MED-roms Forecasting System   E. Di Lorenzo and K. Chhak Georgia Institute of Technology sponsored by Office of Naval Research

 

MED-roms is a component of the project: Bayesian Hierarchical Models to Augment the Mediterranean Forecast System: Extending Ensemble Ocean Forecast Skill - [ html ]

Links: Opendap data access http://dods.o3d.org:8080   Bulding MED-roms hindcast model Slide of PHASE 1 [ HTML, PDF ]

 

Collaborators: Dr. Ralph F. Milliff Colorado Research Associates Division (CoRA), NorthWest Research Associates, Inc. milliff@cora.nwra.com   Professor L. Mark Berliner   Department of Statistics   Ohio State University mb@stat.ohio-state.edu   Professor Nadia Pinardi   Corso di Scienze Ambientali University of Bologna n.pinardi@sincem.unibo.it

Professor Christopher K. Wikle Department of Statistics University of Missouri wikle@stat.missouri.edu   Professor Emanuele Di Lorenzo School of Earth and Atmospheric Sciences Georgia Institute of Technology 311 Ferst Drive, Atlanta, GA 30332 edl@gatech.edu

 

 

ABSTRACT

 

Bayesian Hierarchical Models (BHM) are implemented to establish ensemble ocean forecasting

tools for the Mediterranean Forecast System (MFS). Progress is reported for MFS-Wind-BHM

and MFS-Error-BHM from Phase I of the research. Ocean ensemble initial conditions and ensem-

ble forecasts driven by MFS-Wind-BHM exhibit distributions of ocean circulation uncertainty

concentrated in mesoscale features. Plans for MFS-Error-BHM are described to refine vertical

error covariance estimates, and build daily time-dependence in background error covariance for

MFS reduced-order optimal interpolation. Research plans for Phase II are proposed to focus on

extending skill in targeted ocean forecasts using BHM methodology for superensemble forecast

implementations. Med-MultiModel-BHM is a superensemble forecast system that will include

contributions from a proposed Mediterranean implementation of the Regional Ocean Modelling

System (ROMS) to combine with MFS forecasts. Two variants of an MFS-MultiParam-BHM

methodology are described. In one, a superensemble is constructed of MFS models employing

different vertical mixing parameterizations. In a second variant, a superensemble using different

model resolutions will be constructed. Large ensemble sizes from coarse resolution MFS mod-

els can combine with high-resolution MFS implementations to yield an optimal superensemble

forecast. We propose to explore the accumulating impacts of BHM on MFS using stochastic

optimal/non-normal operator diagnostics.