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
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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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...
