Oloyede Adamson, Ozuomba Simeon, Asuquo Philip, Olatomiwa Lanre, Longe Omowunmi Mary
Advanced Space Technology Applications Laboratory Uyo, National Space Research and Development Agency, P.M.B. 437, Abuja, Nigeria.
Department of Computer Engineering, University of Uyo, Uyo, 520103, Nigeria.
Environ Monit Assess. 2023 Jan 30;195(2):343. doi: 10.1007/s10661-023-10961-z.
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 (R) of 0.710 for maximum temperature, then MAE of 0.876 °C, RMSE of 1.225 °C with a coefficient of determination (R) 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.
对于外推、气候变化及其他气象分析而言，研究过去和当前的天气事件是一个先决条件。美国国家航空航天局（NASA）已开发出一种模型，能够预测地球上任何地点的各种气象数据，包括那些没有气象站、气象卫星覆盖及其他气象测量仪器的地点。本文以阿夸伊博姆机场为例，评估了NASA温度数据相对于尼日利亚气象局（NiMet）地面实测数据的预测准确性。对从NiMet和NASA获取的温度数据进行了探索性数据分析（描述性和诊断性分析），以便为预测性和规范性分析提供清晰的路径。以从NiMet获取的2783天气象数据作为地面实测数据，计算了相应分辨率下NASA预测的准确性。发现最高温度的平均绝对误差（MAE）为2.184°C，均方根误差（RMSE）为2.579°C，决定系数（R）为0.710；最低温度的MAE为0.876°C，RMSE为1.225°C，决定系数（R）为0.620。两个数据集之间存在良好的相关性；因此，可以开发一个模型，以NASA数据作为输入来生成更准确的预测。采用五种预测算法进行了预测性和规范性分析：决策树回归、XGBoost回归以及使用有限内存布罗伊登 - 弗莱彻 - 戈德法布 - 香农（LBFGS）优化器的多层感知器（MLP）、使用随机梯度（SGD）优化器的MLP和使用亚当优化器的MLP。MLP LBFGS算法表现最佳，最高温度的MAE显著降低了35.35%，RMSE降低了31.06%；相应地，最低温度的MAE降低了10.05%，RMSE降低了8.00%。所得结果表明，在有足够数据的情况下，将NASA预测结果作为LBFGS - MLP模型的输入可为研究区域提供更准确的温度预测。
