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Evaluating Impact-based Forecasting Models for Tropical Cyclone Anticipatory Action
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DOI：10.1016/j.ijdrr.2025.105782.png)
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Abstract
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• Introduces a unified model-card framework to compare operational Impact-based Forecast (IBF) methods for tropical cyclone anticipatory action. • Applies both models to Typhoon Kammuri, revealing that a simpler damage-curve approach can match a machine-learning model’s impact prediction in practice. • Highlights how forecast uncertainties and selected methodological choices together shape IBF outcomes, underscoring the need for transparent communication of both natural and human factors. • Presents a proof-of-concept interactive dashboard for practitioners to adjust “knobs” and visualize IBF trade-offs in real-time.
Keywords:
impact-based forecasting
tropical cyclone
anticipatory action
model comparison
forecast uncertainty
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