A Reanalysis of the October 2016 "Meteotsunami" in British Columbia with Help of High-Frequency Radars and Autoregressive Modeling
Baptiste Domps, Julien Marmain, Charles-Antoine Gu\'erin

TL;DR
This paper reanalyzes a 2016 British Columbia event initially flagged as a meteo-tsunami using advanced radar signal processing, revealing it was likely caused by atmospheric conditions rather than seismic activity, and demonstrates improved radar analysis techniques.
Contribution
Introduces a novel auto-regressive and maximum entropy based radar signal processing method that enhances surface current estimation from high-frequency radar data.
Findings
Improved signal-to-noise ratio in radar data analysis.
Evidence suggests the event was caused by atmospheric fronts, not a true tsunami.
Radar can be used to characterize atmospheric phenomena.
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…
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