Local clustering coefficient based on three-way partial correlations in climate networks as a new marker of tropical cyclone

TL;DR

This paper introduces a novel climate network marker based on three-way partial correlations using Kendall's rank correlations, which improves real-time tropical cyclone detection compared to traditional methods.

Contribution

It adapts a neuroscience-based local clustering coefficient for climate networks, replacing Pearson's correlations with Kendall's to enable shorter analysis windows and faster cyclone detection.

Findings

The new marker outperforms traditional unweighted clustering coefficients in cyclone association.

Using Kendall's correlations reduces data requirements for real-time analysis.

The method enhances the responsiveness of climate network analysis to tropical cyclones.

Abstract

We introduce a new network marker for climate network analysis. It is based upon an available special definition of local clustering coefficient for weighted correlation networks, which was previously introduced in the neuroscience context and aimed at compensating for uninformative correlations caused by indirect interactions. We modify this definition further by replacing Pearson's pairwise correlation coefficients and Pearson's three-way partial correlation coefficients by the respective Kendall's rank correlations. This reduces statistical sample size requirements to compute the correlations, which translates into the possibility of using shorter time windows and hence into shorter response time of the real-time climate network analysis. We compare this proposed network marker to the conventional local clustering coefficient based on unweighted networks obtained by thresholding the correlation matrix. We show several examples where the new marker is found to be better associated to tropical cyclones than the unweighted local clustering coefficient.