Model specification and data interpretation of climate in Pakistan

Abstract

The issue of climate change has occurred very strongly during the last 2 decades on global scale in analysis of estimated inferences on the environment of vulnerable states. Pakistan is one of the states that has high vulnerability index to natural disaster. Currently, climate of Pakistan is growing warmer; and we are moving around different signals of change. Meanwhile, it is important to ascertain climate trend of temperature, rain, wind speed and humidity in Pakistan on sequential basis. Therefore, this study aims to ascertain the climate trend through model specification of Stretched distributions comprising Lindley Stretched Exponential distribution, Exponentiated Stretched Exponential distribution, Kumaraswamy Stretched Exponential distribution and Transmuted Stretched Exponential distribution. A sort of comparison among new proposed, classical and modern models is also put up to evaluate the relative quality of statistical models for Climate data through Maximized Log Likelihood, Akaike Information Criterion, Bayesian Information Criterion, Consistent Akaike Information Criterion, Hannan Quinn Information Criteria and Kolmogorov Smirnov test. Findings specify that Kumaraswamy Stretched Exponential distribution is the best fit for modeling mean minimum temperature (oC) data, Lindley Stretched Exponential distribution is the best fit for modeling total July’s rain (mm) data, Transmuted Stretched Exponential distribution is the best fit for modeling wind speed (knots) data, Exponentiated Stretched Exponential distribution is the best fit for modeling humidity (%) data according to model specification. Generally, the results demonstrate that and new proposed models are ascertained best models for specification of climate data in Pakistan. It is also observed that these models proved stretchy and flexible for modeling climate data in the state of Pakistan.