## 4 Discussion
### Estimation Bias Based on Weather: Cloudy vs. Clear  
In our study, we observed that during cloudy days, the estimates provided by Heliosat-2 generally exceeded the ground measurements data in both Japanese locations and one African location. Conversely, on clear days, there was a tendency for underestimation, as indicated by the median value of the MB across all study locations. The proportion of cloudy days sampled (ranging from 19.9% to 41.23% across all regions in our study) was notably lower compared to the total sample of clear days (ranging from 58.77% to 81.1%), as demonstrated in Table 2. This result can be linked to the finding of ([PERSON] et al., 2017), which employed FY-4A satellite images to derive GHI using the Heliosat-2 methodology across four locations in China, from June 2018 to May 2019. Their findings mirrored ours, indicating a tendency to overestimate GHI in locations with limited ground-observed data and to underestimate it when such data were more abundant. Studies ([PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2018), focusing on different versions of Heliosat, similarly noted a general underestimation of GHI values by the model during cloudy-sky conditions. GHI estimation from SENSE2 (Solar Energy Nowcasting System) ([PERSON] et al., 2018) also observed an overestimation of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **N\({}_{\text{Cloudy}}\)** & **N\({}_{\text{Clear}}\)** & **Total Days** & **\% N\({}_{\text{Cloudy}}\)** & **\% N\({}_{\text{Clear}}\)** \\ \hline CAB & 167 & 596 & 763 & 21.89 & 78.12 \\ CNR & 136 & 624 & 760 & 17.89 & 82.11 \\ ABS & 132 & 140 & 272 & 48.53 & 51.47 \\ TAT & 143 & 252 & 395 & 36.20 & 63.80 \\ GOB & 88 & 642 & 730 & 12.05 & 87.95 \\ VUW & 240 & 490 & 730 & 32.88 & 67.12 \\ KSA & 55 & 150 & 205 & 26.83 & 73.17 \\ ASO & 74 & 229 & 303 & 24.42 & 75.58 \\ HON & 82 & 220 & 302 & 27.15 & 72.85 \\ \hline \multicolumn{6}{c}{**Regional Analysis (Sum)**} \\ \hline Locations in Europe & 303 & 1220 & 1523 & 19.9 & 81.1 \\ Locations in East Asia & 275 & 392 & 667 & 41.23 & 58.77 \\ Location in Africa & 328 & 1132 & 1460 & 22.47 & 77.53 \\ Location in the Middle East & 55 & 150 & 205 & 26.83 & 73.17 \\ Locations in South Asia & 156 & 449 & 605 & 25.8 & 74.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Representation of the numbers of clear and cloudy days across all the locations in the current investigation. Here, N\({}_{\text{Clouds}}\) represents the number of cloudy days and N\({}_{\text{Clear}}\) represents the number of clear-sky days. The % estimates of the number of cloudy and clear days are given in the last two columns. GHI under cloudy-sky conditions. They attributed this to the uncertainties associated with cloud-related information within the satellite pixel. Additionally, they noted a relatively low underestimation linked with aerosol optical depth (AOD). In line with our findings, these studies underscore the influences of sky conditions and satellite data uncertainties on GHI estimation accuracy. In the context of solar energy estimation, clear-sky models provide estimates assuming cloudless conditions, considering factors like solar elevation angle, site altitude, and atmospheric properties. However, these models often underestimate the impact of cloud cover. Cloudy-sky conditions, on the other hand, account for cloud scattering and absorption of sunlight, leading to overestimation due to clear-sky assumptions. Real-world observations consider dynamic effects, resulting in differences between estimated and measured GHI, while clear-sky models serve as useful benchmarks, understanding the complexities of cloud influence is crucial for accurate solar resource assessments  
In the current investigation, the performance of Heliosat-2 was primarily analyzed based on cloudy and clear days and using the clearness index.