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Green's Function‐Based Tsunami Data Assimilation: A Fast Data Assimilation Approach Toward Tsunami Early Warning
Author(s) -
Wang Yuchen,
Satake Kenji,
Maeda Takuto,
Gusman Aditya Riadi
Publication year - 2017
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2017gl075307
Subject(s) - data assimilation , assimilation (phonology) , superposition principle , warning system , meteorology , computation , geology , waveform , computer science , seismology , environmental science , algorithm , geography , telecommunications , mathematics , philosophy , linguistics , mathematical analysis , radar
Abstract We propose a new tsunami data assimilation approach based on Green's functions to reduce the computation time for tsunami early warning. Green's Function‐based Tsunami Data Assimilation (GFTDA) forecasts the waveforms at points of interest (PoIs) by superposition of Green's functions between observation stations and PoIs. Unlike the previous assimilation approach, GFTDA does not require the calculation of the tsunami wavefield for the whole region during the assimilation process, because the Green's functions have been calculated in advance. The forecasted waveforms can be calculated by a simple matrix manipulation. The application to the tsunami waveforms recorded by the bottom pressure gauges of the Cascadia Initiative from the 2012 Haida Gwaii earthquake reveals that GFTDA achieves the same accuracy as the previous assimilation approach while reducing the time required to issue a valid tsunami warning.