12 citations · 13 across the 3 of their papers we have counts for
3 papers
physics.ao-ph2016★ 1 cited
Calibration of the K-Profile Parameterization of ocean boundary layer mixing. Part I: Development
S. E. Zedler, C. S. Jackson, F. Yao +4
In model comparisons with observational data, not all data contain information that is useful for answering a specific science question. If non-relevant or highly uncertain data ar…
stat.ME2015★ 12 cited
Polynomial Chaos-based Bayesian Inference of K-Profile Parametrization in a General Circulation Model of the Tropical Pacific
Ihab Sraj, Sarah E. Zedler, Omar M. Knio +2
The authors present a Polynomial Chaos (PC)-based Bayesian inference method for quantifying the uncertainties of the K-Profile Parametrization (KPP) within the MIT General Circulat…
stat.ME2012
Computer Model Calibration using the Ensemble Kalman Filter
Dave Higdon, Matt Pratola, James Gattiker +6
The ensemble Kalman filter (EnKF) (Evensen, 2009) has proven effective in quantifying uncertainty in a number of challenging dynamic, state estimation, or data assimilation, proble…