Abstract Scientific computations for the quantification, estimation and prediction of uncertainties for ocean dynamics are devel-10 oped and exemplified. Primary characteristics of ocean data, models and uncertainties are reviewed and quantitative data 11 assimilation concepts defined. Challenges involved in realistic data-driven simulations of uncertainties for four-dimen-12 sional interdisciplinary ocean processes are emphasized. Equations governing uncertainties in the Bayesian probabilistic 13 sense are summarized. Stochastic forcing formulations are introduced and a new stochastic-deterministic ocean model 14 is presented. A computational methodology and numerical system, Error Subspace Statistical Estimation, for the efficient 15 estimation and prediction of oceanic uncertainties based on these equations is then outlined. Capabilities of the ESSE sys-16 tem are illustrated in three data-assimilative applications: estimation of uncertainties for physical-biogeochemical fields, 17 transfers of ocean physics uncertainties to acoustics, and real-time stochastic ensemble predictions with assimilation of 18 a wide range of data types. Relationships with other modern uncertainty quantification schemes and promising research 19 directions are discussed. 20
In REF , a rigorous computational method for the quantification, prediction, and estimation of uncertainties called Error Subspace Statistical Estimation (ESSE) is described.
Uncertainty estimation and prediction for interdisciplinary ocean dynamics
J. Comput. Phys.
J. Comput. Phys.
Computer Science Mathematics