Combining Crowd-sourcing and Deep Learning to Understand Meso-scale Organization of Shallow Convection

Shallow clouds play a large role for the Earth's energy balance. Therefore, understanding how these clouds organize is crucial in order to understand their effect in a changing climate. In this study we subjectively defined four common patterns of organization from satellite images: Sugar, Flower, Fish and Gravel. On cloud labeling days at two institutes, 67 participants classified more than 30,000 satellite images on a crowd-sourcing platform. Physical analysis reveals that the four patterns are associated with distinct large-scale environmental conditions. We then used the classifications as a training set for deep learning algorithms, which learned to detect the cloud patterns with human accuracy. This enables analysis much beyond the human classifications. As an example, we created global climatologies of the four patterns. These reveal geographical hotspots that provide insight into the interaction of mesoscale cloud organization with the large-scale circulation. Our project shows that combining crowd-sourcing and deep learning opens new data-driven ways to explore cloud-circulation interactions and serves as a template for a wide range of possible studies in the geosciences.