Characterizing and visualizing predictive uncertainty in numerical ensembles through Bayesian model averaging.
Numerical ensemble forecasting is a powerful tool that drives many risk analysis efforts and decision making tasks. These ensembles are composed of individual simulations that each uniquely model a possible outcome for a common event of interest: e.g., the direction and force of a hurricane, or the path of travel and mortality rate of a pandemic. This paper presents a new visual strategy to help quantify and characterize a numerical ensemble's predictive uncertainty: i.e., the ability for ensemble constituents to accurately and consistently predict an event of interest based on ground truth observations. Our strategy employs a Bayesian framework to first construct a statistical aggregate from the ensemble. We extend the information obtained from the aggregate with a visualization strategy that characterizes predictive uncertainty at two levels: at a global level, which assesses the ensemble as a whole, as well as a local level, which examines each of the ensemble's constituents. Through this approach, modelers are able to better assess the predictive strengths and weaknesses of the ensemble as a whole, as well as individual models. We apply our method to two datasets to demonstrate its broad applicability.
Pacific Northwest National Laboratory.
Luke
L
Gosink
Kevin
K
Bensema
Trenton
T
Pulsipher
Harald
H
Obermaier
Michael
M
Henry
Hank
H
Childs
Kenneth I
KI
Joy
D000465:Algorithms; D001499:Bayes Theorem; D003196:Computer Graphics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface