*Hideaki Takenaka1, Teruyuki Nakajima1, Takashi Y Nakajima2, Akiko Higurashi3, Makiko Hashimoto1, Chong Shi1, Kentarou Suzuki4, Junya Uchida4, Yoshiro Yamamoto2, Ken T Murata5, Yohei Yamaguchi6, Alessandro Damiani7, Pradeep Khatri8, Hitoshi Irie7, Atsushi Higuchi7, Weile Wang 9, Hirofumi Hashimoto9, Ramakrishna R Nemani9
(1.JAXA/EORC, 2.TRIC, Tokai Univ., 3.NIES, 4.AORI/CCSR, 5.NICT, 6.Osaka Univ., 7.CEReS, Chiba Univ., 8.Tohoku Univ., 9.NASA Ames Research Center )
Keywords:Satellite remote sensing , Solar radiation, Renewable energy, Photovoltaic power
Earth getting warm by incoming solar radiation and emitting the thermal energy to space by outgoing terrestrial radiation. Clouds can cool the Earth by reflecting solar radiation but also maintain warmth by absorbing and emitting terrestrial radiation. similarly aerosols have an effect on radiation budget by absorption and scattering of Solar radiation. Therefore it is important to estimate the earth's radiation budget accurately based on observation for understanding of climate. In recent years, how to introduce the photovoltaic power generation/renewable energy to electric power grid has been discuss. The surface downwelling solar energy has instantaneous change by weather phenomena. It need accurate estimate technique for Nowcast and Short-term forecast of solar radiation. In this study, we developed the high speed and accurate algorithm for shortwave (SW) radiation budget and it's applied to geostationary satellite for rapid analysis. This technique enabled highly accurate monitoring of solar radiation and photo voltaic (PV) power generation. We update the algorithm by new radiative transfer solver by Neural Network. Learning Algorithm plus (LA+) accelerate advanced remote sensing technique by Active learning and NNN. This presentation provides introduce of solar radiation estimation algorithm, user interface "AMATERASS GIS", and approach of estimation of energy demand based on human activity. (This research was supported in part by CREST/EMS/JST)