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A Catalog of Dispersive Ionospheric and Plasmaspheric Arcs from Cislunar and Lunar-surface GNSS Radio Occultations
Francesco Fiorina, Alex Minetto, Rebecca Ghidoni, Marco Guerra, Claudio Cesaroni, Luca Spogli, Andrea Nardin, Fabio Dovis
TL;DR
Radio occultation and ground sounding observations sample only a limited portion of the grazing-altitude and geometry range needed to characterize the ionosphere and plasmasphere. This paper catalogs 263 LuGRE occultation arcs from cislunar space and the lunar surface, finding an 11.4% disagreement between geometric validation and classification.
Problem
Ground-based and radio-occultation observations sample only a limited portion of the grazing-altitude and geometry range relevant to ionospheric and plasmaspheric characterization.
Source: Background & Summary · p. 2Method
The catalog algorithm detects, characterizes, and classifies LuGRE arcs using dual-frequency observations, treating each arc as the combined ionosphere–plasmasphere signature.
Source: Background & Summary · pp. 2–3Results
263 arcs comprise the catalog, and geometric validation disagrees with classification for 30 arcs, an 11.4% disagreement rate.
Source: Background & Summary · p. 3, Usage Notes · p. 13Takeaways & Limitations
The arc-level metadata and time series support statistical studies and targeted analyses of ionospheric and plasmaspheric effects observed from cislunar space.
Source: Background & Summary · p. 3Sidelobe arcs at high grazing altitudes may be primarily plasmaspheric, but their poorly characterized observations may lead to misleading classifications and warrant caution.
Source: Usage Notes · p. 13
Abstract
from arXiv · showhide
This dataset is a catalog of 263 dispersive arcs induced by ionospheric and plasmaspheric occultation observed in GNSS signals recorded by the Lunar GNSS Receiver Experiment (LuGRE) payload during Blue Ghost Mission 1 (BGM1), in cislunar space and on the lunar surface. Occultation arcs are identified and classified as ingress, egress or tangent by combining independently GPS and Galileo dual-frequency observables and computing the rate of the total electron content (ROT) and ROT index (ROTI), and are validated through a geometric criterion based on the grazing altitude of the tangent point. No distinction is made between the ionospheric and plasmaspheric contributions to each arc, which is treated as a single dispersive signature. For each arc, the catalog provides timing, grazing altitude, summary ROT/ROTI/$\Delta$STEC attributes, the geomagnetic activity level, and time series of leveled slant total electron content (STEC), not corrected for instrumental biases, ROT, ROTI, tangent-point position and local time. The HDF5 (Hierarchical Data Format version 5) file contains a header with dataset-wide metadata and per-class statistics, and one entry per arc. It enables rapid selection of occultation arcs for statistical studies and targeted analyses, and complements ground-based and low Earth orbit radio-occultation data during the declining phase of solar cycle 25.
Background & Summary
LuGRE provided a complementary cislunar vantage point, recording GNSS signals with tangent-point altitudes from 0 to 18,000 km across four mission phases. The catalog identifies 263 occultation arcs as signatures of the combined ionosphere–plasmasphere system and provides metadata and time series for their study.
Background & Summary
- Across commissioning, transit, low lunar orbit, and lunar-surface phases, LuGRE observed tangent-point altitudes spanning 0–18,000 km.This complements observations from ground-based and low Earth orbit experiments, which sample only a limited portion of the altitude and geometry range.Source: Background & Summary · p. 2
- Each arc represents the combined ionosphere–plasmasphere signal because dual-frequency observations measure electron content integrated along the whole ray path.The catalog algorithm detects, characterizes, and classifies arcs across all four mission phases.Source: Background & Summary · p. 2
- 263 arcs are cataloged with timing, grazing altitude, Kp and G-scale labels, ROT/ROTI indices, and associated time series.The HDF5 catalog supports selection by metadata and retrieval of selected arc time series for statistical or targeted analyses.Source: Background & Summary · p. 3
Methods
The catalog is produced by processing LuGRE dual-frequency observables and ancillary reference data through parsing, preprocessing, TEC-index computation, and arc classification. Its inputs include GPS and Galileo observations and mission, position, orbit, and geomagnetic reference data.
