A comparison of satellite-derived sea surface temperature fronts using two edge detection algorithms

Satellite-derived sea surface temperature (SST) fronts provide a valuable resource for the study of oceanic fronts. Two edge detection algorithms designed specifically to detect fronts in satellite-derived SST fields are compared: the histogram-based algorithm of Cayula and Cornillon, 1992, Cayula and Cornillon, 1995 and the entropy-based algorithm of Shimada et al. (2005). The algorithms were applied to 4 months (July and August for both 1995 and 1996) of SST fields and the results are compared with SST data taken by the M.V. Oleander, a container ship that makes weekly transits between New York and Bermuda. There is no significant difference in front pixels found with the Cayula-Cornillon algorithm and those found in the in situ (Oleander) data. Furthermore, for strong fronts, with gradients greater than 0.2 K/km, the distribution of fronts found with the Shimada et al. algorithm is quite similar to that of fronts found with the Cayula-Cornillon algorithm. However, there are significant differences in the number of weak fronts found. This is seen clearly in waters south of the Gulf Stream where the gradient magnitude of fronts found is less than 0.1 K/km. In this region, the probability that the Shimada et al. algorithm detects a front rarely falls below 4% while neither the Cayula-Cornillon algorithm applied to the satellite-derived SST fields nor the gradient-based algorithm applied to the Oleander temperature time series find fronts more than 1% of the time. These results raise the question of exactly what qualifies as an SST front, a classic problem in edge detection

Properties of Rossby Waves in the North Atlantic Estimated from Satellite Data

This study uses satellite observations of sea surface height (SSH) to detect westward-propagating anomalies, presumably baroclinic Rossby waves, in the North Atlantic and to estimate their period, wavelength, amplitude, and phase speed. Detection involved a nonlinear fit of the theoretical dispersion relation for Rossby waves to the time–longitude spectrum at a given latitude. Estimates of period, wavelength, and phase speed resulted directly from the detection process. Based on these, a filter was designed and applied to extract the Rossby wave signal from the data. This allowed a mapping of the spatial variability of the Rossby wave amplitude for the North Atlantic. Results showed the familiar larger speed of observed Rossby waves relative to that expected from theory, with the largest differences occurring at shorter periods. The data also show that the dominant Rossby waves, those with periods that are less than annual, propagated with almost uniform speed in the western part of the North Atlantic between 30° and 40°N. In agreement with previous studies, the amplitude of the Rossby wave field was higher in the western part of the North Atlantic than in the eastern part. This is often attributed to the influence of the Mid-Atlantic Ridge. By contrast, this study, through an analysis of the wave spatial structure, suggests that the source of the baroclinic Rossby waves at midlatitudes in the western North Atlantic is located southeast of the Grand Banks where the Gulf Stream and the deep western boundary current interact with the Newfoundland Ridge. The spatial structure of the waves in the eastern North Atlantic is consistent with the formation of these waves along the basin\u27s eastern boundary

Effects of Geographic Variation in Vertical Mode Structure on the Sea Surface Topography, Energy, and Wind Forcing of Baroclinic Rossby Waves

Interpretation of sea surface height anomaly (SSHA) and wind forcing of first baroclinic mode Rossby waves is considered using linear inviscid long-wave dynamics for both the standard and surface-intensified vertical mode in a continuously stratified rest-state ocean. The ratio between SSHA variance and vertically integrated energy of waves is proportional to 1) a dimensionless ratio characterizing the surface intensification of the pressure eigenfunction, 2) the squared internal gravity wave speed, and 3) the inverse of the water depth. Geographic variations in stratification and bathymetry can therefore cause geographically varying SSHA variance even for spatially uniform wave energy. The ratio between SSHA variance and wave energy across the North Atlantic shows important spatial variations based on eigensolutions for the standard vertical mode determined numerically using climatological hydrography. The surface-intensified mode result is similar, though the ratio is generally slightly larger and less sensitive to depth variations. Results are applied to the propagating annual-frequency portion of TOPEX altimeter SSHA in the North Atlantic. SSHA variance at 35° in the western half of the basin increases by ∼63% over that in the east, but the associated change in inferred first-mode baroclinic Rossby wave energy is a substantially smaller increase of ∼26% (∼34%) for the standard (surface intensified) mode. This is mainly associated with increases to vertical mode surface intensification and squared internal gravity wave speed in the west due to stronger stratification above the pycnocline. The wind-forced wave equation for SSHA has a dimensionless coefficient of Ekman pumping that is proportional to the ratio between SSHA variance and wave energy, implying similar geographic variation in efficiency of wind excitation of Rossby wave SSHA

