Calibration of a real-time tsunami detection algorithm for sites with no instrumental tsunami records: application to coastal tide-gauge stations in eastern Sicily, Italy

Abstract

Abstract. Coastal tide gauges play a very important role in a tsunami warning system, since sea-level data are needed for a correct evaluation of the tsunami threat, and the tsunami arrival has to be recognized as early as possible. Real-time tsunami detection algorithms serve this purpose. For an efficient detection, they have to be calibrated and adapted to the specific local characteristics of the site where they are installed, which is easily done when the station has recorded a sufficiently large number of tsunamis. In this case the recorded database can be used to select the best set of parameters enhancing the discrimination power of the algorithm and minimizing the detection time. This chance is however rare, since most of the coastal tide-gauge stations, either historical or of new installation, have recorded only a few tsunamis in their lifetimes, if any. In this case calibration must be carried out by using synthetic tsunami signals, which poses the problem of how to generate them and how to use them. This paper investigates this issue and proposes a calibration approach by using as an example a specific case, which is the calibration of a real-time detection algorithm called TEDA (Tsunami Early Detection Algorithm) for two stations (namely Tremestieri and Catania) in eastern Sicily, Italy, which were recently installed in the frame of the Italian project TSUNET, aiming at improving the tsunami monitoring capacity in a region that is one of the most hazardous tsunami areas of Italy and of the Mediterranean.

Statistical properties of coastal long waves analysed through sea-level time-gradient functions: exemplary analysis of the Siracusa, Italy, tide-gauge data

“…The detailed description of TEDA can be found in Bressan and Tinti (2011) and in Bressan et al (2013).…”

“…In general, for small tidal heights (in the range of 0.2-0.3 m) as in most of the Mediterranean stations, a fitting polynomial of degree 0 (the mean) is sufficient for a correct de-tiding, computed over an interval T T = 60 min and anticipated by the gap T GT = 15 min, as used here and in Bressan et al (2013). We observe that for coastal oceanic stations, the optimal parameters for a correct de-tiding might vary greatly.…”

“…The functions we introduce here were first defined in a different context, i.e. within a tsunami early detection algorithm (TEDA) with the purpose of detecting tsunamis and hazardous long waves (see Bressan and Tinti, 2011and Bressan et al, 2013, and were proven to be suitable to characterise the sea-level signal in the tsunami frequency band. Before using TEDA for a specific site, a calibration process is needed to determine the parameters of TEDA computational procedures and mathematical expressions.…”

Tsunami arrival time detection system applicable to discontinuous time series data with outliers

“…For stations having insufficient tsunami records, Beltrami and Risio (2011) examined their tsunami detection algorithm with synthetic tsunami signals in which ideal sinusoidal tsunamis were superimposed on ideal wind waves based on JONSWAP (Joint North Sea Wave Project) wave spectra. In addition, Bressan et al (2013) tested their tsunami detection algorithm with synthetic tsunami signals in which the results of numerical simulations were superimposed on the tide gauge record of possible circumstances (e.g., calm or rough sea). Risio and Beltrami (2014) estimated the performance of the tsunami detection algorithm with historical tsunami signals, in which the record of the DART buoy was superimposed on the wind wave, which in turn was synthesized by means of the random-phase method.…”

“…Bressan and Tinti (2012) proposed a tsunami early detection algorithm (TEDA) designed to detect an anomalous water level based on two slope-based algorithms: tsunami detection and secure detection. The TEDA was successfully calibrated and tested on both synthetic tsunamis and historical tsunami records (Bressan and Tinti, 2012;Bressan et al, 2013). Pérez et al (2013) introduced a real-time automatic tsunami detection algorithm based on a variance method that was developed within the TRANSFER (Tsunami Risk and Strategies for the European Region) project.…”