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

Brito [16], specifically with the Extended Multiple Correlation Coefficient DMCx 2 applied to radiation, temperature, and relative humidity between three stations in the interior of the Bahia. As with INMET, Brito [16] also mentioned the importance of this type of study as a contribution to the energy sector. Finally, we show as a highlight for this study the visualization between regions through net- work analysis, with the cross-correlation coefficient roDCCA representing the edges measur- ing the intensity of the correlation between the 26 capitals and the Federal District. Unlike traditional techniques, such as average degree, weighted average degree, network diameter, modularity, PageRank, and connected components, among others, the interconnected net- works were made by boxes representing sizes n = 13 (one day of observations), n = 1261 (97 observation days) and n = 3328 (256 observation days) varied. Another relevant detail in this work was the representation of the networks in which we sought a non-polluting visualization graphically and that the model employed showed the most correlated regions for the variables. However, we opted for regions with a strong cross-correlation of 90%. This does not prevent replicating the methodology for other correlation percentages. Concluding remarks To better understand the behavior of the Brazilian climate, in this paper, four environmental and atmospheric variables, wind speed, radiation, temperature, and humidity, were measured in 27 stations (the capital of each of the 26 Brazilian states plus the federal district) were con-

sidered. The detrended fluctuation analysis was used to evaluate the statistical self-affinity of the signal being the overall main conclusion that the non-stationary and persistent behavior (αDFA(medium) ffi 1.21; n ⩽ 13) upward and/or downward trends are time dependent. In the long-range persistent behavior (αDFA(medium) ffi 0.87; 13 < n ⩽ 5.625), it is observed that var- iables with closer values remain for a longer time, influencing the memory. As for the anti-per- sistent behavior (αDFA(medium) ffi 0.06; n > 5.625), a more stable behavior is expected as the scale reaches the maximum value. The cross-correlation coefficient ρDCCA was used to quantify the long-range cross-correla- tion between Brazilian state capitals per geographical region (North, Northeast, Central-West, Fig 10. Network representation of the environmental variable wind speed, 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.g010 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 15 / 18