Quantifying Prediction Fidelity in Ocean Circulation Models

Abstract

The development, validation and application of robust uncertainty quantification methods to ocean modeling, forecasting, and parameter estimation. OBJECTIVESThis project explores the use of Polynomial Chaos (PC) expansions for improving our understanding of uncertainties in Ocean General Circulation Models (OGCM). Reliable ocean forecasts require an objective, practical and accurate methodology to assess the inherent uncertainties associated with the model and data used to produce these forecasts. OGCMs uncertainties stem from several sources that include: physical approximation of the governing equations; discretization and modeling errors; an incomplete set of sparse (and often noisy) observations to constrain the initial and boundary conditions of the model; and uncertainties in surface momentum and buoyancy fluxes.