Superstatistical analysis of sea surface currents in the Gulf of Trieste, measured by HF Radar, and its relation to wind regimes, using the maximum entropy principle - CNRS - Centre national de la recherche scientifique Access content directly
Journal Articles Nonlinear Processes in Geophysics Year : 2023

Superstatistical analysis of sea surface currents in the Gulf of Trieste, measured by HF Radar, and its relation to wind regimes, using the maximum entropy principle

Abstract

Two years (2021–2022) of High Frequency Radar (HFR) sea surface current data in the Gulf of Trieste (Northern Adriatic Sea) are analysed. Two different time scales are extracted using superstatistical formalism: a relaxation time and a larger timescale over which the system is gaussian. A new analytical universality class of Probability Density Functions (PDFs) is proposed for ocean current data combining a gaussian PDF for the fast fluctuations and a convolution of exponential PDFs for the slowly evolving variance of the gaussian. The Gaussian PDF has maximum entropy for real-valued variables with a given variance. If a positive variable, as is a variance, has a specified mean, the maximum entropy solution is an exponential PDF. Here it is the sum of two exponentials, reflecting the two spatial degrees of freedom. In the Gulf of Trieste there are three distinct main wind forcing regimes: Bora, Sirocco and low wind, leading to a succession of different sea current dynamics on different time scales. The universality class PDF successfully fits the observed data over the two observation years and also for each wind regime separately with a different variance of the variance PDF, which is the only free parameter in all the fits.
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Dates and versions

hal-04235318 , version 1 (10-10-2023)

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Sofia Flora, Laura Ursella, Achim Wirth. Superstatistical analysis of sea surface currents in the Gulf of Trieste, measured by HF Radar, and its relation to wind regimes, using the maximum entropy principle. Nonlinear Processes in Geophysics, 2023, ⟨10.5194/npg-2023-9⟩. ⟨hal-04235318⟩

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