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arxiv: 2606.01724 · v1 · pith:TG5VFIZJnew · submitted 2026-06-01 · 📊 stat.AP · cs.NI

Mapping the Storm: Geospatial Impacts of Severe Weather on LEO Network Performance

Sina Ehsani , Bhanu Pallakonda , Pragyana K. Mishra This is my paper

Pith reviewed 2026-06-28 12:11 UTC · model grok-4.3

classification 📊 stat.AP cs.NI
keywords LEO satellite networksStarlinksevere weathergeospatial analysisnetwork performanceweather impacttelemetry datasatellite internet
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The pith

Severe weather like thunderstorms degrades Starlink performance for more than 55 percent of affected terminals, with outages lasting minutes to hours.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how localized weather affects LEO satellite networks such as Starlink across the contiguous United States. It draws on minute-level telemetry from 1,292 terminals spanning more than 870,000 terminal hours and aligns that data with high-resolution weather observations through spatial joins. The analysis shows that severe events, including thunderstorms with heavy rain or snow, produce substantial KPI degradation in over 55 percent of impacted terminals and can sustain impairments or full outages for minutes to hours. This establishes the first large-scale empirical link between fine-grained weather data and LEO internet performance in both space and time.

Core claim

Severe weather events such as thunderstorms with heavy rain or snow have a pronounced effect on network performance; more than 55% affected terminals experienced substantial degradation, with sustained impairments or full service outages lasting from several minutes to multiple hours.

What carries the argument

Spatial join techniques that time-align terminal telemetry with classified high-resolution weather events to measure KPI changes.

If this is right

  • Weather-aware provisioning can reduce service variability in LEO networks.
  • Geospatial predictive models can incorporate weather-inferred performance data.
  • Resilient system design can account for outage durations from minutes to hours.
  • Service-level forecasting tools can integrate localized meteorological inputs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same spatial-join approach could be applied to other LEO constellations to test consistency of weather sensitivity.
  • Network operators might use these patterns to prioritize backup capacity in high-risk weather zones.
  • Regulatory benchmarks for broadband reliability could include weather-adjusted outage metrics.

Load-bearing premise

The 1,292 terminals and their telemetry provide a representative sample of network behavior under weather events, and spatial joins accurately align locations without significant misclassification.

What would settle it

A large independent sample of terminals during documented severe weather events showing degradation in substantially fewer than 55 percent of cases or no sustained outages beyond a few minutes.

Figures

Figures reproduced from arXiv: 2606.01724 by Bhanu Pallakonda, Pragyana K. Mishra, Sina Ehsani.

Figure 1
Figure 1. Figure 1: Global distribution of a subset of Starlink terminals managed by Armada Edge Platform (AEP): average latency [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The geographic distribution of the 1,292 Starlink [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Pearson correlation matrix between key Starlink [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: These quantitative results support the statistical robust [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Percentage of hourly records with significant per [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Case Study 1 (Minute-Level Detail): Minute-level [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
read the original abstract

LEO satellite constellations, led by deployments such as Starlink, are playing an increasingly pivotal role in enabling global broadband connectivity. However, the reliability and performance of these space-based networks are highly sensitive to environmental dynamics, particularly localized weather phenomena that exhibit strong spatio-temporal variability. In this study, we present a continental-scale geospatial analysis of weather-induced performance degradation in the Starlink LEO network, with a focus on the contiguous United States. Leveraging a unique dataset comprising more than 870,000 terminal hours of minute-level telemetry from 1,292 Starlink terminals, we integrate high-resolution localized weather observations to quantify the impact of various meteorological conditions. We evaluated key performance indicators (KPIs)-including ping latency, ping drop rate, and signal quality-using spatial join techniques and time-aligned correlation with classified weather events. Our analysis reveals that severe weather events, such as thunderstorms with heavy rain or snow, have a pronounced effect on network performance. In particular, more than 55% affected terminals experienced substantial degradation. Temporal continuity analysis at the minute level shows that such degradation can lead to sustained impairments or full service outages lasting from several minutes to multiple hours.This work contributes to the first large-scale empirical study linking LEO satellite Internet performance with fine-grained weather data in both space and time. Our findings offer actionable insights for geospatial predictive modeling, weather-aware network provisioning, and resilient satellite communication system design. We also propose a framework for incorporating weather-inferred performance variability into future geospatial planning and service-level forecasting tools for LEO-based Internet systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper conducts a continental-scale geospatial analysis of weather-induced performance degradation in the Starlink LEO network using minute-level telemetry from 1,292 terminals (870,000 terminal-hours) in the contiguous US. It integrates localized weather observations via spatial joins and time-aligned correlation to evaluate KPIs (ping latency, drop rate, signal quality), reporting that severe weather events such as thunderstorms with heavy rain or snow cause substantial degradation in more than 55% of affected terminals, with sustained impairments or outages lasting minutes to hours. The work positions itself as the first large-scale empirical study linking LEO performance to fine-grained weather data and proposes a framework for weather-aware modeling.

