Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/5721
AuthorsMilliff, R. F.* 
Bonazzi, A.* 
Wikle, C. K.* 
Pinardi, N.* 
Berliner, L. M.* 
TitleOcean Ensemble Forecasting, Part I: Ensemble Mediterranean Winds from a Bayesian Hierarchical Model
Issue Date2009
URIhttp://hdl.handle.net/2122/5721
KeywordsBayesian Hierarchical Model
Mediterranean Sea
Ensemble Forecasting
Subject Classification03. Hydrosphere::03.01. General::03.01.05. Operational oceanography 
AbstractA Bayesian Hierarchical Model (BHM) is developed to estimate surface vector wind fields (SVW), and associated uncertainties, over the Mediterranean Sea. The BHM-SVW incorporates data-stage inputs from analyses and forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) and from the QuikSCAT data record. The process model stage of the BHM-SVW is based on a Rayleigh Friction Equation model for surface winds. Dynamical interpretations of posterior distributions of the BHM-SVW parameters are discussed. Ten realizations from the posterior distribution the BHM-SVW are used to force the data assimilation step of an experimental ensemble ocean forecast system for the Mediterranean Sea in order to create a set of ensemble initial conditions. Ensemble initial condition spread is quantified by computing standard deviations of ocean state variable fields over the 10 ensemble members, driven by 10 realizations from the BHM-SVW posterior distribution over a 14-day sequential data assimilation period. Ensemble spread occurs on mesoscale time and space scales, in close association with strong synoptic scale wind forcing events. A companion paper compares the performance of the MFS ensemble forecasts given initial condition generation and forecast forcing from the BHM-SVW, with forecasts based on more traditional methods of ensemble generation
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