## 4 Results
### Error Characteristics  
To investigate the effect of observation time on the GK2A/AMI-derived CNN SSI error, we examined the error with respect to Korean Standard Time (KST, UTC+9), month, and SZA (Figure 9). The local time showed an RMSE of 0.24 MJ m\({}^{-2}\) or less, and the opposite changes between RMSE and nRMSE overall (Figure 9a). Korea's solar solstice occurs at approximately 12:30 KST, and the sun rises before and sets after this time. Thus, as the solar altitude increases up until 12:30 KST, the amount of ESR also increases; hence, the amplitude of SSI error increases. In contrast, as the solar altitude decreases after 12:30 KST, the amount of ESR also decreases, so the amplitude of the SSI error decreases. As a result, the RMSE increased up until 12:00 KST, and the RMSE decreased after 13:00 KST. Conversely, as the relative accuracy parameter, the nRMSE indicated the lowest value (0.12) at 13:00 KST and high values at the time before sunrise and sunset (Figure 9a). As shown in Figure 9b, it showed an RMSE of 0.25 MJ m\({}^{-2}\) or less and similar changes between RMSE and nRMSE overall. In warm seasons (August to September), a high RMSE (0.205 MJ m\({}^{-2}\)) and a nRMSE higher than and 0.194 were shown, but in April, a low RMSE and nRMSE of 0.150 MJ m\({}^{-2}\) and 0.104, were observed, respectively. Considering that RMSE and nRMSE showed similar trends, this was not due to the amount of ESR. Due to the Korean Peninsula's monsoon, broad and thick clouds are frequent in summer, and clear skies are common in spring [47]. As a result, the SSI is contaminated by intense clouds in the summer, and in spring, its accuracy is improved by frequent clear skies. As the SZA increased, it was found that the RMSE decreased and the nRMSE increased (Figure 9c). As the SZA decreased, the amplitude of the ESR and SSI increased, so its RMSE increased. In addition, because it was close to noon, the variation of the SSI according to the change in SZA was low, so the SSI showed high accuracy, with a low nRMSE of 0.124 at an SZA of less than 30 degrees. In contrast, as the SZA increased and the observation time approached sunset and sun rise, the variation in SSI according to the change in SZA was high, so the SSI showed low accuracy, with a high nRMSE of 0.825 at an SZA of more than 85 degrees. Since the ESR was absolutely affected by the SZA, the error characteristic was evident in the SZA. Conversely, because the time of the sunrise and sunset and the SZA according to the local time varies with season, the ESR and SZA would seasonally change even at the same local time. Therefore, the error characteristic shown in Figure 9a differs from the error characteristic shown in Figure 9c. Furthermore, to examine the effect of the observation environment on the GK2A/AMI-derived CNN SSI error, we examined the error with respect to in situ SSI, visibility, daylight, and cloud amount (Figure 10). In terms of in situ SSI, it was found that the bias and nRMSE decreased and the RMSE approximately increased as the in situ SSI increased (Figure 10a). As the in situ SSI increased, the amplitude of the SSI increased, so the RMSE increased and the nRMSE decreased. As a result, the RMSE and nRMSE were 0.094 MJ m\({}^{-2}\) and 0.689 at an in situ SSI of less than 0.2 MJ m\({}^{-2}\), respectively, and the RMSE and nRMSE were 0.250 MJ m\({}^{-2}\) and 0.074 at an in situ SSI of more than 3.4 MJ m\({}^{-2}\), respectively. A negative bias was shown at an in situ SSI of higher than 2.0 MJ m\({}^{-2}\), which indicates that the CNN-based SSI model from the GK2A/AMI underestimated under high-SSI conditions. The tendency of the CNN model to underestimate became stronger as the SSI increased, and an SSI of higher than 3.4 MJ m\({}^{-2}\) showed a clear negative bias of \(-\)0.137 MJ m\({}^{-2}\). As shown in Figure 10b, as the visibility increased, the nRMSE decreased.