What you’re looking at: All measured SNRs plotted against time with Kp used for color/segmentation in the source. As Kp rises, geomagnetic disturbances increase D-region absorption at lower frequencies and can enhance irregularities in the F-region, often reducing decodable SNRs on HF.
Why it matters: SNR is the practical “link quality” that determines whether a mode will decode. A negative correlation with Kp, especially on the low and mid bands, supports the hypothesis that geomagnetic agitation hurts link margins. Watch for exceptions near grayline when F-layer height changes can temporarily boost SNR.
How to read: Look for median SNR drifting downward on disturbed days (Kp ≥ 4). Persistent outliers at high SNR during high Kp may indicate local or short-hop modes less affected by ionospheric absorption, or periods after substorm recovery.
What you’re looking at: Mean SNR aggregated by hour (adaptive bins) per band. This reveals band-specific sensitivity to Kp and local time.
Why it matters: Each band samples different ionospheric layers and MUFs. 80/40 m are often constrained by D-region absorption in daylight and are quite Kp-sensitive; 20/17/15 m need higher MUF and can collapse when Kp-driven disturbances lower foF2 or increase absorption.
How to read: Compare diurnal SNR envelopes between quiet (Kp ≤ 2) and active periods. Flattening or fragmentation of the usual day/night SNR curve is a fingerprint of geomagnetic disturbance.
What you’re looking at: Great-circle distances for QSOs vs time. Long-haul paths rely on stable F-region refraction; higher Kp can reduce maximum usable frequency and coherence length, shortening typical hops.
Why it matters: If Kp rises and the upper-HF MUF drops below the band in use, the longest workable paths disappear. On lower bands, excess absorption or increased scintillation can also reduce workable distance despite MUF being adequate.
How to read: Track the upper envelope of distance. A sagging upper envelope during high-Kp intervals indicates loss of long-haul capability. Spikes may correspond to grayline or trans-auroral events—check timestamps against your propagation report.
What you’re looking at: Per-band distance envelopes (min–max or percentiles) through time. This separates band-dependent impacts of geomagnetic activity.
Why it matters: High bands (20–10 m) show strong dependence on foF2 and can lose transoceanic capability quickly as Kp rises. Low bands (80–40 m) may hold mid-range paths at night but suffer daytime absorption and auroral absorption toward high latitudes.
How to read: Compare the long-distance tail per band vs quiet days. If only certain azimuths collapse, suspect auroral zone absorption or polar cap absorption.
What you’re looking at: The same distance behavior but with hour-wise adaptive aggregation to stabilize sampling when activity is sparse.
Why it matters: Adaptive bins reduce sampling bias across the day, so the diurnal structure (sunlit vs night sectors) and storm-time suppression become clearer.
How to read: Look for daylight compression on low bands and wholesale collapse of long-haul on high bands during Kp ≥ 4. Nighttime recovery often appears first on 40/30 m.
What you’re looking at: Time series of decoded/confirmed QSOs per band. Activity is a proxy for “band usability,” but it’s influenced by operator population and mode distribution.
Why it matters: During disturbed intervals, even if some links remain possible, the decode density drops. This plot complements SNR by showing the operational footprint, not just physics.
How to read: Expect reductions first on higher bands as Kp rises; if 40 m also dips in local day, suspect D-region absorption. Recovery sequence across bands gives clues to MUF and absorption changes.
What you’re looking at: Bar view of the same activity, helpful for day-to-day comparisons.
Why it matters: Makes it easier to spot storm days, weekend anomalies (operator behavior), and periods of exceptional opening.
How to read: Compare contiguous days at similar solar flux but different Kp to isolate Kp’s operational impact.
What you’re looking at: Unaggregated SNR points. This exposes bursty enhancements (e.g., TIDs, Es overlays on high bands) and decoding tails that averages obscure.
Why it matters: Outliers during high Kp might reveal short-skip or non-great-circle paths (scattering, chordal hops) that remain usable. Conversely, dense clouds of weak SNRs show storm-time fading and phase irregularities.
How to read: Correlate timestamp clusters with your Kp/AE indices and foF2 estimates. If outliers align with sunrise/sunset at either end, suspect grayline enhancement, not Kp immunity.
What you’re looking at: Unaggregated point-to-point distances for each QSO, plotted against Kp. This reveals rare long-haul events, mid-distance anomalies, and clustering patterns that get smoothed out in averages.
Why it matters: Outliers at high Kp can indicate sporadic-E links, F-layer ducting, or skewed paths that survive storm conditions. Conversely, a collapse of long-distance points into short hops shows when ionospheric support fails.
How to read: Look for sustained contacts in different distance regimes:
- Short-hop (<500 km): Usually NVIS or groundwave — survives poor conditions but rarely DX-worthy.
- Mid-skip (500–2000 km): Classic single-hop F-layer or sporadic-E; often the first to fade in geomagnetic storms.
- Long-haul (>8000 km): Multi-hop or chordal propagation; especially sensitive to Kp, but dramatic when it appears during high indices.
What you’re looking at: An interactive map of PSK spots—filtered by band and SNR—rendered from the clean QSO log.
Why it matters: See spatial propagation patterns at a glance. It’s a powerful companion to the temporal charts—giving you a geographic dimension to SNR, distance, and band-dependent behavior.
How to use: Hover or click on individual spots to view call, band, signal strength & more. Toggle filters if available for focused views.
Tip: if you open this file directly with a browser (file://), some browsers block local CSV fetches.
Use the file picker above or run a tiny local server (e.g., python -m http.server) from this folder so
the CSV loads automatically.