Optimizing HF Radar for SAR
using USCG Surface Drifters and Moored ADCPs
MARCOOS Partners
Introduction
Optimal Interpolation
MACOORA formed the MidAtlantic 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.
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.
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).
Total vector currents were combined from radial vectors using both the
Unweighted Least Squares and Optimal Interpolation (Kim et. al. 2008)
algorithms
Unweighted Least Squares
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.
Optimal Interpolation
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.
OI
UWLS
Drifter
Comparison
Real-Time Surface Currents
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.
Evaluation
ADCP
Comparison
4 Modeling Systems
1 Statistical
3 Dynamical
Real-Time QA Criteria
OI
UWLS
Normalized Uncertainty – East
Normalized Uncertainty – North
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.
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.