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  • Author or Editor: Peter Cornillon x
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Peter Cornillon
and
Randolph Watts

Abstract

The northern edge of the Gulf Stream off Cape Hatteras, North Carolina was located in 155 AVHRR-derived maps of sea surface temperature (SST) using five different methods. One method was subjective location of the northern edge by an analyst; the other four involved objective location of the edge by computer using various statistics of the SST field. Specifically, the quantities considered were: maximum SST gradient (calculated over a 3 × 3 pixel box), maximum SST (on a pixel-by-pixel basis), maximum variance (calculated over a 7 × 7 pixel box), and change in the skewness of the SST distribution (calculated over a 5 × 5 pixel box). The resulting locations were compared with the location of the 15°C isotherm at 200 m (T 15) determined from inverted echo sounders (IESs) moored on the sea floor. The best method, which yielded the smallest rms difference from the IES-derived T 15, was the subjective one; the surface front was located 9.0 km shoreward of T 15 with a rms difference of 14.3 km. The best objective technique used the skew of the SST distribution: Each pixel in the image was replaced by the skew of the distribution of the twenty-five SST values obtained from a 5 × 5 pixel square centered on that pixel. The skew changes sign when a step in the SST data, such as the Gulf Stream northern edge, is crossed. The Gulf Stream northern edge located in the skew images was found to be 14.0 km shoreward of T 15 in the mean with a rms difference of 18.2 km. In general, the more spatial information used, the better the estimate.

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Jean-François Cayula
and
Peter Cornillon

Abstract

An algorithm to detect fronts in satellite-derived sea surface temperature fields is presented. Although edge detection is the main focus, the problem of cloud detection is also addressed since unidentified clouds can lead to erroneous edge detection. The algorithm relies on a combination of methods and it operates at the picture, the window, and the local level. The resulting edge detection is not based on the absolute strength of the front, but on the relative strength depending on the context thus, making the edge detection temperature-scale invariant. The performance of this algorithm is shown to be superior to that of simpler algorithms commonly used to locate edges in satellite-derived SST images. This evaluation was performed through a careful comparison between the location of the fronts obtained by applying the various methods to the SST images and the in situ measures of the Gulf Stream position.

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Jean-François Cayula
and
Peter Cornillon

Abstract

This paper presents an approach based on the analysis of an image sequence to detect temperature fronts in a sea surface temperature image. The multi-image edge detection algorithm starts by applying a single-image edge detection algorithm to the sequence of images under study. Next, fronts or portions of fronts, which were detected in neighboring images by the single-image algorithm and which match features in the current image, are identified as persistent. The coordinates of these persistent fronts are then passed to the single-image edge detection algorithm so that additional fronts can be detected. The performance of the multi-image edge detection algorithm, of various single-image algorithms, and of a human expert are evaluated on a set of 98 images. For that purpose, the location of the fronts obtained by applying various methods to the SST images is compared to the in situ measures of the Gulf Stream position. With respect to both quality and the number of detected edges, the multi-image edge detection algorithm is the only automated method that achieves results comparable to those obtained by a human expert.

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David S. Ullman
and
Peter C. Cornillon

Abstract

Sea surface temperature (SST) fronts detected in Advanced Very High Resolution Radiometer (AVHRR) data using automated edge-detection algorithms were compared to fronts found in continuous measurements of SST made aboard a ship of opportunity. Two histograms (a single-image and a multi-image method) and one gradient algorithm were tested for the occurrence of two types of errors: (a) the detection of false fronts and (b) the failure to detect fronts observed in the in situ data. False front error rates were lower for the histogram methods (27%–28%) than for the gradient method (45%). Considering only AVHRR fronts for which the SST gradient along the ship track was greater than 0.1°C km−1, error rates drop to 14% for the histogram methods and 29% for the gradient method. Missed front error rates were lower using the gradient method (16%) than the histogram methods (30%). This error rate drops significantly for the histogram methods (5%–10%) if fronts associated with small-scale SST features (<10 km) are omitted from the comparison. These results suggest that frontal climatologies developed from the application of automated edge-detection methods to long time series of AVHRR images provide acceptably accurate statistics on front occurrence.

