Machine Learning to improve Clouds in Climate Models

Description: Clouds’ variability determine how much incoming radiation reaches the surface and how much radiation leaves the earth. Global climate models have inherently a poor representation of clouds and therefore contribute to the large biases on these radiative fluxes. The main goal of this project is to design and implement Machine Learning software to improve the representation of clouds with the ultimate goal of reducing radiative flux biases.