## 1 Introduction
The relative root mean square error (%RMSE) ranges between 12.0% to 20.0% for all-sky conditions and 8.0% to 12.0% for clear-sky conditions, but notably increases to above 56.0% under cloudy-sky conditions [24]. As for parts of the African region, a study [25] was conducted to evaluate the performance of the Heliosat-based validation method against hourly ground GHI across four typical climatic zones, using entire-year data from 2015. The study reported %RMSE values ranging from 10.4% to 12.7%, with nominal MB values falling between \(-\)0.97% and 0.39%. Considering parts of the East Asian region, the Heliosat-based estimation of GHI against observed data for the time range of 2011-2013 indicated overall relative mean bias deviations (%MB) and %RMSE in daily solar irradiance retrieval values of about 5.0 and 15.0%, respectively. Seasonally, the largest %MB and %RMSE of retrieved daily solar irradiance occurred in spring (9.5 and 21.3%, on average), while the lowest %MB (\(-\)0.3%, on average) and %RMSE (9.7%, on average) occurred in autumn and winter, respectively [26]. Validation studies addressing the Heliosat method play a crucial role in evaluating its accuracy in estimating solar irradiance, ensuring the reliability of its outputs, and pinpointing areas for algorithm enhancement [27]. The present study endeavors to bridge several research gaps within the field. * Firstly, while prior validation studies of the BSRN predominantly utilized data predating 2022, our investigation focuses on a more recent timeframe, utilizing data spanning from 2022 to 2024. This temporal shift ensures that our analyses remain relevant and reflective of current conditions, including the potential impacts of climate change on solar radiation patterns. * Secondly, unlike previous studies that relied solely on either Himawari-8 or MSG datasets for validation, this study utilizes both datasets to cover a broader range of observational locations. Himawari-8 data will be used for the East Asia region, while MSG data will cover the EU, Africa, the Middle East, and South Asia. This approach leverages dedicated satellites for each respective region to derive irradiance, using the same algorithm, and with spatial re-gridding. The datasets generated from 2023 and 2024 will present current trends in solar radiation across these regions. * Lastly, a key objective of our study is the application and refinement of existing methodologies for determining albedo parameters specific to Heliosat-2. We aim to achieve significant improvements in the accuracy and reliability of modeled solar radiation datasets, and therefore, the exactness of the input parameters is crucial. In Section 2, the study addresses the examination of study sites, the utilization of various satellite and ground data, and the methodology employed for extracting GHI data from satellite images, alongside the validation process, which uses different indices against ground datasets. Section 3 presents the obtained results, emphasizing the comparison between satellite and ground data across different sky conditions and assessing the performance of the two satellites in capturing seasonal GHI values. Section 4 further discusses these findings, contextualizing them within previous research endeavors and elucidating any agreements or discrepancies encountered. Finally, Section 5 succinctly encapsulates the key conclusions drawn from the study's findings.