## 1 Introduction
[23] estimated GHI using their own physical model, which is called the CLAVR-x model. Still, as far as we know, only one such study has employed the semi-empirical method. [PERSON] et al. [15] added a preprocessing algorithm into the Rigollier model to distinguish cloud from ground. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Area**} & \multicolumn{2}{c}{**Köppen Climate**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**RMSE (W/m\({}^{2}\))**} & \multirow{2}{*}{**rRMSE (\%)**} & \multirow{2}{*}{**Satellite System**} \\  & & & **Classification** & & & & **System** \\ \hline [PERSON] et al. [15] & 2015 & Korea & Continental (Dwa) & Semi-empirical & \(-\) & 30.8 & COMS \\ \hline [PERSON] et al. [6] & 2018 & French Guiana & Tropical (Af) & Semi-empirical & 133 & 30 & GOES \\ \hline \multirow{3}{*}{[PERSON] et al. [4]} & \multirow{3}{*}{2020} & \multirow{3}{*}{[PERSON]} & \multirow{3}{*}{Continental (Dwa)} & \multirow{3}{*}{Semi-empirical} & \multirow{3}{*}{\begin{tabular}{c} 181 \\ (1 h ahead) \\ 200 \\ 215 \\ \end{tabular} } & \multirow{3}{*}{\(-\)} & \multirow{3}{*}{Fengyun-4A} \\  & & & & & \\  & & & & & \\ \hline [PERSON] et al. [16] & 2021 & \begin{tabular}{c} Northern \\ China \\ \end{tabular} & Continental (Dwa) & Semi-empirical & 109 & \(-\) & Fengyun-4A \\ \hline [PERSON] et al. [17] & 2012 & Korea & Continental (Dwa) & Physical & \begin{tabular}{c} 71 (clear-sky) \\ 90 (cloudy) \\ \end{tabular} & \(-\) & MTSAT-1R \\ \hline [PERSON] et al. [18] & 2013 & World & All classifications & Physical & 35 & 7 & Terra EOS \\ \hline [PERSON] et al. [19] & 2014 & Korea & Continental (Dwa) & Physical & 85 & \(-\) & COMS \\ \hline [PERSON] et al. [20] & 2016 & \begin{tabular}{c} Europe and \\ Africa \\ \end{tabular} & All classifications & Physical & 90 & 18 & Meteosat-2 \\ \hline [PERSON] et al. [21] & 2016 & United States & \begin{tabular}{c} Dry (All subtypes) \\ Temperature (All subtypes) \\ Continental (All subtypes) \\ Continental (All subtypes) \\ \end{tabular} & Physical & 130 & \(-\) & \(-\) \\ [PERSON] et al. [22] & 2016 & Korea & Continental (Dwa) & Physical & \
\begin{tabular}{c} 72 (clear-sky) \\ 106 (cloudy) \\ \end{tabular} & \(-\) & COMS \\ \hline [PERSON] et al. [23] & 2016 & Korea & Continental (Dwa) & Physical &
\begin{tabular}{c} 65 (clear-sky) \\ 149 (cloudy) \\ \end{tabular} & \(13\) & \multirow{3}{*}{COMS} \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Considering the importance of semi-empirical models and the lack of relevant studies evaluating them in the hot summer continental climates, the present study focuses on two objectives. The first objective of this study is to conduct a comparative analysis of the existing semi-empirical solar irradiance models using data from Korea, and thus identify a semi-empirical model that is suitable for the hot summer continental climate. The second objective is to investigate the improvements in accuracy that could be achieved by combining formulas from various models and by applying a machine learning technique for replacing climate-dependent coefficients in the semi-empirical model. The fulfillment of these objectives will contribute to accurately estimating a regional database of solar radiation for the hot summer continental climate. This paper consists of five sections. Section 2 presents ground station measurement of solar irradiance data, COMS satellite images, and AERONET data employed in this study. In Section 3, semi-empirical modeling methods, which include calculations of the cloud index, clear-sky irradiance, and GHI conversion, as well as the machine learning method, are explained. In addition, error metrics for accuracy evaluation of each model are listed. Detailed results, analyses, and discussions are presented in Section 4. Finally, Section 5 provides a summary of the major findings from the study.