This paper validates that distance-structured correlations in GNSS clocks exist in raw observations using broadcast ephemerides, not just precise products—strongly constraining precise-product processing artifacts. Broadcast ephemerides still contain control-segment information, so Satellite Laser Ranging and non-GNSS optical checks remain necessary for definitive confirmation. Prior TEP analyses relied on precise orbit and clock products from global analysis centers, leaving open the possibility that observed signatures were artifacts of sophisticated processing chains. This paper addresses that concern by detecting distance-structured signatures in raw GNSS observations processed using Single Point Positioning (SPP) with broadcast ephemerides as the primary methodology, supplemented by precise ephemeris validation. Analysis of 539 globally distributed stations over 3 years (2022–2024, comprising 1. 17 billion pair-samples across three independent filtering strategies) achieves consistent signal detection across all 72 metric combinations with mean R² = 0. 93, revealing directionally-structured correlations consistent with CODE's 25-year PPP findings (p N-S) at 94% or higher level across modes and metrics (worst case 34/36 months), consistent with a persistent underlying effect. A critical audit indicates this is not an artifact of distance distribution: E-W pairs are actually 13 km longer than N-S pairs (bias against signal), and robust distance-matching strengthens the ratio (1. 033 → 1. 041). At full distances, raw λ ratios can appear suppressed by distance-dependent biases; a geometry-corrected comparison yields ratios of 1. 80–1. 86, within 17% of CODE's benchmark (2. 16). Key validations include: (1) orbital velocity coupling detected at 3. 2–5. 4σ (best: r = -0. 763), replicating CODE's 25-year finding (r = -0. 888), with signal persisting under ionospheric removal (best ionofree: r = -0. 416, 2. 5σ) ; (2) position jitter and clock bias show similar orbital coupling (Δ ≈ 5%), consistent with spacetime—not just temporal—modulation; (3) CMB frame alignment at RA = 188°, Dec = -5° (20. 0° from CMB dipole), matching CODE's benchmark (18. 2°), with Solar Apex disfavored (86. 5° separation) ; (4) geomagnetic stratification using real GFZ Kp data shows near-invariance at the primary threshold (Kp N-S in the ALLSTATIONS analysis, while higher-quality subsets motivate additional hemisphere-controlled falsification tests; (6) year-specific planetary event modulation detected (2. 8× above null, p < 0. 001 for all 6 metrics) with detection rates of 59–68% and no consistent tidal GM/r² scaling (σ-level vs GM/r²: p = 0. 317–0. 989), consistent with alignment-driven geometric coupling rather than a tidal forcing mechanism whose amplitude scales with planetary mass. This paper constitutes Paper 3 of the TEP-GNSS Research Series. Together with Paper 1 (multi-center validation) and Paper 2 (25-year temporal stability), these three complementary analyses—using different data sources, processing chains, and time periods—provide consistent evidence for planetary-scale, directionally-structured correlations in GNSS clock measurements. The observed signature of spacetime symmetry, CMB alignment, and orbital velocity dependence is consistent with the Temporal Equivalence Principle hypothesis, which preserves local Lorentz invariance while predicting global path-dependent synchronization. Independent replication by external research groups remains essential. Website: https: //mlsmawfield. com/tep/gnss-rinex/Code Availability: https: //github. com/matthewsmawfield/TEP-GNSS-RINEX DOI: 10. 5281/zenodo. 17860166 Keywords: temporal equivalence principle – GNSS – RINEX – atomic clocks – Single Point Positioning – spatial correlations – modified gravity Open Science Statement: This work is a preprint and is open to community review, ideas, and collaboration. All materials required for full reproducibility—including data downloads, analysis scripts, code, and manuscripts—are open-source. Feedback and contributions to further test these results are welcome.
Building similarity graph...
Analyzing shared references across papers
Loading...
Matthew Lukin Smawfield
Building similarity graph...
Analyzing shared references across papers
Loading...
Matthew Lukin Smawfield (Tue,) studied this question.
www.synapsesocial.com/papers/69f442fc967e944ac55665ec — DOI: https://doi.org/10.5281/zenodo.19886264