Pattern recognition tasks such as classification, localization, object detection and segmentation have remained challenging problems in the weather and climate sciences. Now, a team at the Lawrence Berkeley National Laboratory is developing ClimateNet, a project that will bring the power of deep learning methods to identify important weather and climate patterns via expert-labeled, community-sourced open datasets and architectures.