## 4 Results
### Evaluation against KMA ASOS Stations  
Based on the theoretical principle and lookup table, the SSI had been derived from the GK2A/AMI as an operational product in real time. Therefore, to evaluate the accuracy of the CNN model for estimating the SSI around Korea in this study, the accuracy of the GK2A/AMI operational SSI was simultaneously verified. For quantitative validation, we compared the hourly GK2A/AMI-derived SSI based on the CNN and the operational GK2A/AMI-derived SSI around Korea with the in situ SSI measured by the KMA ASOS stations from 1 August 2020 to 31 January 2022 (Figure 6). The total number of data matchups was 284,393. The accuracy of the hourly SSI derived from the GK2A/AMI operational algorithm showed an RMSE of 0.318 MJ m\({}^{-2}\) and a Pearson's \(R\) of 0.949; however, the accuracy of the hourly SSI derived from the CNN model showed an RMSE of 0.180 MJ m\({}^{-2}\) and a Pearson's \(R\) of 0.982, which indicated that the ML approach showed higher accuracies compared to the GK2A/AMI operational algorithm. Regarding bias, the GK2A/AMI operational algorithm tended to overestimate the SSI as compared to the in situ measurements, with a bias of 0.118 MJ m\({}^{-2}\). Otherwise, the CNN model tended to underestimate the SSI, showing biases of \(-\)0.007 MJ m\({}^{-2}\). Regardless of positive and negative bias, the magnitude of the bias errors was larger in the GK2A/AMI operational algorithm, which indicated that the CNN model showed better performance considering bias errors. For the period from 1 August 2020 to 31 January 2022, we verified the accuracy of the GK2A/AMI hourly SSI, as derived from the operational algorithm and the CNN model, by comparing them with in situ SSI measurements from each KMA ASOS station (Figure 7). For the operational algorithm, the RMSE values ranged from 0.210 MJ m\({}^{-2}\) (at station 99) to 0.420 MJ m\({}^{-2}\) (at station 185); the bias ranged from 0.008 MJ m\({}^{-2}\) (at station 165) to 0.323 MJ m\({}^{-2}\) (at station 185); and the Pearson's \(R\) ranged from 0.924 (at station 115) to 0.967 (at station 185). Conversely, for the CNN model, the RMSE values ranged from 0.091 MJ m\({}^{-2}\) (at station 137) to 0.314 MJ m\({}^{-2}\) (at station 169); the bias ranged from \(-\)0.082 MJ m\({}^{-2}\) (at station 102) to 0.051 MJ m\({}^{-2}\) (at station 146); and the Pearson's \(R\) ranged from 0.946 (at station 169) to 0.991 (at station 137). Overall, the CNN model showed lower RMSEs and higher R values than the operational algorithm at all KMA ASOS stations, which indicates that the CNN model estimated the SSI more accurately. Figure 6: Validation result of the estimated SSI from GK2A/AMI and ground-based SSI from KMA ASOS stations, for the period from 1 August 2020 to 31 January 2022, using (**a**) the operational algorithm and (**b**) CNN, respectively; the color indicates the point density relative to the total matchups. In particular, at stations 115, 169, and 172 (hereafter referred to as group 1), regardless of the model, high RMSEs and low values of Pearson's \(R\) were observed compared to other stations. In contrast, at stations 112, 168, 184, and 185 (hereafter referred to as group 2), the CNN showed a low RMSE, and the operational algorithm showed a high RMSE, which could have been caused by a high positive bias. Station 172 was located over land between station 251 and station 252. Although the stations among group 1 and 2, excluding station 172, were located over coastal regions or islands, they showed good performance. Therefore, the low performance for group 1 and 2 was not due to the impact from nearby water. The operational product of GK2A/AMI estimated the SSI not by considering neighboring pixels but based only on the pixel equivalent to the area. However, the CNN model characterized the surrounding environment based on neighboring pixels.