Abstract
We report here a series of detailed statistical analyses on the sea level variations in the Port of Trieste using one of the largest existing data catalogues that covers more than a century of measurements. We show that the distribution of waiting times, which are defined here akin to econophysics, namely the series of shortest time spans between a given sea level L and the next sea level of at least L + δ in the catalogue, exhibits a distinct scale-free character for small values of δ. For large values of δ, the shape of the distribution depends largely on how one treats the periodic components embedded in the sea level dataset. We show that direct analyses of the raw dataset yield distributions similar to the exponential distribution, while pre-processing the sea level data by means of a local averaging numerical recipe leads to Pareto-Tsallis distributions.
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