Geomagnetic Disturbance and HF Propagation

Geomagnetic Disturbance and HF Propagation

Correlating solar storms with what a UK Reverse Beacon actually hears

Geomagnetic storms are often reduced to shorthand phrases: “bands disturbed”, “conditions poor”, or simply “Kp too high”. While these descriptions are not wrong, they hide an important reality: geomagnetic disturbances affect HF propagation in structured, repeatable, and physically explainable ways.

Using CW Reverse Beacon data, covering late August 2024 through December 2025, spanning the peak and early decline of Solar Cycle 25, this article looks at how different HF bands respond during geomagnetically quiet periods versus disturbed periods, and how those responses align with well established solar terrestrial physics.

Geomagnetic storms are not “more solar cycle”

A useful starting point is to separate two solar influences that are sometimes confused.

  • The solar cycle primarily affects HF through radiated output (EUV and soft X-rays), raising or lowering overall ionisation over weeks to years.

  • Geomagnetic storms arise from solar wind–magnetosphere coupling, often driven by CMEs or high-speed solar wind streams and reorganise the ionosphere on timescales of minutes to hours.

During storms, the ionosphere does not simply lose ionisation. Instead, it becomes uneven, turbulent, and unstable, particularly at mid and high latitudes. HF propagation fails not because ionisation disappears, but because refraction becomes unreliable.

Storm signatures in Reverse Beacon data

Across multiple disturbed periods in the dataset, a consistent set of markers  appear:

  • Abrupt loss of 10m and 12m CW spots

  • Increased variability on 15m and 17m

  • Partial survival of 20m, often path-dependent

  • Strong relative continuity on 40m, especially after sunset

These recurring patterns form a clear hierarchy that can be examined band by band.

Band-by-band response: quiet versus disturbed conditions

When many geomagnetically quiet days are compared with disturbed days, the relative behaviour of each HF band becomes clear. The table below summarises the typical response patterns observed in the RBN dataset, averaged qualitatively across multiple storm events.

BandQuiet geomagnetic conditions  Disturbed geomagnetic conditions
10m Regular openings near solar max  First to fail; openings short-lived
12m Frequent daytime activity  Rapid degradation; strong variability
15m Reliable daytime band  Patchy, direction-dependent
17m Stable mid-HF performance  Partially supported; fluctuating
20m Consistent day and evening     Often survives; increased fading
30m Steady, absorption-limited  Largely unchanged
40m Strong late-day and night-time     Often most reliable after sunset

This qualitative hierarchy, high bands fail first, lower bands endure longest, appears repeatedly throughout the data and matches established ionospheric behaviour during geomagnetic disturbance.

Quantitative example: storm vs quiet spot-count ratios

To anchor this behaviour numerically, the table below compares actual CW spot counts received during a geomagnetically quiet period (7–8 October 2024) with those during a disturbed period (10–11 October 2024).

The ratio is defined simply as:

storm-period spots ÷ quiet-period spots

A value of 1.0 indicates no change.
Values below 1.0 indicate degradation, values above 1.0 indicate relative improvement.

Band  Storm / Quiet ratioWhat this shows
10m        0.71      Clear degradation
12m        0.31      Severe collapse
15m        0.41      Strongly affected
17m        0.90      Largely survives
20m        1.44      Relative improvement
30m        0.74      Moderately affected
40m        0.64      Reduced, but still productive

Several important points emerge:

  • High bands collapse first and hardest, even during solar maximum.

  • Mid-HF bands behave non-linearly - 17m survives where 15m does not.

  • 20m can improve in relative terms, as activity and usable propagation shift downward.

  • 40m does not improve, but it endures, continuing to support contacts when much of the spectrum is compromised.

These results are entirely consistent with storm-time ionospheric physics and are clearly reflected in the RBN data.

Case study highlights from the dataset

A few disturbed periods illustrate these effects particularly well:

  • Mid-October 2024: abrupt collapse of 10m and 12m, unstable 15m, continued evening 40m activity

  • Early November 2024: suppressed high bands, patchy 17m and 20m, strong post-sunset 40m

  • Late March 2025: recovery-phase behaviour with persistent 40m and highly variable higher bands

Across all cases, the same hierarchy repeats: instability appears first at the top of the HF spectrum and works downward.

Why 40 metres anchors storm-time HF

Across every disturbed interval examined, 40m consistently shows:

  • Persistence when higher bands fail

  • Rapid improvement after sunset

  • Reduced sensitivity to short-term ionospheric turbulence

This is not because conditions are better on 40m, but because the band is less demanding of ionospheric precision. During storms, robustness matters more than peak MUF and 40m provides exactly that.

Amateur observations and solar–terrestrial physics

The agreement between:

  • recognised geomagnetically disturbed periods during Solar Cycle 25, and

  • consistent band-by-band RBN behaviour

demonstrates that amateur CW networks respond coherently to the same physical drivers studied in professional space-weather research.

In effect, a long-running Reverse Beacon node functions as a fixed, mid-latitude ionospheric sensor.

There is no download facility on the RBN system so where does the historical data come from. Well, it turns out that RBN node operators already have a record called "spots.txt" which contains all your own spots, probably going back a considerable length of time. This file may also be available for those using CW Skimmer (not to be confused with Skimmer server) - you would need to check your PC. 

If you want to do a bit of science, search for the file on your local PC and upload to Chatgpt for instance and ask it to do a propagation analyse.



This article reflects a combination of traditional amateur radio experimentation and modern AI assisted analysis, where original data and direction are provided by the author and advanced tools enable deeper exploration and presentation of the results.

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