Articles | Volume 30, issue 4
https://doi.org/10.5194/npg-30-515-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-30-515-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Nov 2023
Research article |
| 21 Nov 2023
| 21 Nov 2023
Superstatistical analysis of sea surface currents in the Gulf of Trieste, measured by high-frequency radar, and its relation to wind regimes using the maximum-entropy principle
Sofia Flora
CORRESPONDING AUTHOR
Dipartimento di Matematica e Geoscienze, University of Trieste, Trieste, Italy
National Institute of Oceanography and Applied Geophysics – OGS, Oceanographic Section, Trieste, Italy
Laura Ursella
National Institute of Oceanography and Applied Geophysics – OGS, Oceanographic Section, Trieste, Italy
Achim Wirth
Univ. Grenoble Alpes, CNRS, Grenoble INP, LEGI, 38000 Grenoble, France
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We developed a hierarchy of idealized deterministic-stochastic models to simulate the sea surface currents in the Gulf of Trieste. They include tide-and-wind driven sea surface current components, resolving the slowly varying part of the flow and a stochastic signal, representing the fast-varying small-scale dynamics. The comparison with High Frequency Radar observations shows that the non-Gaussian stochastic model captures the essential dynamics and permits to mimic the observed fat-tailed PDF.
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Achim Wirth
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Achim Wirth
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Short summary
An increasing amount of data allows us to move from low-order moments of fluctuating observations to their PDFs. We found the analytical fat-tailed PDF form (a combination of Gaussian and two-exponential convolutions) for 2 years of sea surface current increments in the Gulf of Trieste, using superstatistics and the maximum-entropy principle twice: on short and longer timescales. The data from different wind regimes follow the same analytical PDF, pointing towards a universal behaviour.
An increasing amount of data allows us to move from low-order moments of fluctuating...
