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Abstract

A modeling study has been conducted to simulate the June 13, 2013 U.S. East Coast meteotsunami event as a test of the model forecast concept. A numerical simulation based on the MOST (Method of Splitting Tsunami) model was employed for the meteotsunami propagation forecast, while the weather radar reflection imagery was used to simulate real-time input data for the atmospheric pressure-induced tsunami generation. The model tsunami was generated by a moving pressure field during 2.87 h of forcing, and the resultant tsunami was then simulated for additional 5.68 h of propagation without any forcing for a total of 8.55 h of meteotsunami evolution from generation to coastal impact. Simulated time series were compared with the measurements from sea-level coastal gages and the Deep-ocean Assessment and Reporting for Tsunami (DART) data. The model is able to reproduce in general the recorded sea-level changes in the deep ocean and at the coast in terms of arrival times and amplitudes. The model was able to predict coastal tsunami impacts that occurred from one to two hours after the model data assimilation phase ended. Therefore, this approach shows promise for developing meteotsunami model forecast capability based on measurements and data assimilation in real time, at least for meteotsunamis generated by fast-moving weather systems visible on radar reflection imagery. All the data used in this study are already available in real time, the MOST model is already implemented as a seismically generated tsunami forecast model at the Tsunami Warning Centers (TWCs), which makes transition of potential meteotsunami forecast capability to warning operations straightforward.

Details

Title
Meteotsunami model forecast: can coastal hazard be quantified in real time?
Author
Titov Vasily 1   VIAFID ORCID Logo  ; Moore, Christopher 1 

 NOAA/Pacific Marine Environmental Laboratory, Seattle, USA (GRID:grid.422706.5) (ISNI:0000 0001 2168 7479) 
Pages
1545-1561
Publication year
2021
Publication date
Mar 2021
Publisher
Springer Nature B.V.
ISSN
0921030X
e-ISSN
15730840
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2507356745
Copyright
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2021.