Networks analysis of Brazilian climate data based on the DCCA cross-correlation coefficient

states for humidity and wind speed revealed differences due to geographic location and local climate. The approach presented here via DFA follows the same line of thought as the Climatic Stan- dards presented in the 24/03/2022 edition of (INMET) on meteorological variables of the last Fig 6. Cross-correlation, ρDCCA(n), for global radiation in the South region of Brazil. The plots show the cross- correlations between the state capital written in the plot and all others in the region. https://doi.org/10.1371/journal.pone.0290838.g006 Fig 7. Network representation of the environmental variable global solar radiation, considering the 27 Brazilian state capitals as vertices and the edges defined by the DCCA cross-correlation coefficient between pairs of Brazilian state capitals. Only edges associated with the top 10%DCCA cross-correlation coefficients between objects were depicted. The five colors in the vertices correspond to the state capitals in each of the five Brazilian regions. Box sizes of 13–one day, 1261–97 days, and 3328–256 days are depicted, from left to right. https://doi.org/10.1371/journal.pone.0290838.g007 PLOS ONE Networks analysis of Brazilian climate data based on the DCCA cross-correlation coefficient PLOS ONE | https://doi.org/10.1371/journal.pone.0290838 September 15, 2023 13 / 18