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REVIEW 3 major objections 5 minor 1 cited by

Impact of Weather on Satellite Communication: Evaluating Starlink Resilience

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Rain cuts Starlink median uplink throughput by 52% and downlink by 38%, while latency stays flat; cloud cover adds a further ~20% throughput penalty.

desk verdict First FHP terminal weather data near the Arctic, useful rain results, but the cloud-cover headline and abstract percentages need fixing before this is trustworthy. read the letter →

arxiv 2505.04772 v1 pith:5UVB3VPM submitted 2025-05-07 cs.ET cs.NIeess.SP

Muhammad Asad Ullah , Antti Heikkinen , Mikko Uitto , Antti Anttonen , Konstantin Mikhaylov This is my paper · ORCID
classification cs.ETcs.NIeess.SP
keywords StarlinkLEOsatellitecommunicationsweatherimpactrainattenuationcloudcoverthroughputmeasurementround-triptimeKu-band
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Low-Earth-orbit satellite internet is promoted as weather-tolerant, but this paper measures how far that tolerance goes for Starlink's flat high performance terminal, a fixed phased-array antenna rated for extreme weather, near the Arctic Circle. On five measurement days in Oulu, Finland, rain lowered median uplink throughput by 52.27% and median downlink throughput by 37.84%, while round-trip time stayed effectively unchanged. A linear regression over cloud-cover data shows throughput falling as cloud cover rises: skies up to 12.5% cloudy delivered roughly 20% higher throughput than 87.5% cloudy skies. The authors conclude that weather degrades Ku-band Starlink throughput substantially without breaking connectivity, since service availability stayed above 98.5% on both links during rain.

What carries the argument

The carrying object is a measurement campaign centered on Starlink's flat high performance (FHP) terminal, a fixed-installation phased-array antenna rated for extreme weather, mounted on a stationary vehicle. Throughput was measured with iPerf3 over TCP using ten parallel streams in both directions, RTT with ping, and weather with local stations reporting rain intensity, humidity, temperature, and cloud cover at one-minute resolution. The analysis pools results across five server locations because earlier work with the same hardware showed server distance had no material effect, and then compares weather classes or regresses throughput against cloud cover. This design lets the authors separate weather effects from network-path effects in a single geographic setting.

What would settle it

Repeat the cloud-cover comparison with a sky-facing camera or local cloud sensor mounted at the Starlink terminal over a month of variable cloudiness; if throughput does not decline with locally measured cloud fraction, the airport-based cloud regression is an artifact of distance or of the one afternoon. For the rain claim, bin the rain samples by rain rate at the terminal: if median uplink throughput does not drop by roughly half across light-to-moderate rain events, the headline 52% figure does not reproduce.

Watch

Extended reading notes

Core claim

The central claim is that weather attenuates Starlink's radio link asymmetrically. In rain, median uplink throughput falls from 20.9 to 10.5 Mbps (a 52.27% drop) and median downlink from 137 to 90.2 Mbps (a 37.84% drop), while median RTT remains essentially the same; moderate rain also produced a handful of one-second uplink outages. The cloud analysis adds a negative linear relationship between cloud fraction and throughput, with about 20% more throughput at up to 12.5% cloud cover than at 87.5%, and no visible RTT trend. The paper attributes these losses to Ku-band rain and cloud attenuation, and notes that most rain samples were light rain, so heavier rain would likely push throughput lower.

Load-bearing premise

The cloud-cover result stands or falls on whether cloud observations taken at an airport roughly 15 km from the terminal truly represented the sky above the terminal on that single measurement day.

Editorial extensions

If this is right

  • Users of Ku-band LEO services in rainy high-latitude regions should expect roughly a 40-50% throughput reduction during rain even with a premium weather-resistant terminal.
  • Because round-trip time stays flat through rain and clouds, latency-sensitive applications can continue during weather, but large transfers will slow unless a fallback link exists.
  • Service availability above 98.5% during the measured rain indicates that light-to-moderate rain is a soft throughput degradation rather than a hard outage.
  • A linear cloud-cover penalty of about 20% between nearly clear and nearly overcast skies means overcast weather alone meaningfully lowers achievable throughput.
  • Link budgets for future Ku-band LEO constellations should treat cloud cover as a continuous attenuation factor rather than a binary clear/rain condition.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the same data could test whether rain attenuation scales with elevation angle: at low angles the signal crosses more storm volume, so the 52% uplink drop should be larger when the satellite sits near the horizon, a check possible from satellite ephemeris data.
  • Beyond the paper, the cloud slope was measured on a single afternoon and is correlated with rising humidity, so a longer campaign with a local sky-facing sensor could separate cloud attenuation from humidity effects.
  • Beyond the paper, if this behavior holds across Ku-band LEO providers, weather-aware traffic shaping—pre-fetching during clear periods and throttling during rain—could smooth user experience without new infrastructure.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper reports a measurement study of a Starlink flat high performance (FHP) terminal located in Oulu, Finland, near the Arctic Circle. Using iPerf3, ping, and traceroute measurements collected on four days in June 2024, the authors compare TCP uplink and downlink throughput and round-trip time under clear, rainy, and cloudy conditions. The central claims are that rain degrades median uplink and downlink throughput by about 52% and 35--38%, respectively, that RTT is unaffected by rain and clouds, and that linear regression of throughput against cloud cover reveals a negative relationship, with roughly 20% higher throughput at cloud cover up to 12.5% than at 87.5% cover. The paper also documents one-second outages during moderate rain and reports service availability above 98.5%.

