MesoScale Ocean Forecast/Assimilation Studies

• Main Authors: Miller, Arthur J., Cornuelle, Bruce
• Other Authors: SCRIPPS INSTITUTION OF OCEANOGRAPHY LA JOLLA CA
• Format: Text
• Language: English
• Published: 1999
• Subjects: Physical and Dynamic Oceanography; *FORECASTING; *OCEAN MODELS; SOFTWARE ENGINEERING; OCEAN CURRENTS; DATA MANAGEMENT; OCEANOGRAPHIC DATA; OCEAN FORECASTING; Iceland
• Online Access: http://www.dtic.mil/docs/citations/ADA359302; http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA359302

The long-term goal over this three year project has been to develop computer software needed to optimize initial conditions, internal parameters and external parameters for the Harvard primitive equation (PE) model in order to produce the best forecasts in an arbitrary region. This new tool invokes an inverse technique to fuse all available data types, gathered non-synoptically, with optimized model dynamics. The technique is distinct from (and complementary to) the optimal interpolation and Kalman filter assimilation strategies now being developed and used at Harvard (e.g., Lermusiaux, 1997). The scientific objectives of this research include answering the following questions. Can forecast skill in a highly unstable region like the Iceland-Faeroe Front be extended to 7 days? Can a diagnostic simulation over a 10-day interval in that region include all the data in an inverse calculation, or is it too nonlinear? What are the relative impacts of the various data types (CTD/XBT/XCTD casts, current meters, surface drifters) on making forecasts in this region? The technical objectives encompass the details of the model fitting process. How nonlinear is the fit? Can the nonlinearity be reduced by optimizing large-scale structure first? How much data can be fit at one time? Is the distribution of the data sufficient to initialize the model? Are the open boundaries causing instabilities in the model? Real-time ocean forecasting involves assembling an initial state which often requires merging many data types that are usually gathered over non-synoptic intervals. Furthermore, dynamical ocean forecast models still require improvements in their physics (including parameterizations). We are addressing these two issues simultaneously in applying a standard inverse technique to the Harvard PE ocean model in the context of an unique dataset in the Iceland-Faeroe frontal region.