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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.