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 ($R_T$), −14.5 Wm⁻² at the surface ($R_S$), and 12.1 Wm⁻² in the atmosphere ($R_A$), with regional energy budget differences. The Seoul region had a high surface temperature (289.5 K) and a $R_S$ of −33.4 Wm⁻² (surface emission), whereas the Sokcho region had a low surface temperature (284.7 K) and a $R_S$ 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.

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 (R²) 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 R², Bias, and RMSE for the all cases were 0.903, −2.34, and 52.12 Wm⁻², respectively.

In this study, Himawari-8 Advanced Himawari Imager (AHI) longwave channel data that is sensitive to clouds and absorption gas were used to improve the accuracy of the algorithm used to calculate outgoing longwave radiation (OLR) at the top of the atmosphere. A radiative transfer model with a variety of atmospheric conditions was run using Garand vertical profile data as input data. The results of the simulation showed that changes in AHI channels 8, 12, 15, and 16, which were used to calculate OLR, were sensitive to changes in cloud characteristics (cloud optical thickness and cloud height) and absorption gases (water vapor, O₃, CO₂, aerosol optical thickness) in the atmosphere. When compared to long-term analysis OLR data from 2017, as recorded by the Cloud and Earth’s Radiant Energy System (CERES), the OLR calculated in this study had an annual mean bias of 2.28 Wm⁻² and a root mean square error (RMSE) of 11.03 Wm⁻². The new calculation method mitigated the problem of overestimations in OLR in mostly cloudy and overcast regions and underestimated OLR in cloud-free desert regions. It is also an improvement over the result from the existing OLR calculation algorithm, which uses window and water vapor channels.