Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-107-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-107-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
08 Jan 2021
Model description paper |
| 08 Jan 2021
| 08 Jan 2021
ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
Prabhat
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
Karthik Kashinath
CORRESPONDING AUTHOR
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Mayur Mudigonda
Terrafuse, Berkeley, CA, USA
Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
Lukas Kapp-Schwoerer
ETH Zurich, Zürich, Switzerland
Andre Graubner
ETH Zurich, Zürich, Switzerland
Ege Karaismailoglu
ETH Zurich, Zürich, Switzerland
Leo von Kleist
ETH Zurich, Zürich, Switzerland
Thorsten Kurth
NVIDIA, Santa Clara, CA, USA
Annette Greiner
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Ankur Mahesh
Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Kevin Yang
Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
Colby Lewis
Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
Jiayi Chen
Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
Andrew Lou
Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
Sathyavat Chandran
Department of Computer Science, Rice University, Houston, TX, USA
Ben Toms
Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA
Will Chapman
Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
Katherine Dagon
National Center for Atmospheric Research, Boulder, CO, USA
Christine A. Shields
National Center for Atmospheric Research, Boulder, CO, USA
Travis O'Brien
Department of Atmospheric Science, Indiana University, Bloomington, IN, USA
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Michael Wehner
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
William Collins
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
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- EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model J. Quinting & C. Grams https://doi.org/10.5194/gmd-15-715-2022
- Constraining and Characterizing the Size of Atmospheric Rivers: A Perspective Independent From the Detection Algorithm H. Inda‐Díaz et al. https://doi.org/10.1029/2020JD033746
- A Customized Multi‐Scale Deep Learning Framework for Storm Nowcasting S. Yang & H. Yuan https://doi.org/10.1029/2023GL103979
- Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set T. Higgins et al. https://doi.org/10.1029/2022MS003495
- Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO T. Wang & P. Huang https://doi.org/10.1007/s00376-023-3001-1
- Atmospheric rivers and Australian precipitation: Impact of detection algorithm choice S. Pradhan et al. https://doi.org/10.1016/j.jhydrol.2024.132586
- Human‐in‐the‐Loop Segmentation of Earth Surface Imagery D. Buscombe et al. https://doi.org/10.1029/2021EA002085
- Challenges and opportunities for a hybrid modelling approach to earth system science S. See & J. Adie https://doi.org/10.1007/s42514-021-00071-y
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- Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook M. Botifoll et al. https://doi.org/10.1039/D2NH00377E
- AtmoDist: Self-supervised representation learning for atmospheric dynamics S. Hoffmann & C. Lessig https://doi.org/10.1017/eds.2023.1
- Global asymmetries in the moisture origins of atmospheric river precipitation A. Crespo-Otero et al. https://doi.org/10.1038/s41612-026-01408-6
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- A large-scale, multitask, multisensory dataset for climate-aware crop monitoring in the US from 2018–2022 A. Höhl et al. https://doi.org/10.1038/s41597-026-06611-x
- A Comparison between the Kuroshio Extension and Pineapple Express Atmospheric Rivers Affecting the West Coast of North America S. Wang et al. https://doi.org/10.1175/JCLI-D-21-0554.1
- Atmospheric river, a term encompassing different meteorological patterns L. Gimeno et al. https://doi.org/10.1002/wat2.1558
- Application of Deep Learning to Understanding ENSO Dynamics N. Shin et al. https://doi.org/10.1175/AIES-D-21-0011.1
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- The Multi‐Scale Interactions of Atmospheric Phenomenon in Mean and Extreme Precipitation A. Prein et al. https://doi.org/10.1029/2023EF003534
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- Intercomparison of Deep Learning Architectures for the Prediction of Precipitation Fields With a Focus on Extremes N. Otero & P. Horton https://doi.org/10.1029/2023WR035088
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- Atmospheric rivers cause warm winters and extreme heat events S. Scholz & J. Lora https://doi.org/10.1038/s41586-024-08238-7
- Can machine learning models trained using atmospheric simulation data be applied to observation data? D. Matsuoka & J. Carley https://doi.org/10.1017/exp.2022.3
- Using Machine Learning to Analyze Physical Causes of Climate Change: A Case Study of U.S. Midwest Extreme Precipitation F. Davenport & N. Diffenbaugh https://doi.org/10.1029/2021GL093787
- EarthNets: Empowering artificial intelligence for Earth observation Z. Xiong et al. https://doi.org/10.1109/MGRS.2024.3466998
- Bridging the Climate Gap: Multimodel Framework With Explainable Decision-Making for IOD and ENSO Forecasting H. Tiwari et al. https://doi.org/10.1109/TAI.2024.3489535
- Cyclone detection with end-to-end super resolution and faster R-CNN M. Moustafa et al. https://doi.org/10.1007/s12145-024-01281-y
- Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg https://doi.org/10.5194/gmd-16-6433-2023
- Unsupervised discovery of extreme weather events using universal representations of emergent organization A. Rupe et al. https://doi.org/10.1063/5.0267915
Saved (final revised paper)
Latest update: 11 Jun 2026
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.
Detecting extreme weather events is a crucial step in understanding how they change due to...
