SBAS Ionospheric Threat — Empirical Evidence

Scope status

This note translates GNSS-RO empirical findings over Indonesia into SBAS ionospheric integrity language. It is a bridge note between ionospheric science and SBAS system design, not a design specification.

Boundary:

• It does not prescribe operational GIVE values, protection-level equations, or alert-limit values.
• It does not claim that the sampled data is sufficient for certification or service definition.
• It frames empirical observations as threat-discovery inputs for pre-operational SBAS assessment.
• It must be read through SBAS Corrections and Integrity Separation, SBAS Service Performance Concepts, and SBAS Operational Validation Dashboard before being used in implementation planning.

Threat categories from Indonesian GNSS-RO data

Threat 1: Large absolute delay tails

Statistic	TECU	L1 equivalent (m)
Median	46.9	7.6
p95	166.0	26.9
p99	341.1	55.4
Maximum observed	468.4	76.1

SBAS interpretation: The observed upper-tail delays are important threat-discovery signals for low-latitude SBAS research. This page does not compare them with any specific operational SBAS assumption unless a directly extracted service-design source is added.

Threat 2: Spatial gradients

Statistic	Equivalent L1 gradient (mm/km)
Median	6.6
p95	46.7
p99	81.9
Maximum	208.3

SBAS interpretation: Large-gradient observations are threat-discovery signals for differential-correction and integrity analysis. Any comparison with an operational service must be sourced from that service’s own design, performance, or validation evidence.

Threat 3: Undersampled risk windows

Risk window	Profiles available
Post-sunset 18–21 LT	0
Storm/disturbed class (V3)	23 (possibly coincidental sampling)

SBAS interpretation: An integrity threat model built only on available data would systematically underestimate post-sunset risk. This is not a finding of low risk — it is a finding of insufficient data. Operational SBAS certification requires deliberate acquisition of these windows.

Threat 4: Model underestimate bias

Metric	Value
IRI-2020 systematic bias	66.72 TECU (10.8 m L1)
RMSE	94.34 TECU (15.3 m L1)

SBAS interpretation: If an SBAS ionospheric model performs similarly to IRI-2020 in this region, broadcast corrections would systematically understate actual delay. This inflates the residual error budget and can compromise integrity if the GIVE does not overbound the true model error.

Threat-budget scaffold (pre-operational)

The V3 paper introduced a pre-operational SBAS threat-budget separation:

Component	Source	Current trust level
Observed delay-tail proxy	GNSS-RO	Moderate (sparse temporal sampling)
Leave-day-out temporal-transfer residual	Robust model	Moderate (R² = 0.44)
Spatial-gradient allowance	Pairwise RO proxy	Low (not IPP-domain)
Post-sunset coverage penalty	Missing data	Must be explicitly penalized
Storm-time extrapolation penalty	Limited storm samples	Must be explicitly penalized

Key boundary: Convert this scaffold into candidate GIVE-like quantization only after post-sunset and storm gaps are closed with additional data and ground GNSS/scintillation validation.

Relationship to the vault

Parent domain

• IRI-2020 vs GNSS-RO Indonesia — empirical results being translated
• SBAS Integrity — integrity concepts receiving empirical input
• ASEAN SBAS Deployment Barriers — barriers strengthened by empirical findings

Sibling domain

• Indonesian SBAS ION Paper Iterations — research evolution tracking
• Source - Equatorial Ionosphere and SBAS Feasibility — theoretical scaffold now receiving empirical anchor

Upstream notes

• GNSS Radio Occultation — technique
• Total Electron Content (TEC) — observable
• Ionospheric Model Validation — methodology

What this note does NOT claim

• That GNSS-RO can directly compute operational SBAS GIVE values
• That the sampled days provide sufficient coverage for certification
• That Indonesia is “worse” than other equatorial regions (no comparative data)
• That the threat budget is ready for operational use

Source-routing boundary

Use this note only as empirical threat-discovery evidence. Do not use it as:

• an operational SBAS ionospheric correction model;
• an approved GIVE or grid-definition source;
• a service-volume or availability source;
• a procedure-approval source;
• a replacement for ground-monitoring, service-design, or regulator evidence.

Routing path:

Empirical ionosphere evidence
  -> threat discovery / research prioritization
  -> service-design validation question
  -> operational validation only after official service/regulator evidence

Recommended operational-design implications

1. Do not import another region’s ionospheric assumptions without source support for Indonesia or ASEAN.
2. Stratify threat assessment by IGRF dip-latitude region, local-time window, and space-weather state.
3. Combine RO with ground GNSS/scintillation before attempting service-design validation; RO alone is insufficient.
4. Deliberately acquire post-sunset and storm data before deriving operational candidate bins.
5. Use RO as threat-discovery evidence and ground/service/regulator sources for operational validation.

See also

• IRI-2020 vs GNSS-RO Indonesia
• ASEAN SBAS Deployment Barriers
• SBAS Integrity
• SBAS Corrections and Integrity Separation
• SBAS Service Performance Concepts
• SBAS Operational Validation Dashboard
• GNSS Radio Occultation
• Total Electron Content (TEC)
• Ionospheric Model Validation
• Indonesian SBAS ION Paper Iterations
• Source - Equatorial Ionosphere and SBAS Feasibility
• Source - GNSS-RO Indonesia Empirical Study
• Indonesia