Preprints
https://doi.org/10.5194/gmd-2024-139
https://doi.org/10.5194/gmd-2024-139
Submitted as: model description paper
 | 
27 Aug 2024
Submitted as: model description paper |  | 27 Aug 2024
Status: this preprint is currently under review for the journal GMD.

GREAT v1.0: Global Real-time Early Assessment of Tsunamis

Usama Kadri, Ali Abdolali, and Maxim Filimonov

Abstract. We introduce a tsunami warning technology towards a global real-time analysis. The technology is based on the analysis of acoustic signals generated together with the tsunami, due to the compression of the water layer. The acoustic signals propagate much faster than the tsunami, and thus can be recorded at hydrophone stations, which in turn enables the analysis in real-time. The presented technology comprises a collection of models that have been integrated into a software with the goal to make it operational, to complement efforts by warning centres and provide a more reliable assessment, globally. The main models that were integrated in the software are presented and briefly discussed. Test cases performed by the software are compared with DART buoy observations, showing satisfactory agreement though discrepancies arise in particular at far distances and locations separated by land. The calculation time of a full global-scale analysis is in the order of tens of seconds on a standard multi-core machine, without reliance on pre-computations, making it appropriate real-time forecast.

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Usama Kadri, Ali Abdolali, and Maxim Filimonov

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2024-139', Anonymous Referee #1, 10 Nov 2024 reply
Usama Kadri, Ali Abdolali, and Maxim Filimonov
Usama Kadri, Ali Abdolali, and Maxim Filimonov

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Short summary
The GREAT v1.0 software introduces a novel tsunami warning technology for global real-time analysis. It leverages acoustic signals generated by tsunamis, which propagate faster than the tsunami itself, enabling real-time detection and assessment. Integrating various models, the software provides reliable and rapid assessment, mapping risk areas, and estimating tsunami amplitude. This advancement reduces false alarms and enhances global tsunami warning systems' accuracy and efficiency.