Trend Analysis of the Variations of Ambient Temperature Using Mann-Kendall Test and Sen’s Estimate in Calabar, Southern Nigeria

Abstract

Studies have been made to understand the fluctuations of temperature in regions around the world because of an ever changing variation due mostly to man’s anthropogenic activities. This study is focused on analysing the temperature variations in Calabar, Southern Nigeria for 20 years (1998 – 2018) using the Mann-Kendall trend test and Sen’s slope estimator. Results from the Mann-Kendall test shows that the annual trends of the maximum and average ambient temperature are both increasing after showing positive Kendall Z-values (1.04 and 0.10 respectively). The minimum ambient temperature annual trend was observed to be decreasing after having a negative Kendall Z-value (-0.32); the estimations from Sen’s slope agreed with these results. The p-values were calculated to be 0.745, 0.299, and 0.922 for the minimum, maximum, and average ambient temperature trend respectively; these values were all found to be less than all significant levels used 0.1% (0.001), 1% (0.01), 5% (0.05), and 10% (0.1). This shows that the trends were not increasing or decreasing significantly, hence there is “no significant trend”. This may be due to the almost unchanging weather condition in the region arising from its closeness to the Atlantic Ocean.

Solar energy: A panacea for the electricity generation crisis in Nigeria

“…The global total monthly radiation was collected from 2005-2016, and the average was found for each month and presented for all state capitals of the different regions. From mere observations, it can be concluded that the Northern states evidently have higher values of global solar radiation, which is in agreement with facts as the Northern States are hotter, and the Southern States generally have lower temperature values, mainly because of their closeness to the Atlantic Ocean [6,7,8,9]. After using the total monthly global solar radiation with Eq.…”

“…Adopting these alternative energy resources can go as far as creating jobs, improving energy access and security [5], and reducing the emission of fossil fuels, which depletes the ozone layer and contributes to the formation of GHGs; this ramps up the average global surface temperature. Studies by Agbo et al [6,7], Agbo and Ekpo [8], Agbo [9], and Alhaji et al [10] have shown that the ambient temperature for their respective study locations in Nigeria is steadily increasing. This shows the importance of adopting clean, renewable energy.…”

“…Variations of meteorological parameters affect the amount of energy gotten from the sun. According to studies from Agbo et al [6,7], Agbo and Ekpo [8], Agbo [9] Alhaji et al [10], and Akinbami [11], climate change is one of the effects of these variations with its issue resulting from diverse anthropogenic activities. With the recent increase in cellular networks' deployment across the world and the most recent introduction of the 5th Generation (5G) technology, two main problems have been noticed; the cost of energy for running these mobile networks and its effects on the environment.…”

The Role of Statistical Methods and Tools for Weather Forecasting and Modeling

“…The Mann-Kendall original and seasonal test has been applied to analyze the maximum temperature in Calabar, Nigeria for the annual and seasonal (dry and wet season) variation respectively, and results show that the annual, dry season and wet season had increasing variations (after having positive Kendall Z-values of 2.52, 3.23, 4.04 respectively) and they were all increasing significantly at 5% (0.05) level of significance after their p-values were all less than 0.05 agreeing with Agbo and Ekpo [23].…”

“…There are myriads of ways in which weather can be forecasted and this arises from the understanding of basic meteorological parameters and how they behave in the atmosphere; and also from the understanding of the role of statistics in climate research [21]. Research in this area has been reviewed to give a better understanding of the different techniques for analyzing trends; which include, Linear Regression (Multiple and Simple), the Mann-Kendall trend test [22,23] (to test for trends in a time series variation), the Angstrom-Prescott model for estimating solar radiation as well as the python implementation of some various techniques.…”