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Fluid dynamics is the study of fluids in motion. Velocity vectors are often used to illustrate fluid motion in applications like meteorology. For example, wind—the fluid motion of air in the atmosphere—can be represented by vectors indicating the speed and direction of the wind at any given point on a map. Another method for representing fluid motion is a streamline. A streamline represents the path of a small volume of fluid as it flows. When the flow pattern changes with time, the streamlines...
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Related Experiment Video

Updated: May 8, 2026

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
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The Imperial College Storm Model (IRIS) Dataset.

Nathan Sparks1, Ralf Toumi2

  • 1Imperial College London, Department of Physics, London, SW7 2AZ, UK. n.sparks@imperial.ac.uk.

Scientific Data
|April 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces IRIS, a new synthetic tropical cyclone (TC) model. IRIS generates realistic global wind speed data, improving tropical cyclone risk assessment with minimized bias.

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Area of Science:

  • Climatology
  • Meteorology
  • Geophysics

Background:

  • Assessing global tropical cyclone (TC) risk is challenging due to infrequent landfalls and limited observational data.
  • Synthetic TC datasets offer a solution to overcome these limitations and improve risk assessment.

Purpose of the Study:

  • To present a novel global synthetic tropical cyclone dataset generated by the ImpeRIal college Storm (IRIS) model.
  • To address biases present in other synthetic TC models by simulating decay from peak intensity.

Main Methods:

  • Utilized 42 years of observed tropical cyclone data as input.
  • Developed the IRIS model to simulate the decay phase of tropical cyclones from their lifetime maximum intensity.
  • Generated a 10,000-year synthetic dataset of wind speeds.
  • Validated the synthetic dataset against observed tropical cyclone data.

Main Results:

  • The IRIS model successfully captures key statistical characteristics of observed tropical cyclone data.
  • The generated synthetic dataset demonstrates realistic return periods for landfall maximum wind speeds on a global scale.
  • The novel simulation approach in IRIS minimizes dataset bias.

Conclusions:

  • The IRIS model provides a valuable tool for enhancing global tropical cyclone risk assessment.
  • Synthetic datasets generated by IRIS can improve our understanding of extreme wind events.
  • The minimized bias and validated realism make IRIS a significant advancement in climate modeling for natural hazard assessment.