Significance. The study fills a gap in the literature by evaluating the Starlink FHP terminal at high latitudes using local weather-station data, and it provides a bidirectional TCP throughput and RTT comparison across weather conditions. The rain-degradation result is qualitatively supported by multiple measurement days and local rain-intensity data, and the RTT finding is consistent with prior work. The cloud-cover result is the weakest pillar: it rests on one measurement session, remote airport cloud data, and an undescribed regression. If the authors supply regression diagnostics and address confounds, the paper would be a useful empirical contribution; the weather-related quantitative claims are currently not reproducible as presented.

major comments (3)
  1. [Abstract and Section V-A] The abstract reports a median downlink degradation of 37.84%, while Section V-A and Fig. 6 state 35%; the uplink figures also differ slightly (52.27% vs. 52.2%). Because these percentages are headline quantitative claims, the authors must specify which data subsets and which baseline values are used for each percentage and make the numbers internally consistent.
  2. [Section V-B] The cloud-cover analysis rests on a single 148-minute session on June 26, 2024, and the described linear regression is not accompanied by a slope, intercept, R^2, confidence intervals, or p-values. Since cloud cover rises monotonically from near 0% to 87.5% over the session (Fig. 9), the decreasing throughput trend in Fig. 12 is confounded with time, satellite pass geometry, and network conditions. The abstract's claim of approximately 20% higher throughput at low cloud cover is therefore non-reproducible as presented; the authors should report full regression statistics and address time-based confounding, for example by controlling for time of day or collecting additional sessions.
  3. [Section IV-A] The assumption that cloud cover measured at FMI Oulu Airport, about 15 km from the client, represents conditions at the Starlink terminal is justified only by an unspecified 25 km spot-beam diameter and an unknown elevation angle. Ku-band attenuation depends on the cloud water content along the slanted terminal-to-satellite path, not on ground-level okta at a remote airport, so this assumption is not sufficient. The authors should provide empirical validation (e.g., comparison with co-located cloud observations) or explicitly downgrade the cloud-cover analysis to a preliminary observation.
minor comments (5)
  1. [Fig. 4 caption] The caption describes an upper green plot and a lower red plot, but the color and position descriptions appear inconsistent with the displayed panels; please clarify which panel corresponds to June 18 and which to June 27.
  2. [Section IV-B] The sentence 'Unlike iperf3 throughput, we separately examine the RTT for these two servers' is missing a period before the following sentence beginning 'We conducted...'.
  3. [Section V-A] Minor typographical issues include 'ten minutes internals' and 'drizzing'; the abstract also contains 'clouds cover' in places. A proofreading pass is recommended.
  4. [Section V-B and Fig. 11] The timing diagram in Fig. 11 should be reconciled with the reported sample counts (3,690 uplink, 3,690 downlink, and 492 RTT samples) so that readers can verify that the repetitions and durations are consistent.
  5. [Section VI] The conclusion's statement that moderate to heavy rainfall may contribute to higher RTT is presented as an expectation rather than a measured result; please mark it clearly as a hypothesis for future work.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the measurement-derived rain and cloud results are not fitted inputs or self-citation reductions.

