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A Reanalysis of the October 2016 "Meteotsunami" in British Columbia with Help of High-Frequency Radars and Autoregressive Modeling

Abstract : On October 14th, 2016, the station of Tofino (British Columbia, Canada) issued the first ever real-time tsunami alert triggered by a coastal High-Frequency Radar system, based on the identification of abnormal surface current patterns. The detection occurred in the absence of any reported seismic event but coincided with a strong atmospheric perturbation, which qualified the event as meteo-tsunami. We re-analyze this case in the light of a new radar signal processing method which was designed recently for inverting fast-varying sea surface currents from the complex voltage time series received on the antennas. This method, based on an Auto-regressive modeling combined with a Maximum Entropy Method, yields a dramatic improvement in both the Signal-to-Noise Ratio and the quality of the surface current estimation for very short integration time. This makes it possible to evidence the propagation of a sharp wave front of surface current during the event and to map its magnitude and arrival time over the radar coverage. We show that the amplitude and speed of the inferred residual current do not comply with a Proudman resonance mechanism but are consistent with the propagation of a low-pressure atmospheric front and wind vectors as revealed by satellite imagery. This indicates that the event that triggered a tsunami alert is more likely a storm surge than a true meteo-tsunami. Beyond this specific case, another outcome of the analysis is the promising use of oceanographic radars as proxy's for the characterization of atmospheric fronts.
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Contributor : Charles-Antoine Guérin <>
Submitted on : Saturday, November 28, 2020 - 9:54:32 AM
Last modification on : Saturday, May 8, 2021 - 10:10:38 AM

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Baptiste Domps, Julien Marmain, Charles-Antoine Guérin. A Reanalysis of the October 2016 "Meteotsunami" in British Columbia with Help of High-Frequency Radars and Autoregressive Modeling. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2021, ⟨10.1109/LGRS.2021.3066849⟩. ⟨hal-03029276⟩



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