Significance. If the central observational correlations hold after methodological validation, the study would provide a valuable large-scale empirical dataset and framework for geospatial predictive modeling of LEO network resilience, with direct implications for weather-aware provisioning and system design. The scale of the telemetry corpus is a strength, but the absence of reported statistical methods or validation steps limits the current contribution to descriptive correlation rather than robust attribution.

major comments (2)
  1. [Abstract] Abstract: The headline claim that 'more than 55% affected terminals experienced substantial degradation' during thunderstorms/heavy rain or snow supplies no statistical methods, error bars, sample selection criteria, or validation steps. This prevents assessment of whether the fraction is robust or sensitive to confounding factors, directly undermining the central empirical result.
  2. [Abstract] Abstract (spatial join description): The integration of terminal telemetry with weather observations via 'spatial join techniques' does not report the native resolution or source of the weather data, the matching tolerance, or any sensitivity analysis for misclassification (e.g., near event boundaries or when weather grids exceed GPS precision). This is load-bearing for attributing KPI changes to weather rather than other factors and for the 55% degradation figure.
minor comments (1)
  1. [Abstract] The abstract states 'more than 55% affected terminals' but does not define the baseline for 'affected' or the exact degradation threshold used for KPIs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight opportunities to strengthen the presentation of our empirical results. We address each major comment point by point below and will incorporate the suggested clarifications into a revised manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline claim that 'more than 55% affected terminals experienced substantial degradation' during thunderstorms/heavy rain or snow supplies no statistical methods, error bars, sample selection criteria, or validation steps. This prevents assessment of whether the fraction is robust or sensitive to confounding factors, directly undermining the central empirical result.

    Authors: We agree that the abstract, as a concise summary, should provide more context for the central 55% claim to allow readers to assess its robustness. The figure derives from a direct enumeration of terminals meeting explicit degradation criteria (latency increase exceeding 50% or drop rate above 10%) during time-aligned severe weather periods, using the full set of terminals with overlapping weather observations as the sample. We will revise the abstract to briefly state the sample selection and degradation definition, and we will add error bars or bootstrap-derived confidence intervals to the corresponding results in the main text along with a short description of the correlation procedure. This addresses the concern about sensitivity to confounding factors. revision: yes

  2. Referee: [Abstract] Abstract (spatial join description): The integration of terminal telemetry with weather observations via 'spatial join techniques' does not report the native resolution or source of the weather data, the matching tolerance, or any sensitivity analysis for misclassification (e.g., near event boundaries or when weather grids exceed GPS precision). This is load-bearing for attributing KPI changes to weather rather than other factors and for the 55% degradation figure.

    Authors: We acknowledge that the abstract omits key technical parameters of the spatial integration step. We will expand the abstract to specify the weather data source and native resolution, the spatial matching tolerance applied, and the time-alignment window. In addition, we will add a methods subsection (or appendix) that includes sensitivity checks for boundary effects and grid-precision mismatches. These additions will clarify the attribution of KPI changes to weather and support the robustness of the reported degradation fraction. revision: yes

Circularity Check

0 steps flagged

No circularity: purely observational correlation study with no derivation chain

full rationale

The paper reports empirical correlations between minute-level Starlink telemetry (1,292 terminals, 870k terminal-hours) and classified weather events via spatial joins and time alignment. No equations, models, predictions, or first-principles derivations are present that could reduce to inputs by construction. The >55% degradation statistic is a direct count from the joined dataset; the spatial-join accuracy is an unverified assumption but does not constitute circularity under the defined patterns. The work is self-contained as an observational analysis.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no identifiable free parameters, axioms, or invented entities; all claims rest on the unexamined representativeness of the terminal sample and weather classification accuracy.

pith-pipeline@v0.9.1-grok · 5817 in / 1001 out tokens · 20490 ms · 2026-06-28T12:11:44.032878+00:00 · methodology

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