full rationale

The paper is a measurement campaign rather than a derivation chain. The rain result is a direct comparison of measured median throughput across weather labels (clear versus rain), and the cloud result is a descriptive linear regression of measured throughput on measured cloud cover; no parameter is fitted to one subset and then reused to predict a closely related quantity. The cloud-cover comparison of 'up to 12.5%' versus '87.5%' is a summary of the same measured samples, not a prediction generated from a fitted model. The only self-citation, reference [15] by the same authors, is used to justify aggregating iPerf3 results across server locations and to refer to a previously described hardware setup. This is a supporting methodological claim, not the source of the weather findings, and the weather results are not defined in terms of that claim. The cloud analysis rests on one day, on cloud data from an airport about 15 km away, and on a regression without reported statistics; these are reproducibility and correctness concerns, not circular reasoning. No step in the paper reduces by construction to its own inputs, so the circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities. Its quantitative claims rest on descriptive statistics and a single unreported linear regression; the main assumptions are about the representativeness of remote weather stations and the comparability of measurement days.

free parameters (2)
  • Cloud cover linear regression slope and intercept = not reported
    Used in Section V-B to claim a negative relationship between throughput and cloud cover; slope and intercept are fitted to the June 26, 2024 samples but not reported.
  • Cloud cover thresholds (12.5% and 87.5%) = 12.5%, 87.5%
    Chosen post hoc as the observed extremes of cloud cover on June 26; the 20% throughput difference is computed between these two bins, not from a pre-specified hypothesis.
assumptions (4)
  • domain assumption Weather conditions at FMI Oulu Airport, 15 km away, match conditions at the client location
    Cloud cover is only available from the airport; the paper assumes similarity based on a 25 km spot beam diameter and cross-checking of three datasets (Section IV-A).
  • ad hoc to paper Starlink spot beam diameter is about 25 km
    Invoked without citation in Section IV-A to justify uniform weather across the terminal and weather stations.
  • domain assumption Server location has no effect on throughput
    Adopted from the authors' prior study [15] to aggregate iPerf3 results across five servers; not re-verified on this dataset.
  • domain assumption iPerf3 with ten parallel TCP streams measures maximum achievable throughput
    Standard measurement practice used in prior Starlink studies; assumes the public Internet path and Starlink link are not rate-limited elsewhere.

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Cite this review

Pith. "Pith review of Impact of Weather on Satellite Communication: Evaluating Starlink Resilience." pith.science (2026). https://pith.science/paper/5UVB3VPM

@misc{pith2026250504772,
  author       = {Pith},
  title        = {Pith review of: Impact of Weather on Satellite Communication: Evaluating Starlink Resilience},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5UVB3VPM}},
  note         = {Machine review of arXiv:2505.04772}
}
read the original abstract

Satellite communications have emerged as one of the most feasible solutions to provide global wireless coverage and connect the unconnected. Starlink dominates the market with over 7,000 operational satellites in low Earth orbit (LEO) and offers global high-speed and low-latency Internet service for stationary and mobile use cases, including in-motion connectivity for vehicles, vessels, and aircraft. Starlink terminals are designed to handle extreme weather conditions. Starlink recommends a flat high performance (FHP) terminal for users living in areas with extreme weather conditions. The earlier studies evaluated Starlink's FHP throughput for stationary and in-motion users without providing a detailed analysis of how weather affects its performance. There remains a need to investigate the impact of weather on FHP's throughput. In this paper, we address this shortcoming by analyzing the impact of weather on Starlink's performance in Oulu, Finland, a city located in Northern Europe near the Arctic Circle. Our measurements reveal that rain degrades median uplink and downlink throughput by 52.27% and 37.84%, respectively. On the contrary, there was no noticeable impact on the round-trip time. Additionally, we also examine the impact of cloud cover on the Starlink throughput. The linear regression analysis reveals the negative relationship between throughput and cloud cover. The cloud cover of up to 12.5% has around 20% greater throughput than the cloud cover of 87.5%

Figures

Figures reproduced from arXiv: 2505.04772 by the authors.

Figure 1
Figure 1. Total annual precipitation (rain and snow) calculated as the sum of [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Experimental setup illustration, where flat high performance terminal [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Location of the measurement (client) and the weather stations. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Comparison of rain intensity and relative humidity for June 18 and [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Throughput observation for different weather conditions including [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 9
Figure 9. Figure 9: Percentage of the cloud cover during the June 26, 2024, measurements. [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]
Figure 10
Figure 10. Figure 10: Impact of cloud cover on temperature and relative humidity during [PITH_FULL_IMAGE:figures/full_fig_p006_10.png]
Figure 12
Figure 12. Figure 12: Throughput and RTT from an iPerf3 client in Oulu to an iPerf3 server [PITH_FULL_IMAGE:figures/full_fig_p007_12.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Starlink Robot: A Platform and Dataset for Mobile Satellite Communication

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A wheeled robot with a Starlink terminal and multi-modal sensors produces the first synchronized dataset linking mobile satellite communication performance to motion and environmental occlusion.

Reference graph

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