Global sensitivity analysis in an ocean general circulation model: a sparse spectral projection approach

Polynomial chaos (PC) expansions are used to propagate parametric uncertainties in ocean global circulation model. The computations focus on short-time, high-resolution simulations of the Gulf of Mexico, using the hybrid coordinate ocean model, with wind stresses corresponding to hurricane Ivan. A s...

Full description

Saved in:
Bibliographic Details
Published in:Computational geosciences 2012-06, Vol.16 (3), p.757-778
Main Authors: Alexanderian, Alen, Winokur, Justin, Sraj, Ihab, Srinivasan, Ashwanth, Iskandarani, Mohamed, Thacker, William C., Knio, Omar M.
Format: Article
Language:English
Subjects:
Earth and Environmental Science
Earth Sciences
Geotechnical Engineering & Applied Earth Sciences
Hurricanes
Hydrogeology
Mathematical Modeling and Industrial Mathematics
Ocean circulation
Original Paper
Sensitivity analysis
Soil Science & Conservation
Spectrum analysis
Stochastic models
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Polynomial chaos (PC) expansions are used to propagate parametric uncertainties in ocean global circulation model. The computations focus on short-time, high-resolution simulations of the Gulf of Mexico, using the hybrid coordinate ocean model, with wind stresses corresponding to hurricane Ivan. A sparse quadrature approach is used to determine the PC coefficients which provides a detailed representation of the stochastic model response. The quality of the PC representation is first examined through a systematic refinement of the number of resolution levels. The PC representation of the stochastic model response is then utilized to compute distributions of quantities of interest (QoIs) and to analyze the local and global sensitivity of these QoIs to uncertain parameters. Conclusions are finally drawn regarding limitations of local perturbations and variance-based assessment and concerning potential application of the present methodology to inverse problems and to uncertainty management.
ISSN:1420-0597
1573-1499
DOI:10.1007/s10596-012-9286-2