ClimateNet: Bringing the power of Deep Learning to weather and climate sciences via open datasets and architectures (Research Track)
Karthik Kashinath (Lawrence Berkeley National Laboratory); Mayur Mudigonda (UC Berkeley); Kevin Yang (UC Berkeley); Jiayi Chen (UC Berkeley); Annette Greiner (Lawrence Berkeley National Laboratory); Mr Prabhat (Lawrence Berkeley National Laboratory)
Abstract
Pattern recognition tasks such as classification, object detection and segmentation have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting weather patterns and extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of Deep Learning in tackling similar problems in computer vision, we advocate a DL-based approach. However, DL works best in the context of supervised learning, when labeled datasets are readily available. Reliable, labeled training data is scarce in climate science. `ClimateNet' is an effort to solve this problem by creating open, community-sourced expert-labeled datasets that capture information pertaining to class or pattern labels, bounding boxes and segmentation masks. In this paper we present the motivation, design and status of the ClimateNet dataset and associated model architecture.