude and labeled with the site ID and its geomagnetic latitude and longitude in degrees. 12 2024-05-10 16 20 00 2024-05-11 04 08 12 16 20 00 2024-05-12 04 50100 (39.2,-18.9) 50103 (42.1,-18.7) Ackerman (42.1,-18.7) 50127 (43.0,-53.0) 50120 (43.1,-18.3) Union (43.2,-18.4) 50119 (… view at source ↗
Figure 6
Figure 6. Figure 6: Measured GIC, TVA model, and Reference Model time series. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Predicted and measured ∆BH time series comparison. –18– [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Correlation between GIC time series for 1,081 site pairs vs. differences in site [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: GIC linear regression relationships. The white circle is an outlier that was ex [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
read the original abstract

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 $\beta$.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript reports GIC and magnetometer observations during the May 2024 geomagnetic storm, using data from 47 GIC sites and 17 magnetometer sites across the contiguous United States. It compares TVA-computed GIC with measurements (r > 0.8, prediction efficiency 0.4–0.7), evaluates ΔB_H from three global models (MAGE, SWMF, OpenGGCM) against observations (r = 0.21–0.65), and derives two empirical relationships: site-pair GIC correlation as a function of separation distance, β scaling factor, and geomagnetic latitude, plus a regression for maximum GIC magnitude based on the product α × β.

Significance. If the empirical relationships prove robust and generalizable, the work supplies practical, observationally grounded tools for estimating GIC magnitudes and correlations that could aid power-grid operators in the United States during severe storms. The direct TVA comparison and multi-model ΔB_H evaluation provide concrete benchmarks for space-weather impact studies; the large, multi-source dataset assembled for a single extreme event is a clear strength.

major comments (2)
  1. [Abstract, final paragraph] Abstract and final paragraph: the claim that the two empirical relationships are applicable to the contiguous United States rests on the untested assumption that the 47 GIC and 17 magnetometer sites adequately sample the diversity of ground conductivities, geomagnetic latitudes, and grid configurations across the US. No cross-validation, conductivity-model comparison, or sensitivity test to site selection is described; this directly affects the generalizability of the reported relationships.
  2. [Abstract] Abstract: the regression for maximum GIC magnitude is constructed from the product of α and β whose values are fitted to the same site data used to derive the regression itself. This introduces partial circularity; the manuscript should clarify whether the fit is purely descriptive or intended to be predictive, and report any independent validation or out-of-sample testing performed.
minor comments (2)
  1. [Abstract] The abstract and results sections report correlation coefficients and prediction efficiencies without accompanying uncertainties or error bars; adding these would strengthen the quantitative comparisons.
  2. [Methods] No explicit statement of data exclusion criteria or handling of missing intervals is provided for the 47 GIC sites; a brief methods subsection on quality control would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which help clarify the scope and limitations of our empirical relationships. We address each major comment below and will revise the manuscript accordingly to improve precision and transparency.

read point-by-point responses

  1. Referee: [Abstract, final paragraph] Abstract and final paragraph: the claim that the two empirical relationships are applicable to the contiguous United States rests on the untested assumption that the 47 GIC and 17 magnetometer sites adequately sample the diversity of ground conductivities, geomagnetic latitudes, and grid configurations across the US. No cross-validation, conductivity-model comparison, or sensitivity test to site selection is described; this directly affects the generalizability of the reported relationships.

    Authors: We acknowledge that the original manuscript does not include formal cross-validation, sensitivity tests to site selection, or direct comparisons against conductivity models. The 47 GIC and 17 magnetometer sites are distributed across the contiguous United States and span geomagnetic latitudes from approximately 30° to 50°, covering multiple geological provinces. However, we agree this does not constitute a comprehensive sampling of all possible ground conductivities and grid configurations. In the revised manuscript we will add a dedicated paragraph in the discussion section that (i) maps the sites onto available USGS conductivity models, (ii) quantifies the latitudinal and longitudinal coverage, and (iii) explicitly states the limitations on generalizability. We will also revise the abstract and concluding paragraph to qualify the applicability statement, indicating that the relationships are derived from the available sites for this event and require further validation for broader use. revision: yes

  2. Referee: [Abstract] Abstract: the regression for maximum GIC magnitude is constructed from the product of α and β whose values are fitted to the same site data used to derive the regression itself. This introduces partial circularity; the manuscript should clarify whether the fit is purely descriptive or intended to be predictive, and report any independent validation or out-of-sample testing performed.

    Authors: The regression is an empirical, descriptive fit to the maximum GIC magnitudes observed during the May 2024 storm; α and β were determined from the same 47-site dataset to characterize the relationship for this specific event. It is not presented as a predictive model for future storms or unsampled locations. No out-of-sample testing was performed, as the analysis is tied to the unique, high-resolution dataset collected for this extreme storm and the modest number of sites precludes meaningful hold-out validation. In the revised manuscript we will (i) explicitly label the regression as descriptive in the abstract, methods, and results, (ii) report the lack of independent validation, and (iii) add a forward-looking statement recommending validation against additional storm events. revision: yes

Circularity Check

1 steps flagged

Max GIC regression constructed from alpha-beta product fitted to same 47-site dataset

specific steps
  1. fitted input called prediction [Abstract]
    "Two empirical relationships were considered: (1) how the correlation between measured GIC site pairs depended on differences in site separation distance, β scaling factor (related to ground conductivity), and geomagnetic latitude; and (2) a regression model for the maximum GIC magnitude at each site given the product of α (related to magnetic latitude) and β."

    α and β are scaling factors estimated from the same 47 GIC site measurements to capture latitude and conductivity effects. The regression then directly models maximum GIC using the product α×β on those identical sites, so the reported relationship is a fit to the fitted inputs rather than an independent prediction or derivation.

full rationale

The paper's core observational comparisons (TVA GIC r>0.8, model ΔB_H correlations 0.21-0.65) are independent of the empirical fits. However, the two 'empirical relationships' are derived by fitting α (latitude) and β (conductivity proxy) directly to the measured GIC values at the identical 47 sites, then regressing max GIC on their product. This reduces the claimed general relationship to a statistical fit on the input data by construction, matching the 'fitted input called prediction' pattern. The site-pair correlation analysis similarly incorporates the same β scaling. No self-citations, definitional loops, or imported uniqueness theorems appear in the provided text. The representativeness assumption for US-wide applicability is a validity concern rather than circularity.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central empirical relationships rest on two scaling parameters whose values are determined from the storm data itself and on the domain assumption that ground conductivity can be represented by a single per-site beta factor.

free parameters (2)
  • alpha
    Scaling factor tied to geomagnetic latitude and used as input to the regression for maximum GIC magnitude at each site.
  • beta
    Scaling factor tied to local ground conductivity and used both in site-pair correlation analysis and in the alpha-beta regression.
axioms (1)
  • domain assumption Differences in ground conductivity across sites can be captured by a single scalar beta factor that multiplies magnetic perturbations to estimate GIC.
    Invoked when constructing both empirical relationships described in the final paragraph of the abstract.

pith-pipeline@v0.9.0 · 5920 in / 1595 out tokens · 52490 ms · 2026-05-19T06:33:29.967755+00:00 · methodology

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