Deep learning for detecting extreme weather patterns

Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e. pixel-level classification) have remained challenging problems in the weather and climate sciences. Deep learning has shown remarkable success in similar problems in computer vision, robotics, and other domains. In this chapter we take a look at various deep learning models which attempt to solve identification, detection, localization and segmentation as applied to climate data. We conclude with open challenges for the field.