Statistical Modelling Immoderate Weather Event by Using R and SAS: A Case Study of Minneapolis/St Paul Region in Minnesota, USA
- July 3, 2018
- Posted by: RSIS
- Category: Mathematics
International Journal of Research and Scientific Innovation (IJRSI) | Volume V, Issue VI, June 2018 | ISSN 2321–2705
Mayooran Thevaraja and Deepak Sanjel
Department of Mathematics and Statistics, Minnesota State University, Mankato, USA
Abstract:-Climate projections suggest the frequency and intensity of some environmental extremes will be affected in the future due to a changing climate. Ecosystems and the various sectors of human activity are sensitive to extreme weather events, such as heavy rains and floods, droughts and high and low temperatures, especially when they occur over prolonged periods. In 1985 Wigley studied about extreme events dangerously affected human society which is included among others agriculture, water resources, energy demand and mortality. In this paper, extreme elevated temperature events for nearly 117 years from the Minneapolis/St Paul, Minnesota State, and area are analyzed from the major international airport [St. Paul] and popular city in Minnesota. The main aim of this study is to find the best fitting distribution to the extreme daily temperature measured over the Minneapolis region for the years 1900-2016 by using the maximum likelihood approach. The study also predicts the extreme temperature for return periods and their confidence bands. In this paper, extreme temperature events are defined by two different methods based on (1) the annual maximums of the daily temperature, (2) the daily temperature exceeds some specific threshold value and (3) Bayesian Model using Markov chain Monte Carlo (MCMC). The Generalized Extreme Value distribution and the Generalized Pareto distribution are fitted to data corresponding to the methods 1 and 2 to describe the extremes of temperature and to predict its future behavior. Finally, we find the evidence to suggest that the Frechet distribution provides the most appropriate model for the annual maximums of daily temperature after removing an outlier and the Generalized Pareto Distribution (GPD) gives the reasonable model for the daily temperature data over the threshold value of 96°F for the Minneapolis location. Further, we derive estimates of 2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150 and 200 years return levels and its corresponding confidence intervals for extreme temperature.
Keywords: Annual maximum, Threshold, Generalized Extreme Value distribution (GEVD), Generalized Pareto Distribution (GPD), Maximum likelihood estimation, Return period, Bayesian
