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Probabilistic tsunami forecasting for early warning.

J Selva1, S Lorito2, M Volpe2

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This study introduces Probabilistic Tsunami Forecasting (PTF) to improve early warning systems by accounting for data uncertainties. PTF provides more reliable tsunami threat assessments, balancing missed and false alarms for better public safety.

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Area of Science:

  • Earth Sciences
  • Geophysics
  • Oceanography

Background:

  • Tsunami warning systems face significant uncertainty due to data limitations immediately after earthquakes.
  • Rapid forecasting of tsunami threats is crucial for effective early warning.
  • Existing methods struggle to quantify and manage forecast uncertainties.

Purpose of the Study:

  • To introduce and evaluate Probabilistic Tsunami Forecasting (PTF) as a novel approach for tsunami early warning.
  • To explicitly address and quantify data and forecast uncertainties in tsunami threat assessment.
  • To enable flexible alert level definitions based on desired conservatism and alarm balance.

Main Methods:

  • Development and implementation of the Probabilistic Tsunami Forecasting (PTF) framework.
  • Systematic hindcasting and testing using historical tsunami events.
  • Validation against the 2010 Maule (Chile) and 2003 Zemmouri-Boumerdes (Algeria) tsunamis.
  • Testing with all Mediterranean earthquakes triggering alerts at the Italian Tsunami Warning Centre since 2015.

Main Results:

  • PTF successfully quantifies uncertainties in tsunami forecasting.
  • Forecast accuracy was demonstrated across a range of earthquake magnitudes and types.
  • The system allows for adaptable alert levels based on predefined conservatism.
  • Impact forecasts and recommendations become more certain as new data emerge.

Conclusions:

  • Probabilistic Tsunami Forecasting (PTF) offers a robust method for enhancing tsunami early warning systems.
  • The approach effectively manages uncertainties, leading to more reliable threat assessments.
  • PTF provides a flexible framework adaptable to various warning center needs and risk tolerances.