Articles | Volume 13, issue 12
Geosci. Model Dev., 13, 6131–6148, 2020
Geosci. Model Dev., 13, 6131–6148, 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

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

TECA Bayesian AR Detector Training (Version TECA-BARD-v1.0.1) Travis A. O'Brien

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.