  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.
