Climate Data Imputation and Quality Improvement Using Satellite Data

Combating climate change has emerged as a global concern recently, and meteorological data remain an important measure for analyzing and predicting climate trends. However, ground weather stations and sensors can be impacted by faults due to accidents and unreliability, often resulting in, for example, missing data and lowering the overall quality of the data. This paper explores the impact of using satellite data as an input feature for machine learning algorithms. In particular, temperature, pressure, wind speed, and global horizontal radiation data are imputed using various machine learning algorithms to overcome potential data quality issues resulting from the ground stations. The results from two experiments highlight that the performance of the algorithms significantly increases by using satellite data as input features. For instance, the incorporation of satellite data improved the R2 values for temperature prediction using Random Forest and XGBoost to 0.86 and 0.84, respectively, demonstrating a notable enhancement compared to models without satellite data. The paper discusses several implications of these findings and outlines future research directions to further enhance