Metodologia para automatizar a detecção do efeito borda de nuvem utilizando Python

Abstract

This work provides a comprehensive investigation of the cloud enhancement effect, an atmospheric phenomenon in which solar irradiance exceeds expected values due to the interaction of sunlight with cloud edges, resulting in radiative focusing and an increase in the diffuse component. The study begins with an introduction to solar radiation, highlighting the attenuation of energy from the top of the atmosphere to the surface, as well as the composition of global irradiance into direct and diffuse components. It then discusses the importance of clear-sky models, with emphasis on Gueymard’s method and its contributions to improving irradiance estimates, particularly in studies addressing extreme deviations such as the peaks associated with cloud enhancement. The literature review shows that rapid and intense irradiance fluctuations can compromise the reliability of measurement instruments, affect sensor calibration, influence weather forecasting, and require greater rigor in solarimetric analyses due to the sudden and short-lived nature of these events. Based on these foundations, the present study applies measured data processing to analyze the occurrence of such peaks, identifying characteristic patterns and comparing them with clear-sky reference irradiance limits. Data from the Cedro-PE station (2022) were processed in Python, replacing the manual Excel-based workflow. The implemented algorithm enabled more precise and efficient filtering, grouping, and duration estimation of events. Validation confirmed equivalence with manual results, demonstrating the reliability of the automated approach and its applicability to large- scale solarimetric analyses, reinforcing the relevance of this study for improving solar energy monitoring, diagnostics, and forecasting methods. Keywords: Cloud enhancement. Solar irradiance. Clear-sky model. Gueymard method. Data processing.