research product . 2018

On the influence of spatial sampling on climate networks

Molkenthin, N.; Rehfeld, K.; Stolbova, V.; Tupikina, L.; Kurths, J.;
Open Access English
  • Published: 15 Jan 2018
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.
Subjects
arXiv: Physics::Atmospheric and Oceanic Physics
Communities
  • European Marine Science
Funded by
EC| LINC
Project
LINC
Learning about Interacting Networks in Climate
  • Funder: European Commission (EC)
  • Project Code: 289447
  • Funding stream: FP7 | SP3 | PEOPLE
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