Green's Function‐Based Tsunami Data Assimilation: A Fast Data Assimilation Approach Toward Tsunami Early Warning

Wang, Yuchen; Satake, Kenji; Maeda, Takuto; Gusman, Aditya Riadi

doi:10.1002/2017gl075307

Cited by 48 publications

(52 citation statements)

References 20 publications

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“…Although at some time the positive and negative logarithm terms may cancel each other and the geometric mean ratio becomes anomalously high (Wang et al, 2017), a mean value close to 1 generally indicate accurate forecasting. Although at some time the positive and negative logarithm terms may cancel each other and the geometric mean ratio becomes anomalously high (Wang et al, 2017), a mean value close to 1 generally indicate accurate forecasting.…”

Section: Accuracy Of Data Assimilationmentioning

confidence: 99%

“…The time window is defined as the period during which the observed data are used for assimilation (Wang et al, 2017). When the propagating tsunami reaches the real ocean bottom gauges, in this example around 30 min at ocean bottom gauge 23217, the data assimilation process begins.…”

Section: Data Assimilationmentioning

confidence: 99%

“…We used tsunami simulation code JAGURS available at https://github.com/jagursadmin/jagurs) and the data assimilation code TDAC Maeda et al, 2015;Wang et al, 2017; available at https://github.com/takutomaeda/tdac). Ocean Bottom Seismograph Instrument Pool (OBSIP) for providing tsunami data of 2009 Dusky Sound earthquake (http://www.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…One promising method that can be used for tsunami early warning is the tsunami data assimilation first introduced by Maeda et al (2015). Although a large computational load was an obstacle to apply this data assimilation technique in real time, Wang et al (2017) accelerated the forecasting process based on the data assimilation by the introduction of Green's Function-based Tsunami Data Assimilation (GFTDA). This method has already been successfully applied to synthetic tsunamis around Japan based on the Seafloor observation network for earthquakes and tsunamis along the Japan Trench (Maeda et al, 2015) and the real tsunamis recorded by pressure gauges in the Cascadia subduction zone .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tsunami Data Assimilation Without a Dense Observation Network

Wang

Maeda

Satake

et al. 2019

Geophysical Research Letters

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The tsunami data assimilation method enables tsunami forecasting directly from observations, without the need of estimating tsunami sources. However, it requires a dense observation network to produce desirable results. Here we propose a modified method of tsunami data assimilation for regions with a sparse observation network. The method utilizes interpolated waveforms at virtual stations. The tsunami waveforms at the virtual stations between two existing observation stations are estimated by shifting arrival times with the linear interpolation of observed arrival times and by correcting the amplitudes for their water depths. In our new data assimilation approach, we employ the Optimal Interpolation algorithm to both the real observations and virtual stations, in order to construct a complete wavefront of tsunami propagation. The application to the 2004 Sumatra‐Andaman earthquake and the 2009 Dusky Sound, New Zealand, earthquake reveals that addition of virtual stations greatly helps improve the tsunami forecasting accuracy.

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Section: Accuracy Of Data Assimilationmentioning

confidence: 99%

Section: Data Assimilationmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tsunami Data Assimilation Without a Dense Observation Network

Wang

Maeda

Satake

et al. 2019

Geophysical Research Letters

Self Cite

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“…Previous data assimilation studies have used optimal interpolation (OI), a simplified version of the Kalman filter (KF) that keeps both the Kalman gain matrix and error covariance matrix fixed throughout assimilation. This provides computational efficiency, particularly when exploiting linearity to use Green's functions to make forecasts at select locations (Wang et al, ). However, as we demonstrate in this study, improved stability and accuracy can be achieved by updating the gain and error covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

Tsunami Wavefield Reconstruction and Forecasting Using the Ensemble Kalman Filter

Yang

Dunham

Barnier

et al. 2019

Geophysical Research Letters

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Offshore sensor networks like DONET and S‐NET, providing real‐time estimates of wave height through measurements of pressure changes along the seafloor, are revolutionizing local tsunami early warning. Data assimilation techniques, in particular, optimal interpolation (OI), provide real‐time wavefield reconstructions and forecasts. Here we explore an alternative assimilation method, the ensemble Kalman filter (EnKF), and compare it to OI. The methods are tested on a scenario tsunami in the Cascadia subduction zone, obtained from a 2‐D coupled dynamic earthquake and tsunami simulation. Data assimilation uses a 1‐D linear long‐wave model. We find that EnKF achieves more accurate and stable forecasts than OI, both at the coast and across the entire domain, especially for large station spacing. Although EnKF is more computationally expensive than OI, with development in high‐performance computing, it is a promising candidate for real‐time local tsunami early warning.

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A Tsunami Warning System based on Offshore Bottom Pressure Gauges and Data Assimilation for Crete Island in the Eastern Mediterranean Basin

Wang

Heidarzadeh

Satake

et al. 2020

Preprint

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The Eastern Mediterranean Basin (EMB) is under the threat of tsunami events triggered by various causes including earthquakes and landslides. We propose a deployment of Offshore Bottom Pressure Gauges (OBPGs) around Crete Island, which would enable tsunami early warning by data assimilation for disaster mitigation. Our OBPG network consists of 12 gauges distributed around Crete Island, with a 100-km interval, based on three criteria to select the locations. The station network must have a good azimuthal coverage and have enough (>50 km) distance from the coast, and the OBPGs are placed at the locations where the most energetic wave dynamics occur, which is confirmed by Empirical Orthogonal Function (EOF) analysis of pre-calculated tsunami scenarios. We demonstrate three test cases comprising a hypothetical seismogenic tsunami in east Sicily, a hypothetical landslide tsunami in the Aegean Sea, and the real tsunami event of the May 2020 off the Crete earthquake. Our designed OBPG network achieves an accuracy of 88.5% for the hypothetical seismogenic tsunami and 87.3% for the hypothetical landslide tsunami with regard to the forecasting of first tsunami peak. For the real event of May 2020, it predicts the tsunami arrival at tide gauge NOA-04 accurately; the observed and forecasted amplitudes of the first wave are 5.0 cm and 4.5 cm, respectively. The warning lead time for the May 2020 event was ~10 min. Therefore, our results reveal that the assimilation of OBPG data can satisfactorily forecast the amplitudes and arrival times for tsunamis in the EMB.

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Green's Function‐Based Tsunami Data Assimilation: A Fast Data Assimilation Approach Toward Tsunami Early Warning

Cited by 48 publications

References 20 publications

Tsunami Data Assimilation Without a Dense Observation Network

Tsunami Data Assimilation Without a Dense Observation Network

Tsunami Wavefield Reconstruction and Forecasting Using the Ensemble Kalman Filter

A Tsunami Warning System based on Offshore Bottom Pressure Gauges and Data Assimilation for Crete Island in the Eastern Mediterranean Basin

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