  Solar irradiance is fundamental data crucial for analyses related to weather
and climate. High-precision estimation models are necessary to create areal
data for solar irradiance. In this study, we developed a novel estimation model
by utilizing machine learning and multiband data from meteorological satellite
observations. Particularly under clear-sky and thin clouds, satellite
observations can be influenced by surface reflections, which may lead to
overfitting to ground observations. To make the model applicable at any
location, we constructed the model incorporating prior information such as
radiative transfer models and clear-sky probability, based on physical and
meteorological knowledge. As a result, the estimation accuracy significantly
improved at validation sites.
