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Feb 20, 2024Keywords:
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Trends of Diurnal Variations of Meteorological Variables Received at Anchor University Space-Lab Weather Station
Abstract
Background: Understanding the diurnal var iations of meteorological var iables is crucial for weather forecasting and climate prediction. The availability of meteorological data can also be useful in developing models for predicting weather and climate patterns and developing early warning systems for natural disasters.
Objective: This study aimed to analyze the trends of diurnal variations of meteorological variables using data collected from the Weather Station at the Anchor University's Space, Atmospheric Physics and Radio Wave Propagation Laboratory (AUL Space Lab) in Nigeria located at 6.6054° N, 3.2438° E.
Methods: Data were collected between J anuar y and March 2022 using the AcuRite Atlas® (7-in-1) Weather Station, which measured meteorological variables such as temperature, humidity, wind speed and direction, rainfall, UV, light intensity, and lightning. The collected data were analyzed using the AcuRite Atlas indoor touch screen display.
Results: The results showed that the mean range of measured meteorological variables such as wind chill, wind speed and direction, air temperature, relative humidity, dew point, barometric pressure, accumulated rainfall, heat index, and light intensity fell within the range of 22.60C -35.700C, 0 ms-1-9.72 ms-1, 0 ms-1-6.38 ms-1, 0 ms-1- 257, 22.60C-35.70C, 16%-93%, 2.80C-24.800C, 1003.39HPa-1010.16
HPa, 0 mm, 22.30C-38.40C, and 1910 cd m-2 to -72400 cd m-2, respectively. While the diurnal trends of the meteorological variables were comparable to those of other AcuRite Atlas Weather Stations, the data's variability indicated the influence of the tropical local effect.
Conclusion: The AUL Space Lab Weather Station provides valuable meteorological data for researching tropical-related phenomena in West Africa. The results of this study can be helpful in developing models for predicting tropical weather and climate patterns and developing early warning systems for natural disasters such as floods and hurricanes.
