A Novel Information Theoretical Criterion for Climate Network Construction

Sara Cornejo-Bueno,Luis Prieto-Godino,Antonio J Caamaño,Mihaela I Chidean,Sancho Salcedo-Sanz

doi:10.3390/sym12091500

Sara Cornejo-Bueno, Luis Prieto-Godino + Show 3 more

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https://doi.org/10.3390/sym12091500

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Abstract

This paper presents a novel methodology for Climate Network (CN) construction based on the Kullback-Leibler divergence (KLD) among Membership Probability (MP) distributions, obtained from the Second Order Data-Coupled Clustering (SODCC) algorithm. The proposed method is able to obtain CNs with emergent behaviour adapted to the variables being analyzed, and with a low number of spurious or missing links. We evaluate the proposed method in a problem of CN construction to assess differences in wind speed prediction at different wind farms in Spain. The considered problem presents strong local and mesoscale relationships, but low synoptic scale relationships, which have a direct influence in the CN obtained. We carry out a comparison of the proposed approach with a classical correlation-based CN construction method. We show that the proposed approach based on the SODCC algorithm and the KLD constructs CNs with an emergent behaviour according to underlying wind speed prediction data physics, unlike the correlation-based method that produces spurious and missing links. Furthermore, it is shown that the climate network construction method facilitates the evaluation of symmetry properties in the resulting complex networks.

Highlights

Many social, biological or climate systems, among many others, can be naturally described by networks [1,2,3], where nodes represent a problem’s related features, and links denote relationships or interactions between nodes [4]
We evaluate the performance of the proposed Climate Network (CN) construction method based on the Second Order Data-Coupled Clustering (SODCC) algorithm and Kullback-Leibler divergence (KLD)
The dataset considered to construct the CN is the difference between the wind speed prediction by the WRF and the real wind speed measured in each wind farm, i.e., the wind speed prediction error

Summary

Introduction

Biological or climate systems, among many others, can be naturally described by networks [1,2,3], where nodes represent a problem’s related features, and links denote relationships or interactions between nodes [4]. CNs have been the paradigm of spatial networks since their introduction by Tsonis and Roebber in [7] This paradigm has been profusely used to model several phenomena and different inter-relationships in climate-related systems [8,9], including short-term and local or mesoscale

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