Articles | Volume 13, issue 12
Geosci. Model Dev., 13, 6131–6148, 2020
https://doi.org/10.5194/gmd-13-6131-2020
Geosci. Model Dev., 13, 6131–6148, 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 et al.

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Interactive discussion

Status: closed
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.