Upper-air meteorological dataset for Uyo, using radiosonde.

Weather pattern anomalies and climate change have greatly impacted human activities and the environment in varying ways. Whether induced naturally or by anthropogenic activities, it remains a menace to global public health. A foreknowledge of the weather/climate change can help in mitigating the impact of disasters emanating from these changes. Upper-air meteorological data play an exceptionally large role in weather and climate prediction. However, there is a paucity of ground truth meteorological data in Nigeria and many parts of Africa. Consequently, the need to measure and archive these data. Internet of things and blockchain technologies are employed to build a system that captures and records meteorological data at up to 9,000 metres above sea level. Spanning between January 18, 2021 and July 26, 2021, in Uyo local government area, upper air pressure, temperature, dew point, time and the elevation at which they were captured, are the meteorological data presented in this data article.

Data-driven techniques for temperature data prediction: big data analytics approach.

For extrapolation, climate change and other meteorological analysis, a study of past and current weather events is a prerequisite. NASA (National Aeronautics and Space Administration) has been able to develop a model capable of predicting various weather data for any location on the Earth, including locations lacking weather stations, weather satellite coverage, and other weather measuring instruments. This paper evaluates the prediction accuracy of the NASA temperature data with respect to NiMet (Nigerian Meteorological Agency) ground truth measurement, using Akwa Ibom Airport as a case study. Exploratory data analysis (descriptive and diagnostic analyses) of temperature retrieved from NiMet and NASA was performed to give a clear path to follow for predictive and prescriptive analyses. Using 2783 days of weather data retrieved from NiMet as ground truth, the accuracy of NASA predictions with the corresponding resolution was calculated. Mean absolute error (MAE) of 2.184 °C and root mean square error (RMSE) of 2.579 °C, with a coefficient of determination (R2) of 0.710 for maximum temperature, then MAE of 0.876 °C, RMSE of 1.225 °C with a coefficient of determination (R2) of 0.620 for minimum temperature was discovered. There is a good correlation between the two datasets; hence, a model can be developed to generate more accurate predictions, using the NASA data as input. Predictive and prescriptive analyses were performed by employing five prediction algorithms: decision tree regression, XGBoost regression and MLP (multilayer perceptron) with LBFGS (limited-memory Broyden-Fletcher-Goldfarb-Shanno) optimizer, MLP with SGD (stochastic gradient) optimizer and MLP with Adam optimizer. The MLP LBFGS algorithm performed best, by significantly reducing the MAE by 35.35% and RMSE by 31.06% for maximum temperature, accordingly, MAE by 10.05% and RMSE by 8.00% for minimum temperature. Results obtained show that given sufficient data, plugging NASA predictions as input to an LBFGS-MLP model gives more accurate temperature predictions for the study area.