Uncovering temporal regularity in atmospheric dynamics

What is it about?

Extracting meaningful information from complex oscillatory signals is a challenging task with applications across disciplines. Here, we propose a novel technique, based on Hilbert analysis, and apply it to global observational surface air temperature (SAT) datasets. We show that, by combining moving temporal average with Hilbert analysis, we can uncover underlying regularities in SAT dynamics, which are not always detected by spectral analysis.

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Why is it important?

Our results demonstrate that Hilbert analysis combined with temporal averaging is a powerful new tool for discovering hidden temporal regularity in complex oscillatory signals.

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