Quality Control Procedures for Solar Radiation Data in Northern Chile
Abstract
Data quality control is essential to ensure the reliability of research and analysis results. Terrestrial measurements of solar radiation often present intermittency, randomness, and uncertainty, which may lead to errors. Quality control procedures help detect erroneous, missing, or anomalous data caused by factors such as instrument cleaning, miscalibration, sensor imbalance, or communication issues. This study evaluates validation methods for ground-based irradiance measurements in the Atacama Desert and the city of Antofagasta, northern Chile. The three components of global irradiance were analyzed independently and in combination, complemented by color-based visualization of distribution patterns over time. This approach allowed the identification of valid, anomalous, and missing data. Most anomalies were linked to tracking system failures, with unevenness and soiling as minor contributors. The results demonstrated that the proposed methodology effectively filtered and detected common errors, while visual analysis supported the inspection and diagnosis of anomalies in the measurements.
Keywords
Data quality control, Solar irradiance validation, Time series, Anomaly detection, Visual analysis