Abstract
The relevance in studying climatological phenomena is based on the influence that variables of this nature exert on the world. Among the most observed variables, temperature stands out, whose effect of its variation may cause significant impacts, such as the proliferation of biological species, agricultural production, population health, etc. Probability distributions have been studied to verify the best fit to describe and/or predict the behavior of climate variables and, in this context, the present study evaluated, among six probability distributions, the best fit to describe a historical temperature series. minimum monthly mean. The series used in this study encompass a period of 38 years (1980 to 2018) separated by month from the weather station of the Manaus - AM station (OMM: 82331) obtained from INMET, totaling 459 observations. Difference-Sign and Turning Point tests were used to verify data independence and the maximum likelihood method to estimate the parameters. Kolmogorov-Smirnov, Anderson-Darling, Cramér-von Mises, Akaike Information Criterion and quantile-quantile plots were used to select the best fit distribution. Log-Normal, Gama, Weibull, Gumbel type II, Benini and Rice distributions were evaluated, with the best performing Rice, Log-Normal and Gumbel II distributions being highlighted.
Highlights
The relevance of studying climatological phenomena is based on the influence that variables of this nature have in different areas of knowledge or even in everyday life
There are indications that the probability distribution selected to describe the month of March may not be the most suitable to describe the data of average minimum temperature for the month of September, for example, and this distinction is due to the distinct behavior between the series
As observed graphically and by the Kolmogorov-Smirnov, Anderson-Darling and Cramér-von Mises tests, that, in cases where the Log-Normal distribution emerges as the distribution with the most appropriate adjustment, the Gamma and Rice distributions could be adopted with little difference between them, being recommended in the description of the behavior for mean minimum temperature data as potential competitors to those usually used
Summary
The relevance of studying climatological phenomena is based on the influence that variables of this nature have in different areas of knowledge or even in everyday life. Among the most observed variables, the temperature stands out, whose effect of its variation can cause significant impacts, such as in the proliferation of animal and vegetable species, agricultural production, population health, etc From this perspective, analyzes of historical series of climatic variables have been carried out in order to describe and/or predict the behavior of these variables, as studies by (Astolpho (2003); Berlato & Althaus (2010); Araújo et al (2010); Assis et al (2013); Gomes et al (2015); Silva et al (2013); Assis et al (2018); Ximenes et al (2020); de Mendoza Borges et al (2020); Aguirre et al (2020) and Santiago et al (2020)) whose objective was to verify the best fit to describe climatological measures in cities in Brazil. The author mentioned above mentions that researchers have been elaborating models, through the processing of supercomputers of series of information of all kinds, linked to climatic situations, to try to predict future trends of climate change, in different scenarios
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