Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 12
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.
https://doi.org/10.5194/gmd-13-6131-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 12
Model description paper
 | 
03 Dec 2020
Model description paper |  | 03 Dec 2020

Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1

Travis A. O'Brien, Mark D. Risser, Burlen Loring, Abdelrahman A. Elbashandy, Harinarayan Krishnan, Jeffrey Johnson, Christina M. Patricola-DiRosario, John P. O'Brien, Ankur Mahesh, Prabhat, Sarahí Arriaga Ramirez, Alan M. Rhoades, Alexander Charn, Héctor Inda Díaz, and William D. Collins

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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 plausible weather 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.
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