On the influence of spatial sampling on climate networks

N Molkenthin, K Rehfeld, V Stolbova, L Tupikina, J Kurths

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Abstract

Climate networks are constructed from climate
time series data using correlation measures. It is widely accepted
that the geographical proximity, as well as other geographical
features such as ocean and atmospheric currents,
have a large impact on the observable time-series similarity.
Therefore it is to be expected that the spatial sampling
will influence the reconstructed network. Here we investigate
this by comparing analytical flow networks, networks generated
with the START model and networks from temperature
data from the Asian monsoon domain. We evaluate them on
a regular grid, a grid with added random jittering and two
variations of clustered sampling. We find that the impact of
the spatial sampling on most network measures only distorts
the plots if the node distribution is significantly inhomogeneous.
As a simple diagnostic measure for the detection of
inhomogeneous sampling we suggest the Voronoi cell size
distribution.
Original languageEnglish
Pages (from-to)651-657
Number of pages7
JournalNonlinear Processes in Geophysics
Volume21
Issue number3
DOIs
Publication statusPublished - 3 Jun 2014

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    Molkenthin, N., Rehfeld, K., Stolbova, V., Tupikina, L., & Kurths, J. (2014). On the influence of spatial sampling on climate networks. Nonlinear Processes in Geophysics, 21(3), 651-657. https://doi.org/10.5194/npg-21-651-2014