## 2 Development of LST Retrieval Algorithm  
In general, numerous ground truth LST data are needed to develop an LST retrieval algorithm using statistical regression methods. However, unlike the SST, the available match-up data (_i.e._, ground-level observational data) for LST over the COMS observing region (Figure 1) are severely limited. Hence, we generated pseudo match-up data through radiative transfer model simulations using MODTRAN4 over a wide range of atmospheric and surface conditions. In this paper, MODTRAN4 [36] has been used, because it represents the state-of-the-art in realistic computing of absorption and scattering in the terrestrial atmosphere at high spectral resolution (1 cm\({}^{-1}\)) over the various infrared spectral ranges, providing accurate simulations of atmospheric radiative transfer [37, 38, 39]. Furthermore, the model was based on the angular dependence of the detected radiance, similar to what has been done in previous studies [2, 31, 32, 40]. We assumed that the sky was completely clear and that the main factors affecting the accuracy of the retrieved LST data using the SW method were atmospheric profiles of temperature and water vapor, surface emissivity, SZA and lapse rate at the surface layer. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Characteristics** & **Visible** & **Shortwave Infrared** & **Water Vapor** & **Infrared1** & **Infrared2** \\ \hline Band width (\(\upmu\)m) & 0.55–0.80 & 3.5–4.0 & 6.50–7.00 & 10.3–11.3 & 11.5–12.5 \\ Band center (\(\upmu\)m) & 0.675 & 3.75 & 6.75 & 10.8 & 12.0 \\ Instantaneous Field of & & & & & \\ View(\(\upmu\)rad) & & & & & \\ Spatial Res. (km) & 1 & 4 & 4 & 4 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Communication, Ocean and Meteorological Satellite (COMS) meteorological imager used in this study. The radiative transfer simulations were designed to include all of the impacting factors (Table 2). To account for atmospheric effects, we used the latest version of the Thermodynamic Initial Guess Retrieval (TIGR) database, version TIGR2000_6 CORPS, which was constructed by the Laboratoire de Meteorologie Dynamique [41]. Of the total TIGR database, only 359 stations were selected for use in this study. These stations are geographically located within 50\({}^{\circ}\) of SZA from the COMS nadir position (see Figure 1). In this study, SZA was calculated for each TIGR data point. In general, the diurnal variations of LST are much greater than those for air temperature (Ta), especially in dry regions, such as desert and semidesert regions. To account for the large diurnal variation of LST, it was set to vary from (Ta - 6) K to (Ta + 16) K in progressive increments equivalent to 2 K. Here, Ta represents the air temperature of the lowest layer in the TIGR data. The surface emissivity for the infrared radiation channel IR1 was set to vary from 0.9478 to 0.9968 in progressive increments of 0.0049, and the emissivity difference between IR1 and IR2 channels was set to vary from \(-\)0.012 to 0.012 in progressive increments of 0.004. The conditions used in this study are similar to [PERSON] and Romaguera [40] and [PERSON] _et al_. [32]. As a result, the number of total simulations amounted to 359 (profiles and SZAs) \(\times\) 12 (surface lapse rates) \(\times\) 11 (emissivity values) \(\times\) 7 (emissivity differences) = 331,716. Figure 1: Spatial distribution of Thermodynamic Initial Guess Retrieval (TIGR) data used in this study. The nadir position of COMS is represented by “-”. The brightness temperatures of the two SW channels were produced using the averaged radiances generated through the radiative transfer model simulations and the COMS spectral response function via an inverse Planck equation (Figure 2). The spectral response functions of COMS IR1 and IR2 are slightly different from those of the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) instrument onboard the Meteosat Second Generation 2 (MSG2) satellite.