## 1 Introduction  
Measuring solar radiation is crucial for various applications, including climate monitoring and weather forecasting, and is particularly important for the development and optimization of renewable energy projects. Accurate solar radiation data are essential for optimizing solar energy applications and validating models that forecast long-term daily global radiation levels, aiding in the efficient deployment and management of solar energy systems [1, 2]. Furthermore, accurate solar radiation data are indispensable for environmental and climate studies, impacting sustainable energy solutions aimed at mitigating climate change [3]. The importance of disseminating best practices in solar radiation measurement and modelling is emphasized, highlighting their significance in educational and operational contexts within the solar industry [4]. These efforts collectively facilitate the efficient deployment of solar technologies, enhancing energy management systems and contributing to sustainable-development goals. Solar radiation data are derived from various sources, including ground-based measurements, satellite observations, and reanalysis datasets, each offering unique insightsinto solar energy patterns and dynamics. Ground-based measurements, such as those obtained from pyranometers, pyrheliometers, and weather stations, provide direct and accurate assessments of solar radiation at specific locations. For example, the World Radiation Monitoring Center-Baseline Surface Radiation Network (WRMC-BSRN) operates globally distributed ground stations equipped with high-quality instruments to measure solar radiation parameters [5]. These measurements contribute data valuable for understanding regional variations in solar radiation and their impacts on climate and energy systems. Within the realm of climate change research, BSRN data have been instrumental in investigating global phenomena, such as solar dimming and brightening [6; 7]. In the context of energy systems, these data have been crucial in validating the frequency and ramp distributions pertinent to studies focusing on low-voltage grid dynamics [8]. Satellite observations complement ground measurements by providing global coverage and continuous monitoring. Satellites like Himawari, Meteosat Second Generation (MSG), GOES (Geostationary Operational Environmental Satellites), and INSAT (Indian National Satellite System) offer geostationary perspectives, capturing solar radiation data over specific regions with a high temporal resolution [9]. These satellites are instrumental in weather forecasting, solar energy planning, and monitoring meteorological phenomena. Reanalysis datasets, such as ECMWF-ERA5 (European Centre for Medium-Range Weather Forecasts-Fifth Generation Reanalysis) and MERRA2 (Modern-Era Retrospective Analysis for Research and Applications, version 2), merge observations with numerical models to offer consistent and gridded records of solar radiation and other meteorological parameters over time and space [10]. These datasets are invaluable for climate studies, renewable energy planning, and the understanding of historical solar radiation patterns. Satellite data estimate the solar radiation on Earth's surface via remote sensing [11]. While they offer broad coverage and frequent updates, ground-based data sources provide precise measurements, but are limited to specific locations. Reanalysis data merge observations with models based on historical records, but may have biases and uncertainties [12]. Integrating these datasets requires validation, the addressing of uncertainties, and the combination of sources for reliable solar radiation estimates.