Introduction

1. Optimizing HF Radar for SAR using USCG Surface Drifters and Moored ADCPs MARCOOS Partners Introduction Optimal Interpolation Real-Time QA Criteria Real-time quality assurance was initially determined based on the normalized uncertainty values generated in the OI combination for the east (below left) and north (below right) components. Our goal was to select a threshold that would maximize data coverage while preserving data quality. The plots below show the correlation (left) and coverage (right) of the OI compared to the drifter based on the normalized uncertainty. Given plots like this our real-time data includes only those girdpoints in which the normalized uncertainty of both components is below 0.6. Total vector currents were combined from radial vectors using both the Unweighted Least Squares and Optimal Interpolation (Kim et. al. 2008) algorithms MACOORA formed the Mid-Atlantic Regional Coastal Ocean Observing System (MARCOOS) to generate quality controlled and sustained ocean observation and forecast products that fulfill user needs. MARCOOS products will support the two priority regional themes of Maritime Safety and Ecological Decision Making. In addition it will provide critical regional-scale input to the region’s nested sub-regional efforts on Coastal Inundation and Water Quality. Unweighted Least Squares Optimal Interpolation Evaluation MARCOOS will accomplish this by coordinating an extensive array of existing observational, data management, and modeling assets to generate and disseminate real time data, nowcasts and forecasts of the ocean extending from Cape Cod to Cape Hatteras. Prior to the introduction of this new OI product to the Coast Guard decision tool, an extensive validation and evaluation of this product was done. Using a test period in the winter of 2007, totals generated with both the existing Unweighted Least Squares (UWLS) algorithm and the new Optimal Interpolation (OI) algorithm were compared to a moored ADCP (blue circle below right) and a drifter track (below right). The analysis included sensitivity to input parameters to OI including expected variances and spatial scales. The evaluation showed that the new OI and existing UWLS had similar skill in regions of good system geometry (ADCP Comparison). However, in regions of poor coverage like the offshore edge of the CODAR domain (Drifter Comparison), the OI was much more robust in filling gaps and eliminating outliers. Normalized Uncertainty – North Normalized Uncertainty – East HF Radar Network For Maritime Safety the emphasis will be on the operation of a nested HF radar array. This array will provide real-time surface current data directly to four modeling groups. These forecasts and the real-time data will be fed directly to the Coast Guard decision tool (SAROPS) through their Environmental Data Server (EDS). Integration into SAROPS The major accomplishment in this effort is the quality controlled MARCOOS HF Radar totals and associated Short Term Prediction System (STPS) forecasts are being served through the Coast Guard’s Environmental Data Server (EDS) and then into the Coast Guard Search and Rescue Optimal Planning System (SAROPS) as of May 4, 2009. The figure below left shows the MARCOOS HF Radar and STPS product in SAROPS. The pink square off the coast of NJ is the initial search area. The rainbow pattern on the right hand side shows the probability distribution of the search area after some time has passed. The figure also shows the path of Coast Guard Self Locating Datum Marker Buoy (SLDMB) through the coverage field as a dark blue line. ADCP Comparison OI UWLS 4 Modeling Systems 1 Statistical 3 Dynamical Drifter Comparison OI UWLS Real-Time Surface Currents SAROPS Kim, S.Y., E. J. Terrill, and B. D. Cornuelle. 2008. Mapping surface currents from HF radar radial velocity measurements using optimal interpolation. J. Geophys. Res., VOL. 113, C10023, doi:10.1029/2007JC004244.