Improving prediction of marine low clouds with cloud droplet number concentration and a deep learning method

Marine low clouds have a pronounced cooling effect on the climate system because of their large cloud fraction (CF) and high albedo. However, predicting marine low clouds with satellite data remains challenging due to the non-linear response of marine low clouds to cloud-controlling factors (CCFs) and the ignorance of cloud droplet number concentration (Nd). Here, we developed a unified convolutional neural network (CNN) incorporating meteorology and Nd as CCFs to predict critical properties of marine low clouds, such as CF, albedo, and cloud radiative effects (CRE). Our CNN model excels in capturing the variability of these cloud properties, achieving over 70% variance explanation for daily 1x1 degree areas, surpassing previous studies. It also effectively replicates geographical patterns of CF, albedo, and CRE, including climatology and long-term trends from 2003 to 2022. This research underscores the significant potential of deep learning in deep exploitation of the information content of the data and, thus, advancing our understanding of aerosol-cloud interactions, a pioneering effort in the field.