A Novel Information Theoretical Criterion for Climate Network Construction

• Main Authors: Sara Cornejo-Bueno, Mihaela I. Chidean, Antonio J. Caamaño, Luis Prieto-Godino, Sancho Salcedo-Sanz
• Format: Article
• Language: English
• Published: MDPI AG 2020-09-01
• Series: Symmetry
• Subjects: climate networks; complex networks; Kullback-Leibler divergence; data-coupled clustering; wind farms
• Online Access: https://www.mdpi.com/2073-8994/12/9/1500

This paper presents a novel methodology for Climate Networkconstruction based on the Kullback-Leibler divergenceamong Membership Probabilitydistributions, obtained from the Second Order Data-Coupled Clusteringalgorithm. 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 Climate Networkconstruction 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 Climate Networkobtained. We carry out a comparison of the proposed approach with a classical correlation-based Climate Networkconstruction method. We show that the proposed approach based on the Second Order Data-Coupled Clusteringalgorithm and the Kullback-Leibler divergenceconstructs 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.