Kurths, J

Molkenthin, N

Rehfeld, K

Stolbova, V

Tupikina, L

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

Molkenthin, N.

Rehfeld, K.

Stolbova, V.

Tupikina, L.

Kurths, J.

Repositorium für Naturwissenschaften und Technik  (TIB Hannover)

English

On the influence of spatial sampling on climate networks

Electronic Publication Information Center

Aberdeen University Research

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

N. Molkenthin

K. Rehfeld

V. Stolbova

L. Tupikina

J. Kurths

Directory of Open Access Journals

Nonlinear Processes in Geophysics

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.
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On the influence of spatial sampling on climate networks

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Repositorium für Naturwissenschaften und Technik (TIB Hannover)

Electronic Publication Information Center

Aberdeen University Research

Directory of Open Access Journals

Crossref