An Automatic Cloud Observation System (ACOS) and cloud cover calculation algorithm were developed to calculate the cloud cover at night, and the calculation results were compared with the cloud cover data of a manned observatory (Daejeon Regional Office of Meteorology, DROM) that records human observations. Annual and seasonal analyses were conducted using the 1900–0600 local standard time (LST) hourly data from January to December 2019. Prior to calculating the cloud cover of ACOS, pre-processing was performed by removing surrounding obstacles and correcting the distortion caused by the fish-eye lens. In addition, the red–blue ratio (RBR) threshold was determined, according to the image characteristics (RBR and luminance) using the red, green, and blue (RGB) brightness value of the area in which the solar zenith angle (SZA) was less than 80°, to calculate the cloud cover. The calculated cloud cover exhibited a bias of −0.28 tenths, root mean square error (RMSE) of 1.78 tenths, and a correlation coefficient of 0.91 for DROM across all cases. The frequency of the cases that exhibited differences less than 1 tenth between the observed and calculated cloud cover was 46.82%, while the frequency of cases that exhibited differences less than 2 tenths was 87.79%.

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This study developed a retrieval algorithm for reflected shortwave radiation at the top of the atmosphere (RSR). This algorithm is based on Himawari-8/AHI (Advanced Himawari Imager) whose sensor characteristics and observation area are similar to the next-generation Geostationary Korea Multi-Purpose Satellite/Advanced Meteorological Imager (GK-2A/AMI). This algorithm converts the radiance into reflectance for six shortwave channels and retrieves the RSR with a regression coefficient look-up-table according to geometry of the solar-viewing (solar zenith angle, viewing zenith angle, and relative azimuth angle) and atmospheric conditions (surface type and absence/presence of clouds), and removed sun glint with high uncertainty. The regression coefficients were calculated using numerical experiments from the radiative transfer model (SBDART), and ridge regression for broadband albedo at the top of the atmosphere (TOA albedo) and narrowband reflectance considering anisotropy. The retrieved RSR were validated using Terra, Aqua, and S-NPP/CERES data on the 15th day of every month from July 2015 to February 2017. The coefficient of determination (R 2 ) between AHI and CERES for scene analysis was higher than 0.867 and the Bias and root mean square error (RMSE) were −21.34-5.52 and 51.74-59.28 Wm −2 . The R 2 , Bias, and RMSE for the all cases were 0.903, −2.34, and 52.12 Wm −2 , respectively.

In this study, a radiation component calculation algorithm was developed using channel data from the Himawari-8 Advanced Himawari Imager (AHI) and meteorological data from the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS). In addition, the energy budget of the Korean Peninsula region in 2016 was calculated and its regional differences were analyzed. Radiation components derived using the algorithm were calibrated using the broadband radiation component data from the Clouds and the Earth’s Radiant Energy System (CERES) to improve their accuracy. The calculated radiation components and the CERES data showed an annual mean percent bias of less than 3.5% and a high correlation coefficient of over 0.98. The energy budget of the Korean Peninsula region was −2.4 Wm−2 at the top of the atmosphere (RT), −14.5 Wm−2 at the surface (RS), and 12.1 Wm−2 in the atmosphere (RA), with regional energy budget differences. The Seoul region had a high surface temperature (289.5 K) and a RS of −33.4 Wm−2 (surface emission), whereas the Sokcho region had a low surface temperature (284.7 K) and a RS of 5.0 Wm−2 (surface absorption), for a difference of 38.5 Wm−2. In short, regions with relatively high surface temperatures tended to show energy emission, and regions with relatively low surface temperatures tended to show energy absorption. Such regional energy imbalances can cause weather and climate changes and bring about meteorological disasters, and thus research on detecting energy budget changes must be continued.