Tsunami detection by high-frequency radar in British Columbia: performance assessment of the time-correlation algorithm for synthetic and real events

Guérin, Charles-Antoine; Grilli, Stéphan T.; Moran, Patrick; Grilli, Annette R.; Insua, Tania L.

doi:10.1007/s10236-018-1139-7

Cited by 17 publications

(22 citation statements)

References 36 publications

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“…One promising avenue for early detection of tsunamis from such non-seismic sources is the use of shore-based high frequency radars, combined with tsunami detection algorithms based on simulations of potential tsunami scenarios cf. 53 , and literature reviews therein. Following the premonitory study of 12 , the present research confirms that state-of-the-art numerical models can accurately simulate the potential hazard from volcanic induced tsunamis.…”

Section: Discussionmentioning

confidence: 99%

Modelling of the tsunami from the December 22, 2018 lateral collapse of Anak Krakatau volcano in the Sunda Straits, Indonesia

Grilli

Tappin

Carey

et al. 2019

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On Dec. 22, 2018, at approximately 20:55–57 local time, Anak Krakatau volcano, located in the Sunda Straits of Indonesia, experienced a major lateral collapse during a period of eruptive activity that began in June. The collapse discharged volcaniclastic material into the 250 m deep caldera southwest of the volcano, which generated a tsunami with runups of up to 13 m on the adjacent coasts of Sumatra and Java. The tsunami caused at least 437 fatalities, the greatest number from a volcanically-induced tsunami since the catastrophic explosive eruption of Krakatau in 1883 and the sector collapse of Ritter Island in 1888. For the first time in over 100 years, the 2018 Anak Krakatau event provides an opportunity to study a major volcanically-generated tsunami that caused widespread loss of life and significant damage. Here, we present numerical simulations of the tsunami, with state-of the-art numerical models, based on a combined landslide-source and bathymetric dataset. We constrain the geometry and magnitude of the landslide source through analyses of pre- and post-event satellite images and aerial photography, which demonstrate that the primary landslide scar bisected the Anak Krakatau volcano, cutting behind the central vent and removing 50% of its subaerial extent. Estimated submarine collapse geometries result in a primary landslide volume range of 0.22–0.30 km 3 , which is used to initialize a tsunami generation and propagation model with two different landslide rheologies (granular and fluid). Observations of a single tsunami, with no subsequent waves, are consistent with our interpretation of landslide failure in a rapid, single phase of movement rather than a more piecemeal process, generating a tsunami which reached nearby coastlines within ~30 minutes. Both modelled rheologies successfully reproduce observed tsunami characteristics from post-event field survey results, tide gauge records, and eyewitness reports, suggesting our estimated landslide volume range is appropriate. This event highlights the significant hazard posed by relatively small-scale lateral volcanic collapses, which can occur en-masse , without any precursory signals, and are an efficient and unpredictable tsunami source. Our successful simulations demonstrate that current numerical models can accurately forecast tsunami hazards from these events. In cases such as Anak Krakatau’s, the absence of precursory warning signals together with the short travel time following tsunami initiation present a major challenge for mitigating tsunami coastal impact.

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Section: Discussionmentioning

confidence: 99%

Modelling of the tsunami from the December 22, 2018 lateral collapse of Anak Krakatau volcano in the Sunda Straits, Indonesia

Grilli

Tappin

Carey

et al. 2019

Sci Rep

Self Cite

222

140

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“…These storms were the remnants of Typhoon Songda, thus the triggering event was atmospheric in origin and there was no seismic alert issued at that time. An in-depth a posteriori analysis of the meteorological data gathered during the event, together with the recorded HF radar data in the light of an improved tsunami detection algorithm, clearly showed that two successive abnormal long waves impacted the coast, which was a meteotsunami (Guérin et al, 2018). This tsunami was first detected by the HFR 60 km offshore, about 45 min before its arrival on the coast.…”

