Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-6131-2020
© Author(s) 2020. 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-13-6131-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Dec 2020
Model description paper |
| 03 Dec 2020
| 03 Dec 2020
Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1
Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, Indiana, USA
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Mark D. Risser
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Burlen Loring
Computational Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Abdelrahman A. Elbashandy
Computational Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Harinarayan Krishnan
Computational Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Jeffrey Johnson
Cohere Consulting LLC, Seattle, Washington, USA
Christina M. Patricola-DiRosario
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa, USA
John P. O'Brien
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Earth and Planetary Science, University of California, Santa Cruz, California, USA
Ankur Mahesh
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Prabhat
National Energy Research Scientific Computing Center, Lawrence Berkeley National Lab, Berkeley, California, USA
Sarahí Arriaga Ramirez
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Land, Air and Water Resources, University of California, Davis, California, USA
Alan M. Rhoades
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Alexander Charn
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Earth and Planetary Science, University of California, Berkeley, California, USA
Héctor Inda Díaz
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Land, Air and Water Resources, University of California, Davis, California, USA
William D. Collins
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Earth and Planetary Science, University of California, Berkeley, California, USA
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Cited
19 citations as recorded by crossref.
- When Will Humanity Notice Its Influence on Atmospheric Rivers? K. Tseng et al. https://doi.org/10.1029/2021JD036044
- Unsupervised discovery of extreme weather events using universal representations of emergent organization A. Rupe et al. https://doi.org/10.1063/5.0267915
- 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
- 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
- Identifying Eastern US Atmospheric River Types and Evaluating Historical Trends C. Ramseyer et al. https://doi.org/10.1029/2021JD036198
- Back-to-back high category atmospheric river landfalls occur more often on the west coast of the United States Y. Zhou et al. https://doi.org/10.1038/s43247-024-01368-w
- Evaluating Uncertainty and Modes of Variability for Antarctic Atmospheric Rivers C. Shields et al. https://doi.org/10.1029/2022GL099577
- Pushing the frontiers in climate modelling and analysis with machine learning V. Eyring et al. https://doi.org/10.1038/s41558-024-02095-y
- Recent Opposite Trends of Atmospheric Rivers Over East Asia and Western North Pacific Driven by the Pacific Decadal Oscillation W. Huang et al. https://doi.org/10.1029/2023JD039147
- Spatio-temporal variability of atmospheric rivers and associated atmospheric parameters in the Euro-Atlantic region V. Thandlam et al. https://doi.org/10.1007/s00704-021-03776-w
- Assessing changes in atmospheric rivers under stratospheric aerosol injection using ARISE-SAI-1.5 K. Quagraine et al. https://doi.org/10.1088/2752-5295/ade61a
- Increased amplitude of atmospheric rivers and associated extreme precipitation in ultra-high-resolution greenhouse warming simulations A. Nellikkattil et al. https://doi.org/10.1038/s43247-023-00963-7
- Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1 A. Mahesh et al. https://doi.org/10.5194/gmd-17-3533-2024
- Antarctic Atmospheric River Climatology and Precipitation Impacts J. Wille et al. https://doi.org/10.1029/2020JD033788
- Increases in Future AR Count and Size: Overview of the ARTMIP Tier 2 CMIP5/6 Experiment T. O’Brien et al. https://doi.org/10.1029/2021JD036013
- Intense atmospheric rivers can weaken ice shelf stability at the Antarctic Peninsula J. Wille et al. https://doi.org/10.1038/s43247-022-00422-9
- Structural uncertainty in mapping Euro-Atlantic atmospheric rivers obscures understanding of associated meteorological extremes V. Thandlam et al. https://doi.org/10.1038/s41598-025-19685-1
- Exploratory Precipitation Metrics: Spatiotemporal Characteristics, Process-Oriented, and Phenomena-Based L. Leung et al. https://doi.org/10.1175/JCLI-D-21-0590.1
- Global Application of the Atmospheric River Scale B. Guan et al. https://doi.org/10.1029/2022JD037180
19 citations as recorded by crossref.
- When Will Humanity Notice Its Influence on Atmospheric Rivers? K. Tseng et al. https://doi.org/10.1029/2021JD036044
- Unsupervised discovery of extreme weather events using universal representations of emergent organization A. Rupe et al. https://doi.org/10.1063/5.0267915
- 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
- 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
- Identifying Eastern US Atmospheric River Types and Evaluating Historical Trends C. Ramseyer et al. https://doi.org/10.1029/2021JD036198
- Back-to-back high category atmospheric river landfalls occur more often on the west coast of the United States Y. Zhou et al. https://doi.org/10.1038/s43247-024-01368-w
- Evaluating Uncertainty and Modes of Variability for Antarctic Atmospheric Rivers C. Shields et al. https://doi.org/10.1029/2022GL099577
- Pushing the frontiers in climate modelling and analysis with machine learning V. Eyring et al. https://doi.org/10.1038/s41558-024-02095-y
- Recent Opposite Trends of Atmospheric Rivers Over East Asia and Western North Pacific Driven by the Pacific Decadal Oscillation W. Huang et al. https://doi.org/10.1029/2023JD039147
- Spatio-temporal variability of atmospheric rivers and associated atmospheric parameters in the Euro-Atlantic region V. Thandlam et al. https://doi.org/10.1007/s00704-021-03776-w
- Assessing changes in atmospheric rivers under stratospheric aerosol injection using ARISE-SAI-1.5 K. Quagraine et al. https://doi.org/10.1088/2752-5295/ade61a
- Increased amplitude of atmospheric rivers and associated extreme precipitation in ultra-high-resolution greenhouse warming simulations A. Nellikkattil et al. https://doi.org/10.1038/s43247-023-00963-7
- Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1 A. Mahesh et al. https://doi.org/10.5194/gmd-17-3533-2024
- Antarctic Atmospheric River Climatology and Precipitation Impacts J. Wille et al. https://doi.org/10.1029/2020JD033788
- Increases in Future AR Count and Size: Overview of the ARTMIP Tier 2 CMIP5/6 Experiment T. O’Brien et al. https://doi.org/10.1029/2021JD036013
- Intense atmospheric rivers can weaken ice shelf stability at the Antarctic Peninsula J. Wille et al. https://doi.org/10.1038/s43247-022-00422-9
- Structural uncertainty in mapping Euro-Atlantic atmospheric rivers obscures understanding of associated meteorological extremes V. Thandlam et al. https://doi.org/10.1038/s41598-025-19685-1
- Exploratory Precipitation Metrics: Spatiotemporal Characteristics, Process-Oriented, and Phenomena-Based L. Leung et al. https://doi.org/10.1175/JCLI-D-21-0590.1
- Global Application of the Atmospheric River Scale B. Guan et al. https://doi.org/10.1029/2022JD037180
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
Researchers utilize various algorithms to identify extreme weather features in climate data, and we seek to answer this question: given a
plausibleweather event detector, how does uncertainty in the detector impact scientific results? We generate a suite of statistical models that emulate expert identification of weather features. We find that the connection between El Niño and atmospheric rivers – a specific extreme weather type – depends systematically on the design of the detector.
Researchers utilize various algorithms to identify extreme weather features in climate data, and...
