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Short-Record-Based Extreme Wind Simulation.

Edmond D H Cheng1, Arthur N L Chiu1

  • 1Department of Civil Engineering, University of Hawaii at Manoa, Honolulu, Hawaii 96822.

Journal of Research of the National Institute of Standards and Technology
|July 5, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces a Markov chain model to estimate extreme wind speeds from limited historical data. The model aids in natural hazard damage mitigation by simulating long-term wind patterns.

Area of Science:

  • Climatology
  • Meteorology
  • Environmental Science

Background:

  • Accurate estimation of extreme wind speeds is crucial for natural hazard damage mitigation.
  • Limited historical wind data presents a challenge for reliable long-term extreme wind speed assessment.

Purpose of the Study:

  • To investigate a Markov chain model for generating long-term annual extreme winds from short-term historical records.
  • To enhance the utilization of limited wind data for natural hazard mitigation strategies.

Main Methods:

  • Developed a simulation model comprising three components: state of wind speeds, wind speed distribution functions, and transition probability matrices.
  • Generated time series of hourly wind speeds, differentiating between well-behaved climate conditions and extreme wind events.
Keywords:
Gumbel distributionMarkov chain modelextreme wind speedsimulationtransition probability matrix

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  • Applied the model to short-term wind records from Houston Intercontinental Airport, Texas.

    • Main Results:

    • Demonstrated the model's capability to generate long-term extreme wind speed estimates based on limited historical data.
    • Successfully applied the Markov chain model to a real-world case study in Texas.

    Conclusions:

    • The Markov chain model provides a viable approach for estimating extreme wind speeds using scarce historical data.
    • This methodology can significantly improve natural hazard damage mitigation planning by providing more robust wind speed projections.