Detecting transitions and quantifying differences in two SST datasets using spatial permutation entropy
Juan Gancio Vázquez, Giulio Tirabassi, Cristina Masoller, Marcelo Barreiro
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 …
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