## 5 Summary  
We developed an SW-type LST retrieval algorithm from the initial data collected by the Korean geostationary satellite, COMS. The algorithm, termed CSW, was developed through radiative transfer model simulations under various atmospheric profiles derived from TIGR data, SZAs, surface emissivity values and surface lapse rate conditions using MODTRAN4. The estimation capabilities of the CSW LST algorithm were evaluated for various impacting factors, and the retrieval accuracy of COMS LST data was evaluated with the collocated MODIS LST data. For evaluation, averaged \(5\times 5\) MODIS LST data with time differences of less than \(\pm\)5 min were used. The surface emissivity values of the two SW channels were generated by a VCM that assumed pixels were composed of soil and vegetation.  
In general, the CSW algorithm estimated the LST distribution relatively well (averaged bias = 0.00 K, RMSE = 1.41 K, correlation coefficient = 0.99) for the given atmospheric and surface conditions. However, the estimation capabilities of the CSW algorithm were clearly impacted by large BTDs and  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Year Month**} & \multicolumn{3}{c}{**Total**} & \multicolumn{3}{c}{**Daytime**} & \multicolumn{3}{c}{**Nighttime**} \\ \cline{2-10}  & **Corr. Coeff.** & **Bias (K)** & **RMSE (K)** & **Corr. Coeff.** & **Bias (K)** & **RMSE (K)** & **Corr. Coeff.** & **Bias (K)** & **RMSE (K)** \\ \hline \multirow{9}{*}{2011} & 4 & 0.995 & \(-\)1.506 & 2.477 & 0.973 & \(-\)2.177 & 3.187 & 0.982 & \(-\)1.080 & 1.892 \\  & 5 & 0.995 & \(-\)2.421 & 3.056 & 0.966 & \(-\)  
surface lapse rates, particularly during the early morning hours and at noon. The impacts of large BTDs and surface lapse rates were enhanced by SZA. However, the impacts of surface emissivity on two thermal channels were not systematic and easily discernible. The VCM method reproduced spatiotemporal variations of surface emissivity reasonably well. The emissivity of IR2 was slightly greater than that of IR1 over the whole study area, but this difference was especially pronounced over desert and sparsely vegetated regions.  
The CSW algorithm was able to reasonably retrieve the spatially distributed LST data compared to MODIS LST data irrespective of what months or time of day the data were collected. The one-year evaluation results with the collocated MODIS LST data showed that the retrieval capabilities of the CSW algorithm are more or less promising (annual mean bias = \(-1.009\) K, RMSE = \(2.613\) K, correlation coefficient = \(0.988\)). However, there were some relatively large variations in the monthly mean bias and RMSE for a few different months and times of day. These results suggest that the retrieval capabilities of the CSW algorithm could benefit from further improvements prior to using the algorithm in various applications, including regeneration of coefficients based on the critical impacting factors and investigation of the co-linearity between MODIS and COMS LST products. Additionally, the collection of more _in situ_ observations of LST for different land covers and weather conditions could greatly help to improve the future assessments of the quantitative accuracy of the COMS LST data, because the MODIS LST data is not ground truth data [16, 28, 40, 55].  
This research was supported by the Korean Meteorological Administration Research and Development Program under Grant CATER 2012-2067. The one-year COMS Level-1B data and MODIS LST data were provided by the National Meteorological Satellite Center, the Korean Meteorological Administration and the National Aeronautics and Space Administration, respectively.  
The authors declare no conflict of interest.