ChEESE-PTF Workflow for Probabilistic Tsunami Forecasting v0.1
Creators
- 1. INGV
- 2. NGI
- 3. UMA
- 4. CINECA
Description
ChEESE-PTF
PROBABILISTIC TSUNAMI FORECASTING (PTF) FOR EARLY WARNING AND RAPID POST EVENT ASSESSMENT
This workflow provides rapid probabilistic forecasting of tsunami inundation, following an earthquake offshore or close to the coast, before it actually occurs or before tsunami observations are available. For near-field tsunami early warning (EW) purposes, the large uncertainty about earthquake location and magnitude, as available in the first minutes, are reflected in forecasting uncertainty. For the purpose of supporting rapid post-disaster intervention, for which more time is available, additional information on tsunami source and tsunami waves recorded in the subsequent phases can be exploited to eventually narrow down the tsunami forecast uncertainty. The workflow also provides an early estimate of the earthquake parameters with their uncertainty when they are not yet available, as a by-product.
For further details:
Private GitLab repository:
https://gitlab.com/manuela.volpe/cheese-ptf
To request access contact manuela.volpe@ingv.it
Based on:
Selva J., Lorito S., Volpe M., Romano F., Tonini R., et al. Probabilistic tsunami forecasting forn early warning, Nat. Commun. 12(2021) 5677, https://doi.org/10.1038/s41467-021-25815-w.
Notes
Files
PTF_readme.pdf
Files
(133.5 kB)
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Additional details
References
- Selva J., Lorito S., Volpe M., Romano F., Tonini R., et al. Probabilistic tsunami forecasting forn early warning, Nat. Commun. 12(2021) 5677, https://doi.org/10.1038/s41467-021-25815-w.
- Løvholt F., Lorito S., Macias J., Volpe M., Selva J., and Gibbons S. (2019). Urgent tsunami computing, in International Conference for High Performance Computing, Networking, Storage and Analysis 2019, IEEE/ACM HPC for Urgent Decision Making (Urgent HPC), DOI 10.1109/UrgentHPC49580.2019.00011.
- Selva, J. et al. Probabilistic Tsunami Forecasting (PTF) for Tsunami Early Warning operations. Geophysical Research Abstracts Vol. 21, EGU2019-17775, 2019. EGU General Assembly 2019, solicited.