A statistical study of wind speed and its connectivity with relative humidity and temperature in Ughelli, Delta State, Nigeria

Abstract

One of the vital climatic parameters with significant roles in many natural phenomena is wind. The importance of wind cannot be overemphasized due to its role as a source of renewable energy. The understanding of wind is of great importance particularly for the purpose of prediction and management of severe weather events. However, wind as a climatic parameter depends on relative humidity and temperature as well as other weather parameters and several statistical approaches such as time series analysis, extreme value analysis and spatial analysis have been used to analyze wind speed data. This study uses the kernel density method in analyzing wind speed in Ughelli, Delta State and its connection with relative humidity and temperature using the Gaussian kernel function for a period of five consecutive years from 2018 to 2022. The performance measure employ is the asymptotic mean integrated squared error (AMISE) with the Pearson R test that measures the strength of the relationship that exists between parameters. The results of the investigation with regards to the AMISE shows that 2018 recorded best performance with wind speed and relative humidity while 2021 recorded best performance for wind speed and temperature but 2019 recorded unsatisfactory outcomes for wind speed and the two parameters. This implies that human activities that depend on these parameters for their performance did best in 2018 and 2021 respectively. Furthermore, in terms of connectivity, wind speed and relative humidity are negatively correlated in 2018 and 2022 but positively correlated in 2019, 2020 and 2021 while wind speed and temperature are negatively correlated which implies that as temperature increases, wind speed decreases.