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Michael A. Alfultis
and
Peter Cornillon

Abstract

The North Atlantic Subtropical Mode Water (STMW) layer was identified based on its temperature, large thickness, and small temperature gradient. Comparisons between this method and identifying the STMW layer using a density-based (i.e., potential vorticity) criteria indicate that this method successfully identifies the STMW layer as the remnant of the previous winter's convective mixing. By using this temperature-based characterization of the STMW layer, this method was able to develop a climatology using the large number of expendable bathythermographs (XBTs) deployed between 1968 and 1988, and contained in the World Ocean Atlas 1994 historical hydrographic database. From this climatology, the STMW layer that is the remnant of the previous winter's convective activity is typically found between 175 and 450 m, has an average temperature near 18°C, and has a mean temperature gradient of 0.5°C (100 m)−1. Comparisons of the STMW temperature, thickness, and temperature gradient characteristics in this climatology agree with other observations of the North Atlantic STMW layer.

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Qingtao Song
,
Tetsu Hara
,
Peter Cornillon
, and
Carl A. Friehe

Abstract

Simulations, made with the fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5), of the response of the marine atmospheric boundary layer (MABL) as air moves over a sharp SST front are compared with observations made during the Frontal Air–Sea Interaction Experiment (FASINEX) in the North Atlantic subtropical convergence zone. The purpose of undertaking these comparisons was to evaluate the performance of MM5 in the vicinity of an SST front and to determine which of the planetary boundary layer (PBL) parameterizations available best represents MABL processes. FASINEX provides an ideal dataset for this work in that it contains detailed measurements for scenarios at the two extremes: wind blowing from warm to cold water normal to a 2°C SST front and the converse, wind blowing from cold to warm water.

For the wind blowing from warm to cold water, there is a pronounced modification of the near-surface wind field over the front, in both model results and aircraft observations. The decrease of near-surface wind speed and stress is due to a stable internal boundary layer (IBL) induced by the SST front, restricting exchange of mass and momentum between the surface and upper part of the MABL. For the cold-to-warm case, the relatively strong vertical mixing through the entire MABL over warm water dampens the response of the near-surface winds and surface stress to the SST front. The properties observed by the aircraft are simulated quite well in both cases, suggesting that MM5 captures the appropriate boundary layer physics at the mesoscale or regional scale.

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Fabian Schloesser
,
Peter Cornillon
,
Kathleen Donohue
,
Brahim Boussidi
, and
Emily Iskin

Abstract

Detailed understanding of submesoscale processes and their role in global ocean circulation is constrained, in part, by the lack of global observational datasets of sufficiently high resolution. Here, the potential of thermosalinograph (TSG) and Visible Infrared Imager Radiometer Suite (VIIRS) data is evaluated, to characterize the submesoscale structure of the near-surface temperature fields in the Gulf Stream and Sargasso Sea. In addition to spectral density, the structure function is considered, a statistical measure less susceptible to data gaps, which are common in the satellite-derived fields. The structure function is found to be an unreliable estimator, especially for steep spectral slopes, nominally between 2 and 3, typical of the Gulf Stream and Sargasso regions. A quality-control threshold is developed based on the number and size of gaps to ensure reliable spectral density estimates. Analysis of the impact of gaps in the VIIRS data on the spectra shows that both the number of missing values and the size of gaps affect the results, and that the steeper the spectral slope the more significant the impact. Furthermore, the TSG, with a nominal resolution of 75 m, captures the spectral characteristics of the fields in both regions down to scales substantially smaller than 1 km, while the VIIRS fields, with a nominal resolution of 750 m, reproduce the spectra well down to scales of about 20 km in the Sargasso Sea and 5 km in the Gulf Stream. The scales at which the VIIRS and TSG spectra diverge are thought to be determined by sensor and retrieval noise.

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Peter Cornillon
,
David Evans
,
Otis B. Brown
,
Robert Evans
,
Paul Eden
, and
James Brown

Abstract

A method for acquisition, processing and analysis of digital, satellite-derived SST fields on a research vessel at sea in near-real-time (within 10 h of the satellite pass) is discussed. Such imagery provides a general view of the SST field over a large area (700 × 900 km) centered on a 128 × 128 pixel, full-resolution view of the study area.

The ability to send these images to the research vessel in a reasonable amount of time (about 1 h using ATS-3) was a result of a three-level approach to data compression. To perform data compression, first, the overall image was decimated by 2 while the central 128 × 128 pixel portion was retained in full resolution. Second, a 1-bit-deep cloud mask was derived from the image. Third, the remaining SST values were encoded as SST steps from the previous pixel on a given scan line. Overall, the data were reduced by 75%–80%. An error-correcting protocol KERMIT, was used to establish low error rate data communications through the ATS-3 VHF links. A moderate capability digital display unit facilitates display and manipulation of the resultant imagery.

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