Probabilistic Modeling of Monthly Temperature Historical Series in Mossoró, Northeastern Brazil

Abstract

We fitted the following seven distribution probabilities to the data of monthly average temperature in Mossoró, northeastern Brazil: Normal, Log-Normal, Beta, Gamma, Log-Pearson (Type III), Gumbel, and Weibull. To assess the goodness of fit the empirical distributions to the theoretical distribution, we applied the tests of Kolmogorov-Smirnov, Chi-square, Cramer-von Mises, Anderson-Darling, Kuiper, and Logarithm of Maximum Likelihood, at 10% of probability. The temperature series were obtained from 1970 to 2007. The Normal distribution provided the best fit to the historical series of average monthly temperature. Although the Kolmogorov-Smirnov test showed a very high level of approval, which generated some uncertainty regarding the test criteria, it is the more recommended to studies with approximately symmetric data and small series.

Stochastic modeling of reference evapotranspiration in arid and semi-arid zones

“…The values of Eto (reference evapotranspiration) were accumulated in consecutive periods of 10 days, and then analyzed using program R, Version 3.1.1 (2018), to verify the adjustment of the accumulated Eto to the probability distribution of Normal model, with confidence levels of 1% to 95%, where adjustments are performed through nonparametric tests Kolmogorv-Smirnov (KS), Wilcoxon (W2), Anderson-Darling (AD) and Chi-square % probability (Catalunha et al, 2002). The Maximum Likelihood Logarithm (V) was applied to verify the occurrence of a higher frequency of acceptance of the null hypothesis (Assis et al, 2018). The data of the studied phenomena is represented by the normal probability model.…”