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Status: Unchecked ×Keyword: typhoon forecasting ×Clear all

2 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Machine‐Learning (ML)‐Physics Fusion Model Accelerates the Paradigm Shift in Typhoon Forecasting With a CNOP‐Based Assimilation Framework

   Niu, Wang, Mu et al. · Geophysical Research Letters · 2025

   The authors couple the FuXi machine-learning model with the physics-based Shanghai Typhoon Model, then use a sensitivity method to guide satellite data assimilation for two 2024 super typhoons.

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   1. UncheckedDense satellite data assimilated in sensitive regions can further improve typhoon forecasts, even though the hybrid model is constrained by FuXi's large-scale fields.“Despite being constrained by FuXi's large‐scale forecast fields, the dense assimilation of satellite observations within these sensitive regions can further enhance typhoon forecasts.”
   2. UncheckedA hybrid of the FuXi machine-learning model and the physics-based SHTM, linked by spectral nudging, improved forecasts of two 2024 super typhoons.“By employing spectral nudging, the hybrid FuXi‐SHTM model leverages FuXi's robust large‐scale forecasting capabilities alongside SHTM's mesoscale strengths, significantly enhancing track, intensity, and precipitation predictions for super typhoons Yagi (2024) and Krathon (2024).”