Methods
- The catalog characterizes arcs using TEC-derived ROT and ROTI, which identify the timespan of dispersive effects, within a pipeline from raw observables to arc identification and classification.The workflow also estimates STEC and geometrically validates classified arcs.Source: Methods · pp. 3–4, Figure 2 · p. 4
Input data
- GPS and Galileo L1/E1 and L5/E5a observables were recorded at 1 Hz; 20 RTP operations were processed independently after OP3_0 was excluded.OP3_0 was discarded because only one satellite signal was visible and tracked for less than 100 seconds.Source: Input data · p. 4
LuGRE Data parsing
- LuNART decodes each RTP telemetry file into common-grid time series of C/N0, pseudorange, and carrier-phase observables in a per-operation MATLAB structure.These parsed measurements feed the subsequent processing steps.Source: LuGRE Data parsing · p. 5
Input data pre-processing
Preprocessing flags cycle slips and degraded-quality measurements, then constructs an arc-validity mask that preserves continuity across single-frequency gaps while retaining epochs with dual-frequency data.
Input data pre-processing
- Cycle slips detected with a Grubbs outlier test on code-minus-carrier series trigger leveling-filter reinitialization and exclusion of corrupted ROT estimates.The flagged epochs are treated in later processing.Source: Input data pre-processing · p. 5
- Measurements with C/N0 below 20 dB-Hz are excluded to prevent degraded tracking conditions from adding noise to derived quantities.The mask is applied to both pseudorange and carrier-phase measurements.Source: Input data pre-processing · p. 5
- The validity mask requires dual-frequency availability at the first and last retained epochs, while single-frequency gaps within the interval do not interrupt visibility.ROTI is interpolated across internal gaps only at the end of processing, preventing early interpolation artifacts from propagating and avoiding false arc boundaries.Source: Input data pre-processing · p. 5
Total electron content and derived parameters
Dual-frequency GNSS code and carrier-phase measurements provide STEC estimates, which are leveled and differentiated to derive ROT and ROTI for characterizing the dispersive signal. The method uses a 30-s ROTI window, so comparisons with values from commonly used 15-min windows require care.
STEC and carrier-phase leveling
- STEC is estimated from dual-frequency GNSS measurements, with code-derived values absolute but noisy and phase-derived values more precise but biased by unresolved ambiguity.A recursive carrier-phase leveling method tracks the code-minus-phase offset and combines the estimates; the filter is reinitialized at dataset or valid-data starts, after cycle slips, and after sufficiently long gaps.Source: Total electron content and derived… · p. 6
ROT and ROTI
- ROT is computed by a three-point central finite difference of phase-derived STEC, removing constant carrier-phase ambiguity and instrumental biases.First-order forward and backward differences are used at segment endpoints; ROT estimates within one epoch of a flagged cycle slip are discarded because slips create spikes that propagate into ROTI.Source: Total electron content and derived… · pp. 6–7
- ROTI describes ROT variability as its standard deviation over a sliding window, with a 30-s window selected to balance estimate stability and spatial extent.ROTI is expressed in TECU/s; its 30-s window is shorter than the 15-min windows typically used with geodetic receivers, so direct literature comparisons should account for the difference.Source: Total electron content and derived… · p. 7
- The catalog retains original ROTI values while Akima-interpolated gaps are used only to preserve continuity during arc classification.Interpolated values are not used quantitatively or included in the catalog.Source: Total electron content and derived… · p. 7
Arcs identification and classification
The method segments ROTI series, extracts arc features, groups nearby segments, and classifies candidate arcs using ROT and ROTI behavior. Ingress, egress, and tangent labels reflect the sign and temporal pattern of these measures, with consecutive-arc checks and geometric validation supporting the classification.
Arcs identification and classification
- Dispersive arcs are identified through segmentation, feature extraction, and grouping: restored ROTI series are segmented into contiguous candidate intervals, while features are computed on original, non-interpolated ROT and ROTI series.Consecutive candidate arcs with Tg < 300 s are grouped; otherwise, they are treated as distinct arcs.Source: Arcs identification and classification · pp. 7–8
Ingress
- Ingress candidates have median ROT above δ = 0.007 TECU/s, with ROTI minimum marking onset and its maximum expected near the arc end.The positive ROT condition is necessary but not sufficient; the arc is trimmed so its start coincides with the ROTI minimum.Source: Ingress · p. 8, Arcs identification and classification · p. 8
Egress
- Egress arcs have median ROT below −δ, with ROTI maximum near the beginning and the ROTI minimum setting the arc end.The decreasing plasma contribution produces the opposite ROT behavior from ingress.Source: Egress · p. 9
Tangent
- Tangent arcs have near-zero median ROT as ingress and egress contributions largely cancel, while the ROTI minimum lies inside the arc and its maximum may be at an edge or central cusp.A hard threshold can leave ROT and ROTI criteria inconsistent; fallback labels based only on median ROT should be treated cautiously. Consecutive labels are also checked against expected ingress-egress chronology and occultation geometry.Source: Tangent · p. 9
Data Records
The HDF5 catalog organizes dataset-wide metadata and per-arc records in a two-level structure, with identifiers, statistics, descriptive fields, and time series supporting arc-level access.
