Detecting transitions and quantifying differences in two SST datasets using spatial permutation entropy

Earth System Dynamics 2026 Open access

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Authors: Juan Gancio, Giulio Tirabassi, Cristina Masoller, Marcelo Barreiro
DOI: 10.5194/esd-17-533-2026
Cited by: 0
References: 65

Abstract

Abstract. Weather prediction systems rely on the vast amounts of data continuously generated by Earth modeling and monitoring systems, and efficient data analysis techniques are needed to track changes and compare datasets. Here we show that a nonlinear quantifier, the spatial permutation entropy (SPE), is useful to characterize spatio-temporal complex data, allowing detailed analysis at different scales. Specifically, we use SPE to analyze ERA5 and NOAA OI v2 sea surface temperature (SST) anomalies in two key regions, Nino3.4 and Gulf Stream. We perform a quantitative comparison of these two SST products and find that SPE detects differences at short spatial scales (

Fields of Study & Subjects

Scientific Research and Discoveries

Publication details

Journal / source
Earth System Dynamics
Publisher
Copernicus GmbH
Volume
17
Issue
3
Pages
533–561
Publication date
12 May 2026
Language
EN