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Books > Earth & environment > The environment > Environmental impact of natural disasters & phenomena
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Tsunami Data Assimilation for Early Warning (Hardcover, 1st ed. 2022)
Loot Price: R4,219
Discovery Miles 42 190
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Tsunami Data Assimilation for Early Warning (Hardcover, 1st ed. 2022)
Series: Springer Theses
Expected to ship within 18 - 22 working days
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This book focuses on proposing a tsunami early warning system using
data assimilation of offshore data. First, Green's Function-based
Tsunami Data Assimilation (GFTDA) is proposed to reduce the
computation time for assimilation. It can forecast the waveform at
Points of Interest (PoIs) by superposing Green's functions between
observational stations and PoIs. GFTDA achieves an equivalently
high accuracy of tsunami forecasting to the previous approaches,
while saving sufficient time to achieve an early warning. Second, a
modified tsunami data assimilation method is explored for regions
with a sparse observation network. The method uses interpolated
waveforms at virtual stations to construct the complete wavefront
for tsunami propagation. Its application to the 2009 Dusky Sound,
New Zealand earthquake, and the 2015 Illapel earthquake revealed
that adopting virtual stations greatly improved the tsunami
forecasting accuracy for regions without a dense observation
network. Finally, a real-time tsunami detection algorithm using
Ensemble Empirical Mode Decomposition (EEMD) is presented. The
tsunami signals of the offshore bottom pressure gauge can be
automatically separated from the tidal components, seismic waves,
and background noise. The algorithm could detect tsunami arrival
with a short detection delay and accurately characterize the
tsunami amplitude. Furthermore, the tsunami data assimilation
approach is combined with the real-time tsunami detection
algorithm, which is applied to the tsunami of the 2016 Fukushima
earthquake. The proposed tsunami data assimilation approach can be
put into practice with the help of the real-time tsunami detection
algorithm.
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