## 4 Results
### Evaluation against KHOA IORS and NIFoS Flux Towers  
As the GK2A/AMI hourly SSI model using the CNN method was trained using only the ground-based SSI measurements from the KMA ASOS stations, it was necessary to inspect the applicability of the estimated hourly CNN SSI from GK2A/AMI by comparing it with ground-based SSI measurements from the KHOA IORS and NIFoS flux towers for the period from 1 August 2020 to 31 January 2022 (Figure 8). The KHOA IORS and NIFoS flux towers measured the SSI every minute and every 30 min, respectively, and we derived hourly SSI using only those in situ SSI data for which there were no missing  
Figure 7: Comparison of the estimated SSI from GK2A/AMI using (**a–c**) the operational algorithm and (**d–f**) CNN, and in situ SSI measurements from KMA ASOS stations, for the period from 1 August 2020 to 31 January 2022, where the colored squares represent (**a,d**) RMSE, (**b,e**) bias, and (**c,f**) Pearson’s \(R\).  
data over an hour. In situ hourly SSI from the KHOA IORS and NIFoS flux towers ranged from 0.001 MJ m\({}^{-2}\) to 4.017 MJ m\({}^{-2}\), and GK2A/AMI-derived CNN hourly SSI ranged from 0.0 MJ m\({}^{-2}\) to 3.638 MJ m\({}^{-2}\). Compared with the in situ hourly SSI, the total number of data matchups was 36,246, and the GK2A/AMI-derived CNN hourly SSI showed accuracies of 0.328 MJ m\({}^{-2}\) (RMSE), 0.252 MJ m\({}^{-2}\) (MAE), 0.326 MJ m\({}^{-2}\) (STD), and \(-0.038\) MJ m\({}^{-2}\) (bias), with an nRMSE of 0.269, indicating that the CNN-based hourly SSI retrieval model had a tendency to underestimate the SSI relative to the ground-based SSI measurements from the KHOA IORS and NIFoS flux towers overall. In particular, for an SSI of less than 2.0 MJ m\({}^{-2}\), the GK2A/AMI-derived CNN hourly SSI showed accuracies of 0.321 MJ m\({}^{-2}\) (RMSE) and 0.011 MJ m\({}^{-2}\) (bias), indicating that its tendency to underestimate SSI weakened under low-SSI conditions. However, for an SSI greater than 2.0 MJ m\({}^{-2}\), the RMSE and bias were 0.350 MJ m\({}^{-2}\) and \(-0.195\) MJ m\({}^{-2}\), respectively, implying that the tendency to underestimate SSI intensified under high-SSI conditions. The characteristic that the tendency of the CNN model to underestimate became stronger as the SSI increased was also found through the regression line, whose slope was 0.8785 (less than 1) and bias 0.1105 (greater than 0). Because the CNN model was trained based only on the KMA ASOS stations, the estimated SSI from the CNN model could be optimized for the Korean Peninsula. Furthermore, the CNN model showed a different tendency depending on the magnitude of SSI. Therefore, when applying the CNN model for other regions, it is necessary to consider its tendencies. For low-latitude regions, where a high SSI is more frequent, the underestimation by the model would be more apparent; for high-latitude regions, where a low SSI is more frequent, the underestimation by the model would weaken. Although the CNN-based SSI model showed an underestimation of SSI compared to the in situ SSI values from the KHOA IORS, the Pearson's \(R\) was 0.939 for the overall SSI, indicating that the CNN-based hourly SSI retrieval model accurately estimated the in situ SSI from the KHOA IORS, overall.  
Figure 8: Validation of the estimated SSI from GK2A/AMI and ground-based SSI measurements from IORS and NIFoS flux towers for the period from 1 August 2020 to 31 January 2022, with the CNN model as the reference model; the color indicates the point density relative to the total matchups; the blue line indicates the regression line.