Bayesian Hierarchical Model Characterization of Model Error in Ocean Data Assimilation and Forecasts.

摘要

We seek to focus quantitative uncertainty management attributes of the Bayesian Hierarchical Model (BHM) methodology on the identification, characterization, and evolution of irreducible model error in ocean data assimilation and forecast systems. Our project objectives are designed to build upon experience gained under prior Office of Naval Research (ONR) support. This annual report describes progress attained in projects led by PI Milliff in the first full year of funding. First year results were also presented at a project workshop held at the Courant Institute for Mathematical Sciences, New York University, in November 2011. Objectives addressed in this annual report focus on extensions of a time- and space-dependent vertical error covariance BHM from the Mediterranean Forecast System (MFS) to the Regional Ocean Model System (ROMS) applications in the California Current System (CCS).