A Big Data Approach to Meteotsunami Detection using NOAA Water Level Gauges Along the US East Coast

Meteotsunamis are atmospherically induced long ocean waves in the tsunami frequency band with periods ranging from several minutes to several hours.  A handful of recent events have resulted in noticeable impacts and increased the interest in meteotsunamis from both the public and the scientific community.  However, despite increased scientific interest there lacks a quantified assessment of exactly how frequently meteotsunamis occur and in what locations.  Here we present a proof of concept for a meteotsunami detection algorithm, designed to identify potential meteotsunami-type events utilizing historical water level observations from NOAA water level gauges.  The approach relies on wavelet and spectral analyses of annual 6-minute and 1-minute water level observations applied simultaneously at over 80 gauges along the United States east coast.  An event is recorded as a possible meteotsunami when higher than normal energy is prevalent in the tsunami frequency band (a detected tsunami signature), a minimum of three gauges observe the event (is spatially significant), and the resultant wave height reaches a minimum of 0.15 m (is vertically significant).  This method is utilized to identify potential meteotsunami events along the United States east coast in 2013.  These events are reviewed with concurrent meteorological data to attempt to identify potential forcing mechanisms and to eliminate false positives.  Once validated this detection approach can be applied to over 20 years of water level observations throughout the coastal U.S. to develop a climatology of meteotsunami-type events.