Ajuste de la distribución estadística para evaluar los patrones de precipitaciones en diversas zonas de Assam, India

The maximum and minimum projected rainfall for a specific time of the year, taking into account a particular level of probabilities, makes up the probable rainfall, which is a great meteorological parameter of information. The purpose of this study is to assess the performance of probability distributions using various goodness-of-fit tests in order to develop a standard for selecting them in various zones of Assam based on monthly rainfall data from 1985-2022. Ten different distributions such as Gumbel, Weibull, Gamma, Logistic, Exponential, log-Normal, Pearson-0, Pearson-I, Pearson-III and Pearson-V distributions have been considered in the study. The maximum likelihood estimation approach was used to estimate the parameters associated with these distributions. The best fitted probability distribution is determined using a variety of goodness of fit techniques, including the Bayesian information criteria (BIC), Akaike information criterion (AIC), and Kolmogorov-Smirnov (K-S) test. Although not one distribution fits the rainfall data perfectly for every month, the Pearson-I distribution typically fits the data better than the other distributions most of the time, according to the goodness of fit tools. The data has been collected from the National Data Centre (NDC), India Meteorological Department (IMD), Pune.