Data Records
- The HDF5 file has a Header for dataset-wide metadata and summary statistics and a Body with one entry for each identified occultation arc.Figure 3 distinguishes root attributes, per-type arc statistics, and per-arc descriptive fields and time series.Source: Data Records · p. 9, Figure 3 · p. 10
Arc identifier
- Each arc’s composite identifier encodes a unique number, arc type, satellite ID, and operation ID, which are also stored as separate fields.Source: Arc identifier · p. 10
Header
- The header stores dataset-wide attributes and summary statistics computed independently for each arc type.Source: Header · p. 10, Table 2 · p. 11
Body
- Each Body entry contains scalar attributes and associated time series; Figure 3 specifies 20 descriptive attributes and 13 time series.Source: Body · p. 10, Figure 3 · p. 10
- Stored time series span the full satellite tracking arc rather than only the identified arc interval, and may contain gaps when either frequency’s data are missing.The tracking interval is ro_utc_start to ro_utc_end; the identified arc interval is arc_utc_start to arc_utc_end.Source: Body · pp. 10–11
- Table 5 reports arc counts, within-group percentages, and mean durations for the full catalog and by constellation and mission phase.Source: Body · p. 11
Technical Validation
Geometric validation reconstructs the GNSS line of sight and tangent point, then compares grazing-altitude evolution with the proposed arc classification. The validation neglects ionospheric ray bending.
Technical Validation
- The validation reconstructs the satellite-to-receiver line of sight using transmission-time satellite positions, receiver positions, and a Sagnac correction for Earth rotation.The closest approach of the ray to Earth’s center gives the tangent point, which is converted from ECEF to geodetic coordinates using WGS84.Source: Technical Validation · pp. 11–12
- Ingress and egress are distinguished by the sign of the median grazing-altitude time derivative relative to a 1000 m/s threshold; tangent arcs have an interior minimum.Ingress uses a negative derivative below −γ, while egress uses a positive derivative above γ.Source: Technical Validation · p. 13
- The geometric check compares its classification with the proposed method to identify arcs warranting specific analysis.Its straight-line path approximation neglects ionospheric ray bending.Source: Technical Validation · p. 13
Usage Notes
Sidelobe arcs at high grazing altitudes may be dominated by plasmaspheric effects, and the catalog does not distinguish them from main-lobe arcs. Geometric validation disagrees with the catalog classification for 30 of 263 arcs.
Usage Notes
- Sidelobe observations at high grazing altitudes may reflect primarily plasmaspheric dispersive effects and can lead to misleading classifications.The catalog does not distinguish sidelobe from main-lobe arcs; sidelobe observations are poorly characterized in the literature and warrant caution.Source: Usage Notes · p. 13
- Geometric validation disagrees with classification for 30 of 263 arcs (11.4%); these cases are flagged in ro_status, which users should inspect or exclude depending on the intended application.Source: Usage Notes · pp. 13–14
- At observation-window edges, partial-arc ROT and a 30-s ROTI window can become poorly informative; dual-frequency tracking gaps also make both series intermittent.When ROT and ROTI are inconsistent, classification falls back to thresholded ROT, which is sensitive to edge effects and other non-idealities.Source: Usage Notes · pp. 13–14
Data Availability
The catalog is available as version v1.0.0 on Zenodo and is linked to the CLIP-ON community.
Data Availability
- The catalog is deposited as version v1.0.0 on Zenodo and linked to the CLIP-ON community, which collects LuGRE and future cislunar-mission space-climatology contributions.The repository is accessible under the catalog’s name.Source: Data Availability · p. 14
Code Availability
The dataset creation code is available on Zenodo and is written for MATLAB.
Code Availability
- Dataset creation code is available on Zenodo, was written in MATLAB 2022b, and is suggested to be run with that version to avoid compatibility problems.A README describing code usage is included in the repository.Source: Code Availability · p. 14
Funding
Dataset creation was funded under contract n. 2021-26-HH.0 between Agenzia Spaziale Italiana and Politecnico di Torino.
Funding
- Contract n. 2021-26-HH.0 between Agenzia Spaziale Italiana and Politecnico di Torino funded the dataset’s creation.The contract covered research and development related to GNSS navigation in the Earth–Moon space volume within the Lunar GNSS Receiver Experiment.Source: Funding · p. 14