the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1
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
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
plausibleweather 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.