Section: Hazard Detectionmentioning

confidence: 95%

The Global High Frequency Radar Network

et al. 2019

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Academic, government, and private organizations from around the globe have established High Frequency radar (hereinafter, HFR) networks at regional or national levels. Partnerships have been established to coordinate and collaborate on a single global HFR network (http://global-hfradar.org/). These partnerships were established in 2012 as part of the Group on Earth Observations (GEO) to promote HFR technology and increase data sharing among operators and users. The main product of HFR networks are continuous maps of ocean surface currents within 200 km of the coast at high spatial (1-6 km) and temporal resolution (hourly or higher). Cutting-edge remote sensing technologies are becoming a standard component for ocean observing systems, contributing to the paradigm shift toward ocean monitoring. In 2017 the Global HFR Network was recognized by the Joint Technical WMO-IOC Commission for Oceanography and Marine Meteorology (JCOMM) as an observing network of the Global Ocean Observing System (GOOS). In this paper we will discuss the development of the network as well as establishing goals for the future. The U.S. High Frequency Radar Network (HFRNet) has been in operation for over 13 years, with radar data being ingested from 31 organizations including measurements from Canada and Mexico. HFRNet currently holds a collection from over 150 radar installations totaling millions of records of surface ocean velocity measurements. During the past 10 years in Europe,

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“…The Japan tsunami was detected 15-19 min before its arrival at the coast. Grilii et al [14] and Guérin et al [15] proposed a detection method using cross-correlation of the signals received at two points along a tsunami wave ray calculated beforehand and reported detection beyond the continental shelf, based on numerical experiments for far-and near-field tsunamis. The usage of band-averaged velocities is effective for detecting an incoming tsunami by reducing the observed errors in radial velocities and extracting the coherent motions of surface waters.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, there are some difficulties in determining the value of the q-factor beforehand, which varies with the bottom topography and tsunami magnitude, and in applying the method to a region with a complex bottom topography when the area-band-averaged velocities are used for tsunami arrival detection. For the signal-based method, it is necessary to calculate the tsunami wave rays beforehand, and the performance depends on the selection of rays and the choice of low-and high-contrast threshold values (Guérin et al [15]). …”

Section: Discussionmentioning

confidence: 99%

“…Both current-based and receiving-signal-based methods should be validated and assessed through statistical analysis based on long-term observation signals observed by HF radar over at least a full year for a large variety of oceanic conditions, because their detection performance will vary in accordance with the sea surface state (Guérin et al [15]; Fuji and Hinata [17]). However, long-term performance assessment of these methods has not yet been fully conducted.…”

Section: Discussionmentioning

confidence: 99%

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Real-Time Tsunami Detection with Oceanographic Radar Based on Virtual Tsunami Observation Experiments

et al. 2018

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Abstract:The tsunami generated by the 2011 Tohoku-Oki earthquake was the first time that the velocity fields of a tsunami were measured by using high-frequency oceanographic radar (HF radar) and since then, the development of HF radar systems for tsunami detection has progressed. Here, a real-time tsunami detection method was developed, based on virtual tsunami observation experiments proposed by Fuji et al. In the experiments, we used actual signals received in February 2014 by the Nagano Japan Radio Co., Ltd. radar system installed on the Mihama coast and simulated tsunami velocities induced by the Nankai Trough earthquake. The tsunami was detected based on the temporal change in the cross-correlation of radial velocities between two observation points. Performance of the method was statistically evaluated referring to Fuji and Hinata. Statistical analysis of the detection probability was performed using 590 scenarios. The maximum detection probability was 15% at 4 min after tsunami occurrence and increased to 80% at 7 min, which corresponds to 9 min before tsunami arrival at the coast. The 80% detection probability line located 3 km behind the tsunami wavefront proceeded to the coast as the tsunami propagated to the coast. To obtain a comprehensive understanding of the tsunami detection probability of the radar system, virtual tsunami observation experiments are required for other seasons in 2014, when the sea surface state was different from that in February, and for other earthquakes.

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Tsunami detection by high-frequency radar in British Columbia: performance assessment of the time-correlation algorithm for synthetic and real events

Abstract: The authors recently proposed a new method for detecting tsunamis using High-Frequency (HF) radar observations, referred to as "Time-Correlation

Cited by 17 publications

References 36 publications

Modelling of the tsunami from the December 22, 2018 lateral collapse of Anak Krakatau volcano in the Sunda Straits, Indonesia

Modelling of the tsunami from the December 22, 2018 lateral collapse of Anak Krakatau volcano in the Sunda Straits, Indonesia

The Global High Frequency Radar Network

Real-Time Tsunami Detection with Oceanographic Radar Based on Virtual Tsunami Observation Experiments

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