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May 22, 2026Journal of the Meteorological Society of Japan Ser IIOpen Access

Parameter estimation of an atmospheric model using geostationary satellite observation to improve prediction of tropical cyclones: an idealized experiment

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Authors

YHYuki HiroseLDLe DucFTFuto Tomizawa

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Overview

Randomized trial estimates model parameters using satellite observations to enhance tropical cyclone predictions, suggesting significant improvements in forecasting accuracy.

Key Points

  • This study aims to reduce parametric uncertainty in atmospheric modeling by calibrating parameters with geostationary satellite data to enhance tropical cyclone predictions.
  • Developed a calibration method for meso-scale atmospheric model parameters using satellite brightness temperature observations.
  • Utilized machine-learning-based surrogate models and an image-processing inspired evaluation index.
  • Evaluated the estimated parameters' impact on the accuracy of tropical cyclone intensity predictions.
  • Effectively estimated parameters in cloud microphysics and boundary layer schemes from satellite observations.
  • Improved prediction accuracy of satellite image simulations and reduced errors in tropical cyclone intensity forecasts.
  • Demonstrated the advantages of using multiple parameter adjustments based on geostationary satellite data.

Cite This Study

Hirose et al. (2026) studied this question.

synapsesocial.com/papers/6a0ff3d9d674f7c03778cbc3https://doi.org/10.1007/s44394-026-00021-8
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