Articles | Volume 14, issue 1
Model description paper
08 Jan 2021
Model description paper |  | 08 Jan 2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins

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Cited articles

Allen, M. and Ingram, W.: Constraints on Future Changes in Climate and the Hydrologic Cycle, Nature, 419, 224–32,, 2002. a, b
Bonfanti, C., Stewart, J., Maksimovic, S., Hall, D., Govett, M., Trailovic, L., and Jankov, I.: Detecting Extratropical and Tropical Cyclone Regions of Interest (ROI) in Satellite Data using Deep Learning, available at: (last access: 14 December 2020), 2018a. a
Bonfanti, C., Trailovic, L., Stewart, J., and Govett, M.: Machine Learning: Defining Worldwide Cyclone Labels for Training, 2018 21st International Conference on Information Fusion (FUSION), IEEE,, 2018b. a
Brenowitz, N. D. and Bretherton, C. S.: Prognostic validation of a neural network unified physics parameterization, Geophys. Res. Lett., 45, 6289–6298, 2018. a
Chapman, W., Subramanian, A., Delle Monache, L., Xie, S., and Ralph, F.: Improving Atmospheric River Forecasts With Machine Learning, Geophys. Res. Lett., 46, 10627–10635, 2019. a
Short summary
Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create ClimateNet – an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.