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the claim
Hurricane wind speeds can be estimated using atmospheric pressure and other meteorological factors.
the verdict
SUPPORTED
the evidence backs this
refutedsupported
the weight of evidence
4 sources for · 0 against

Multiple meteorological studies confirm that hurricane wind speeds and central pressures are closely linked and can be estimated or modeled using barometric pressure and other storm characteristics.

Evidence for · 4
2022 · cited by 8
Paper 5 demonstrates that atmospheric pressure filling and decay of tropical cyclones can be physically modeled using wind and pressure variables.
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The analysis

The claim states that hurricane wind speeds can be estimated using atmospheric pressure and other meteorological factors. Papers 5, 6, 7, and 9 all directly discuss and utilize the physical and empirical relationships between hurricane wind speeds, central or environmental pressure, storm size, and other meteorological factors to estimate storm intensity or pressure deficits. Therefore, the claim is fully supported by the retrieved evidence.

More for · 3
2024 · cited by 5
Paper 6 examines the relationship between minimum sea level pressure and maximum wind speed in tropical cyclones.
2010 · cited by 4
Paper 7 uses wind-pressure relationships and central pressure measurements to re-evaluate the maximum winds of historical tropical cyclones.
2024 · cited by 3
Paper 9 develops an empirical model for predicting minimum central pressure and hurricane intensity using wind speeds, storm size, and environmental pressure.
Everything we examined (12)
We also searched for evidence AGAINST this claim, not only for it.
  1. Climatic and meteorological exposure and mental and behavioral health: A systematic review and meta-analysis.peer-reviewedno side takennot shown: read and judged not to bear on this claim
  2. Hurricane Intensity Predictabilitypeer-reviewedno side takennot shown: read and judged not to bear on this claim
  3. Hurricane maximum potential intensity equilibriumpeer-reviewedno side takennot shown: read and judged not to bear on this claim
  4. Deep Learning–Based Summertime Turbulence Intensity Estimation Using Satellite Observationspeer-reviewedno side takennot shown: read and judged not to bear on this claim
  5. A general pattern of trade-offs between ecosystem resistance and resilience to tropical cyclones.peer-reviewedno side takennot shown: read and judged not to bear on this claim
  6. A physical model of tropical cyclone central pressure filling at landfallpeer-reviewedsupports
  7. A Numerical Study of Tropical Cyclone and Ocean Responses to Air‐Sea Momentum Flux at High Windspeer-reviewedsupports
  8. The benefit of hindsight: re-examining the maximum winds during tropical cyclone Tracypeer-reviewedsupports
  9. Leveraging SHapley Additive exPlanations (SHAP) and fuzzy logic for efficient rainfall forecasts.peer-reviewedno side takennot shown: read and judged not to bear on this claim
  10. A simple model for predicting tropical cyclone minimum central pressure from intensity and sizepeer-reviewedsupports
  11. Evaluating the impact of climate change on hurricane wind risk: A machine learning approach.peer-reviewedno side takennot shown: read and judged not to bear on this claim
  12. A global dataset of synthetic tropical cyclone tracks for the El Niño‒Southern Oscillation.peer-reviewedno side takennot shown: read and judged not to bear on this claim
The paper trail · every fact has a biography
first checked06 Aug 2026
judged → SUPPORTED · 8106 Aug 2026
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