Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-6131-2020
https://doi.org/10.5194/gmd-13-6131-2020
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, 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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Interactive discussion

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Lorena Grabowski on behalf of the Authors (30 Jul 2020)  Author's response
ED: Referee Nomination & Report Request started (13 Aug 2020) by Christina McCluskey
RR by Anonymous Referee #1 (24 Aug 2020)
ED: Publish as is (16 Oct 2020) by Christina McCluskey
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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.