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This study introduces a new Bayesian method using underwater acoustics to accurately estimate hurricane wind speeds. The technique successfully predicts maximum wind speed and radius, crucial for storm classification and warnings.

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Area of Science:

  • Oceanography
  • Meteorology
  • Acoustics

Background:

  • Accurate estimation of hurricane parameters is vital for disaster preparedness.
  • Traditional methods for measuring hurricane wind speeds can be challenging and limited in scope.
  • Underwater acoustics offer a novel approach to inferring surface wind conditions.

Purpose of the Study:

  • To develop and validate a Bayesian inversion method for determining key hurricane surface wind parameters.
  • To utilize underwater acoustic measurements for estimating parameters in the Holland hurricane wind model.
  • To assess the accuracy of this acoustic method using synthetic and real-world typhoon data.

Main Methods:

  • Bayesian inversion coupled with Markov chain Monte Carlo (MCMC) sampling.
  • Modeling the linear dependence of low-frequency underwater noise on surface wind speed (15-50 m/s).
  • Estimating hurricane parameters by inverting acoustic data through the axisymmetric Holland model.

Main Results:

  • The method yields unbiased parameter estimates when validated with synthetic data.
  • Accurate estimation of maximum wind speed was achieved for Typhoon Fanapi (2010).
  • A universal constant slope of 0.48 s m⁻¹ was adopted for the wind-noise relationship.

Conclusions:

  • The presented acoustic-based Bayesian method provides a reliable way to estimate hurricane wind parameters.
  • This technique enhances hurricane classification and forecasting capabilities.
  • Underwater acoustics represent a promising tool for remote sensing of tropical cyclone intensity.