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

which are very near geographically, and the second by the capitals of Pernambuco, Paraı´ba, Sergipe, Alagoas, Rio Grande do Norte, Bahia, Rio de Janeiro, São Paulo, Espirito Santo, Santa Catarina, Rio Grande do Sul and Parana´, representing almost all Brazilian coast from the Northeast to the South. Figs 9 and 10 show the network analysis for the relative air humidity and wind speed, respectively, and their interpretation can be done in a similar manner as in Figs 7 and 8. Simi- larly to radiation and air temperature, the edges are mostly connections between neighboring regions and regions located on the Brazilian coast. Discussion In this paper, we seek to analyze the time series of radiation, temperature, humidity, and wind speed from 27 Brazilian weather stations, in five regions, from the point of view of autocorrela- tion and cross-correlation. Throughout Detrended fluctuation analysis (DFA), we revealed a characteristic pattern for radiation for all scales, regardless of the region. For temperature, sim- ilarly to radiation, we observed a less intense transition for the Southeast and South regions. For humidity in the Southeast and South regions and wind speed in the Midwest, Southeast, and South regions, there was no transition for one day of observation (n = 13), and different characteristics for state capitals in the North and Northeast regions were observed. Persistent and anti-persistent behavior was interpreted as time variations, with close values influencing the memory of observations (hour-to-hour). Autocorrelation results for Southeast and South

Fig 5. Cross-correlation, ρDCCA(n), for global radiation in the Southeast 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.g005 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 12 / 18