Statistical Modeling of Temperature Extremes Behaviour in Ghana

Abstract

This paper focuses on modeling the extreme maximum temperature in Ghana using the extreme value theory. This is to inform decision-makers to help them plan appropriate risk mitigating measures to reduce the damage caused by drought. The block maxima with Generalized Extreme Value (GEV) and the General Pareto (GP) (with “all excesses” and decluster peaks) were used on 113 years of monthly temperature data in Ghana. Two statistical tests for stationarity, namely Augmented Dickey-Fuller (ADF) and Mann-Kendall tests were performed. In the GEV modeling, the model selection criteria (Akaike information criterion and likelihood-ratio test) and the diagnostic checking indicate that the model with linear trend in location parameter is appropriate. In fitting the GP distribution, the results from the parameter estimation show that GP with “all excesses” better fits the data than the decluster peaks. The diagnostic checking also lead to the same conclusion. The GEV estimates of the return level show that the return temperature which exceeds the maximum temperature of the observation period (36.3) starts to appear in the return period of T= 20 over years. This suggests that in 20 years to come, maximum temperature in Ghana will exceed 36.3, which may indicate a drought period.