Wikle, C. K.

This article analyzes the ocean forecast response to surface vector wind (SVW) distributions generated by a Bayesian hierarchical model (BHM) developed in Part I of this series. A new method for ocean ensemble forecasting (OEF), the socalled BHM-SVW-OEF, is described. BHM-SVW realizations are used to produce and force perturbations in the ocean state during 14 day analysis and 10 day forecast cycles of the Mediterranean Forecast System (MFS). The BHM-SVW-OEF ocean response spread is amplified at the mesoscales and in the pycnocline of the eddy field. The new method is compared with an ensemble response forced by European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EEPS) surface winds, and with an ensemble forecast started from perturbed initial conditions derived froman ad hoc thermocline intensified random perturbation (TIRP) method. The EEPS-OEF shows spread on basin scales while the TIRP-OEF response is mesoscale-intensified as in the BHM-SVW-OEF response. TIRP-OEF perturbations fill more of the MFS domain, while the BHM-SVW-OEF perturbations are more location-specific, concentrating ensemble spread at the sites where the ocean-model response to uncertainty in the surface wind forcing is largest.

A Bayesian hierarchical model (BHM) is developed to estimate surface vector wind (SVW) fields 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 SVW retrievals 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 of 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. The sequential data-assimilation method of the Mediterranean forecast system (MFS) is adapted to the ensemble implementation. Analyses of sample ensemble initial conditions for a single data-assimilation period in MFS are presented to demonstrate the multivariate impact of the BHM–SVW ensemble generation methodology. Ensemble initial-condition spread is quantified by computing standard deviations of ocean state variable fields over the ten ensemble members. The methodological findings in this article are of two kinds. From the perspective of statistical modelling, the process-model development is more closely related tophysicalbalances than inpreviousworkwithmodels for the SVW.Fromthe ocean forecast perspective, the generation of ocean ensemble initial conditions via BHM is shown to be practical for operational implementation in an ensemble ocean forecast system. Phenomenologically, ensemble spread generated via BHM–SVW occurs on ocean mesoscale time- and space-scales, in close association with strong synoptic-scale wind-forcing events. A companion article describes the impacts of the BHM–SVW ensemble method on the ocean forecast in comparisons with more traditional ensemble methods.