Developing an Automated Method for Detection of Operationally Relevant Ocean Fronts and Eddies

Since the early 90’s, the U.S. Navy has utilized an observation-based process for identification of frontal systems and eddies. These Ocean Feature Assessments (OFA) rely on trained analysts to identify and position ocean features using satellite-observed sea surface temperatures. Meanwhile, as enhancements and expansion of the navy’s Hybrid Coastal Ocean Model (HYCOM) and Regional Navy Coastal Ocean Model (RNCOM) domains have proceeded, the Naval Oceanographic Office (NAVO) has provided Tactical Oceanographic Feature Assessments (TOFA) that are based on data-validated model output but also rely on analyst identification of significant features. A recently completed project has migrated OFA production to the ArcGIS-based Acoustic Reach-back Cell Ocean Analysis Suite (ARCOAS), enabling use of additional observational datasets and significantly decreasing production time; however, it has highlighted inconsistencies inherent to this analyst-based identification process. Current efforts are focused on development of an automated method for detecting operationally significant fronts and eddies that integrates model output and observational data on a global scale. Previous attempts to employ techniques from the scientific community have been unable to meet the production tempo at NAVO. Thus, a system that incorporates existing techniques (Marr-Hildreth, Okubo-Weiss, etc.) with internally-developed feature identification methods (from model-derived physical and acoustic properties) is required. Ongoing expansions to the ARCOAS toolset have shown promising early results.