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.

Data sets

Expert AR Detector Counts (Version 1.0) Travis A. O'Brien, Christina M. Patricola, John P. O'Brien, Ankur Mahesh, Sarahi Arriaga-Ramirez, Alan M. Rhoades, Alexander Charn, and Hector Inda-Diaz https://doi.org/10.5281/zenodo.4130559

Model code and software

Toolkit for Extreme Climate Analysis - TECA Bayesian AR Detector v1.0.1 Burlen Loring, Travis A. O'Brien, Abdelrahman E. Elbashandy, Jeffrey N. Johnson, Harinarayan Krishnan, Noel Keen, Prabhat, and CASCADE SFA https://doi.org/10.5281/zenodo.4130468

TECA Bayesian AR Detector Training (Version TECA-BARD-v1.0.1) Travis A. O'Brien https://doi.org/10.5281/zenodo.4130486

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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.