The top-of-atmosphere(TOA)Earth-reflected shortwave radiation flux is a crucial component of the Earth’s radiation budget(ERB).The Earth polychromatic imaging camera onboard the Deep Space Climate Observatory(DSCOVR/...The top-of-atmosphere(TOA)Earth-reflected shortwave radiation flux is a crucial component of the Earth’s radiation budget(ERB).The Earth polychromatic imaging camera onboard the Deep Space Climate Observatory(DSCOVR/EPIC)provides a unique Earth observation perspective from the Sun-Earth Lagrange point 1.Traditionally,angular distribution models(ADMs)are required to account for the radiation anisotropy.However,no ADMs are available intended for DSCOVR/EPIC,while the development and application of ADMs involved complicated procedures.With an attempt to simplify and improve the derivation of TOA shortwave flux,this study proposed a machine learning-based approach to interpret the DSCOVR/EPIC data and evaluate its application potential in monitoring the global shortwave flux.With the Clouds and the Earth’s Radiant Energy System(CERES)products as the benchmark,36 neural network models were developed for each scene type and air condition to estimate the TOA shortwave flux from DSCOVR/EPIC,and a model was then developed to relate the EPIC-derived flux to global TOA shortwave flux.Results showed that the neural network models worked well in estimating the TOA shortwave flux,which produced consistent spatial distributions with CERES products across scene level,daily,and monthly scales.With the developed models,the EPIC-derived flux could account for 97%variations of global TOA shortwave flux at the daily scale,which were better than the EPIC-L2 albedo product.Overall,this study demonstrated the promising potential of deep space-based Earth observation for ERB monitoring,and the proposed method holds potentials for geostationary satellites and Moon-based Earth observations.展开更多
基金funded by the National Natural Science Foundation of China(grant no.42371337)the Guangdong Basic and Applied Basic Research Foundation(grant no.2023 A1515011946 and 2024A1515011388)+1 种基金the Shenzhen Science and Technology Program(grant no.JCYJ20230808105709020 and JCYJ20240813142621029)the Shenzhen Municipal Government Investment Project(no.2106-440300-04-03-901272).
文摘The top-of-atmosphere(TOA)Earth-reflected shortwave radiation flux is a crucial component of the Earth’s radiation budget(ERB).The Earth polychromatic imaging camera onboard the Deep Space Climate Observatory(DSCOVR/EPIC)provides a unique Earth observation perspective from the Sun-Earth Lagrange point 1.Traditionally,angular distribution models(ADMs)are required to account for the radiation anisotropy.However,no ADMs are available intended for DSCOVR/EPIC,while the development and application of ADMs involved complicated procedures.With an attempt to simplify and improve the derivation of TOA shortwave flux,this study proposed a machine learning-based approach to interpret the DSCOVR/EPIC data and evaluate its application potential in monitoring the global shortwave flux.With the Clouds and the Earth’s Radiant Energy System(CERES)products as the benchmark,36 neural network models were developed for each scene type and air condition to estimate the TOA shortwave flux from DSCOVR/EPIC,and a model was then developed to relate the EPIC-derived flux to global TOA shortwave flux.Results showed that the neural network models worked well in estimating the TOA shortwave flux,which produced consistent spatial distributions with CERES products across scene level,daily,and monthly scales.With the developed models,the EPIC-derived flux could account for 97%variations of global TOA shortwave flux at the daily scale,which were better than the EPIC-L2 albedo product.Overall,this study demonstrated the promising potential of deep space-based Earth observation for ERB monitoring,and the proposed method holds potentials for geostationary satellites and Moon-based Earth observations.
