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

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.