Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects.

There is a lack of satellite-based aerosol retrievals in the vicinity of low-topped clouds, mainly because reflectance from aerosols is overwhelmed by three-dimensional cloud radiative effects. To account for cloud radiative effects on reflectance observations, we develop a Convolutional Neural Network and retrieve aerosol optical depth (AOD) with 100-500 m horizontal resolution for all cloud-free regions regardless of their distances to clouds. The retrieval uncertainty is 0.

EPIC Spectral Observations of Variability in Earth's Global Reflectance.

NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR) satellite observes the entire sunlit Earth every 65 to 110 min from the Sun-Earth Lagrangian L1 point. This paper presents initial EPIC shortwave spectral observations of the sunlit Earth reflectance and analyses of its diurnal and seasonal variations. The results show that the reflectance depends mostly on (1) the ratio between land and ocean areas exposed to the Sun and (2) cloud spatial and temporal distributions over the sunlit side of Earth.

Testing the Two-Layer Model for Correcting Near Cloud Reflectance Enhancement Using LES/SHDOM Simulated Radiances.

A transition zone exists between cloudy skies and clear sky, such that clouds scatter solar radiation into clear sky regions. From a satellite perspective, it appears that clouds enhance the radiation nearby. We seek a simple method to estimate this enhancement, since it is so computationally expensive to account for all 3-dimensional (3D) scattering processes.