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- Author or Editor: Libe Washburn x
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Abstract
HF radars measure ocean surface currents near coastlines with a spatial and temporal resolution that remains unmatched by other approaches. Most HF radars employ direction-finding techniques, which obtain the most accurate ocean surface current data when using measured, rather than idealized, antenna patterns. Simplifying and automating the antenna pattern measurement (APM) process would improve the utility of HF radar data, since idealized patterns are widely used. A method is presented for obtaining antenna pattern measurements for direction-finding HF radars from ships of opportunity. Positions obtained from the Automatic Identification System (AIS) are used to identify signals backscattered from ships in ocean current radar data. These signals and ship position data are then combined to determine the HF radar APM. Data screening methods are developed and shown to produce APMs with low error when compared with APMs obtained with shipboard transponder-based approaches. The analysis indicates that APMs can be reproduced when the signal-to-noise ratio (SNR) of the backscattered signal is greater than 11 dB. Large angular sectors of the APM can be obtained on time scales of days, with as few as 50 ships.
Abstract
HF radars measure ocean surface currents near coastlines with a spatial and temporal resolution that remains unmatched by other approaches. Most HF radars employ direction-finding techniques, which obtain the most accurate ocean surface current data when using measured, rather than idealized, antenna patterns. Simplifying and automating the antenna pattern measurement (APM) process would improve the utility of HF radar data, since idealized patterns are widely used. A method is presented for obtaining antenna pattern measurements for direction-finding HF radars from ships of opportunity. Positions obtained from the Automatic Identification System (AIS) are used to identify signals backscattered from ships in ocean current radar data. These signals and ship position data are then combined to determine the HF radar APM. Data screening methods are developed and shown to produce APMs with low error when compared with APMs obtained with shipboard transponder-based approaches. The analysis indicates that APMs can be reproduced when the signal-to-noise ratio (SNR) of the backscattered signal is greater than 11 dB. Large angular sectors of the APM can be obtained on time scales of days, with as few as 50 ships.
Abstract
Automated eddy detection methods are fundamental tools to analyze eddy activity from the large datasets derived from satellite measurements and numerical model simulations. Existing methods are either based on the distribution of physical parameters usually computed from velocity derivatives or on the geometry of velocity streamlines around minima or maxima of sea level anomaly. A new algorithm was developed based exclusively on the geometry of the velocity vectors. Four constraints characterizing the spatial distribution of the velocity vectors around eddy centers were derived from the general features associated with velocity fields in the presence of eddies. The grid points in the domain for which these four constraints are satisfied are detected as eddy centers. Eddy sizes are computed from closed contours of the streamfunction field, and eddy tracks are retrieved by comparing the distribution of eddy centers at successive time steps. The results were validated against manually derived eddy fields. Two parameters in the algorithm can be modified by the users to optimize its performance. The algorithm is applied to both a high-resolution model product and high-frequency radar surface velocity fields in the Southern California Bight.
Abstract
Automated eddy detection methods are fundamental tools to analyze eddy activity from the large datasets derived from satellite measurements and numerical model simulations. Existing methods are either based on the distribution of physical parameters usually computed from velocity derivatives or on the geometry of velocity streamlines around minima or maxima of sea level anomaly. A new algorithm was developed based exclusively on the geometry of the velocity vectors. Four constraints characterizing the spatial distribution of the velocity vectors around eddy centers were derived from the general features associated with velocity fields in the presence of eddies. The grid points in the domain for which these four constraints are satisfied are detected as eddy centers. Eddy sizes are computed from closed contours of the streamfunction field, and eddy tracks are retrieved by comparing the distribution of eddy centers at successive time steps. The results were validated against manually derived eddy fields. Two parameters in the algorithm can be modified by the users to optimize its performance. The algorithm is applied to both a high-resolution model product and high-frequency radar surface velocity fields in the Southern California Bight.
Abstract
Dense arrays of surface drifters are used to quantify the flow field on time and space scales over which high-frequency (HF) radar observations are measured. Up to 13 drifters were repetitively deployed off the Santa Barbara and San Diego coasts on 7 days during 18 months. Each day a regularly spaced grid overlaid on a 1-km2 (San Diego) or 4-km2 (Santa Barbara) square, located where HF radar radial data are nearly orthogonal, was seeded with drifters. As drifters moved from the square, they were retrieved and replaced to maintain a spatially uniform distribution of observations within the sampling area during the day. This sampling scheme resulted in up to 56 velocity observations distributed over the time (1 h) and space (1 and 4 km2) scales implicit in typical surface current maps from HF radar. Root-mean-square (RMS) differences between HF radar radial velocities obtained using measured antenna patterns, and average drifter velocities, are mostly 3–5 cm s−1. Smaller RMS differences compared with past validation studies that employ current meters are due to drifter resolution of subgrid-scale velocity variance included in time and space average HF radar fields. Roughly 5 cm s−1 can be attributed to sampling on disparate time and space scales. Despite generally good agreement, differences can change dramatically with time. In one instance, the difference increases from near zero to more than 20 cm s−1 within 2 h. The RMS difference and bias (mean absolute difference) for that day exceed 7 and 12 cm s−1, respectively.
Abstract
Dense arrays of surface drifters are used to quantify the flow field on time and space scales over which high-frequency (HF) radar observations are measured. Up to 13 drifters were repetitively deployed off the Santa Barbara and San Diego coasts on 7 days during 18 months. Each day a regularly spaced grid overlaid on a 1-km2 (San Diego) or 4-km2 (Santa Barbara) square, located where HF radar radial data are nearly orthogonal, was seeded with drifters. As drifters moved from the square, they were retrieved and replaced to maintain a spatially uniform distribution of observations within the sampling area during the day. This sampling scheme resulted in up to 56 velocity observations distributed over the time (1 h) and space (1 and 4 km2) scales implicit in typical surface current maps from HF radar. Root-mean-square (RMS) differences between HF radar radial velocities obtained using measured antenna patterns, and average drifter velocities, are mostly 3–5 cm s−1. Smaller RMS differences compared with past validation studies that employ current meters are due to drifter resolution of subgrid-scale velocity variance included in time and space average HF radar fields. Roughly 5 cm s−1 can be attributed to sampling on disparate time and space scales. Despite generally good agreement, differences can change dramatically with time. In one instance, the difference increases from near zero to more than 20 cm s−1 within 2 h. The RMS difference and bias (mean absolute difference) for that day exceed 7 and 12 cm s−1, respectively.
