Bayesian Hierarchical Models to Augment the Mediterranean Forecast System
Abstract
Eighteen months into the project, the long-term goals and objectives remain as stated in the progress report last year. Our goal is to develop an ensemble ocean forecast methodology, using Bayesian Hierarchical Modelling (BHM) tools. The ocean ensemble forecast methods to be developed should be practical enough to benefit the Mediterranean Forecast System (MFS) in its operational mode, and they should demonstrate forecast uncertainties during difficult to predict regime transitions in the Mediterranean Sea (e.g. the Fall transition, deep water formation). Two main objectives comprise the research plan. First, an ensemble of ocean initial conditions will be derived from realizations of the surface wind forcing as drawn from a posterior distribution of a BHM for the surface wind process. The surface wind field realizations have been used in separate data assimilation steps to produce unique, but realizable ocean initial conditions. The surface wind likelihood distributions are based on QuikSCAT data and ECMWF analyses. The prior distributions are based on a time-dependent augmentation of the stochastic geostrophy model introduced by Royle et al. (1998). The second objective involves the accurate representation of forecast error covariance evolution in MFS. The operational implementation of a reduced order optimal interpolation (ROOI) data assimilation method for MFS involves an ad-hoc truncation in the representation of the background error covariance. BHM can be used to remove this arbitrary truncation. Moreover, as ensemble forecasts are run in MFS for abrupt seasonal transition events, the ensemble spread will be used to refine priors in the BHM for error covariance evolution.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 30, 2006
- Accession Number
- ADA630916
Entities
People
- Christopher K. Wikle
- Mark Berliner
- Ralph F. Milliff
Organizations
- Northwest Research Associates