Improving CERES Low-Latency Surface Radiation Fluxes with Machine/Deep Learning
Description
In conjunction with sophisticated radiative transfer simulations, the CERES (Clouds and the Earth's Radiant Energy System) team is using machine and deep learning methods to improve near real-time surface radiative fluxes for clean energy, infrastructure energy use, and agricultural applications.
Detailed example
improve near real-time surface radiative fluxes
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
In conjunction with sophisticated radiative transfer simulations, the CERES (Clouds and the Earth's Radiant Energy System) team is using machine and deep learning methods to improve near real-time surface radiative fluxes for clean energy, infrastructure energy use, and agricultural applications.
Controls / human review
ATO: Not reported; PIA: Not published