Edge Detection Algorithm for SST Images

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

Stability‐induced modification of sea surface winds over Gulf Stream rings

Satellite‐borne scatterometer and infrared data collected over Gulf Stream warm and cold core rings are used to study the effect of the sea‐air temperature difference on the wind speed over rings. The observed acceleration of the wind over warm core rings and deceleration over cold core rings is found to be consistent with that predicted by the planetary boundary layer model of Brown and Foster [1994]. In addition it is shown that the distance over which the winds respond to an ocean surface temperature step is short (≤25km) while the distance over which the marine boundary layer responds to a surface temperature step is long (≥175km)

Cloud Detection from a Sequence of SST Images

A cloud detection algorithm was designed as an adjunct to a companion edge-detection algorithm. The cloud detection integrates two distinct algorithms: one based on multiimage processing, the other on single-image analysis. The multiimage portion of the cloud detection algorithm operates on a time sequence of sea surface temperature (SST) images. It is designed to detect clouds associated with regions of apparently lower temperatures than the underlying SST field. A pixel in the current image is initially considered to be corrupted by clouds if it is significantly cooler than the corresponding pixel in a neighbor image. To refine the initial classification, the algorithm checks the current image and the neighbor image for the presence of water masses, which through displacement could explain the change in temperature. The single-image cloud detection algorithm is designed to detect clouds associated with regions of the SST image where gradient vectors have a large magnitude. These regions are flagged in the map of potential clouds. multiimage processing is integrated with the single-image algorithm by adding pixels classified as cloudy at the multiimage level to the map of potential clouds. Further analysis of the gradient vector field and of the shapes of potentially cloudy areas allows one to determine whether these regions correspond to clouds or SST fronts. A previous study has shown that the clouds identified by the single-image algorithm were in close agreement with those detected by a human expert. To validate the additional multiimage processing, the effect of the integrated cloud detection on the performance of a companion edge detection algorithm is examined. These results and a direct comparison with the cloud masks produced by a human expert indicate that, compared to the single-image algorithm, the multiimage algorithm successfully identify additional cloud-corrupted regions while keeping a low rate for the detection of false clouds

A Characterization of the North Atlantic STMW Layer Climatology Using \u3cem\u3eWorld Ocean Atlas 1994\u3c/em\u3e Data

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\u27s 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\u27s 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

Annual and Interannual Changes in the North Atlantic STMW Layer Properties

Subtropical mode waters (STWMs) are water masses formed in winter by convective mixing on the equatorward side of western boundary currents in the subtropical gyres. After the return of the seasonal stratification in spring, it is found at the stratification minimum between the seasonal and main pycnoclines. By characterizing STMW primarily at the density gradient minimum, previous studies were limited in their ability to describe STMW properties over large temporal and spatial scales. Rather than using a density-based characterization, the North Atlantic STMW layer was identified here by its much smaller temperature gradient relative to the more stratified seasonal and main thermocline, its temperature, and its large thickness. By using this temperature-based characterization, this study was able to develop a climatology using the large number of XBTs deployed between 1968 and 1988 and contained in the World Ocean Atlas 1994 historical hydrographic database and to use this climatology to examine STMW properties on large spatial and long temporal scales. Three different characterizations were used to assess the degree of convective renewal of the STMW layer during the 1968–88 winters. Two characterizations were based on comparing the winter mixed layer properties to the STMW layer properties in the previous fall, while the third characterization involved comparing the temperature gradient through the STMW layer in the spring to the STMW layer temperature gradient in the previous fall. Based on these characterizations, there was considerable spatial and temporal variability in the renewal of the STMW layer\u27s vertical homogeneity from 1968 to 1988. Basinwide renewal occurred in 1969, 1970, 1977, 1978, 1981, and 1985, with more localized renewal, usually east of 55°W, in the other years. While STMW is nearly vertically homogeneous immediately after renewal, the temperature gradient through the layer increases with time following renewal. The annual rate of increase in the temperature gradient in the year following renewal is ∼5–6 (× 10−4°C per 100 m per day), while the interannual rate of increase is ∼2.0 × 10−4°C per 100 m per day following winters with no renewal of the STMW layer

Edge Detection Applies to SST Fields

The wide availability of workstations has made the creation of sophisticated image processing algorithms economically possible. Here the latest version of an algorithm designed to detect fronts automatically in satellite-derived Sea Surface Temperature (SST) fields, is presented. The Algorithm operates at three levels: picture level, window level, and local/pixel level, much as humans seem to. Following input of the data, the most obvious clouds (based on temperature and shape) are identified and tagged so that data which do not represent sea surface temperature are not used in the subsequent modules. These steps operate at the picture and then at the window level. The procedure continues at the window level with the formal portion of the edge detection. Using techniques for unsupervised learning, the temperature distribution (histogram) in each window is analyzed to determine the statistical relevance of each possible front. To remedy the weakness related to the fact that clouds and water masses do not always form compact populations, the algorithm also includes a study of the spatial properties instead of relying entirely on temperatures. In this way, temperature fronts are unequivocally defined. Finally, local operators are introduced to complete the contours found by the region based algorithm. 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 other algorithms commonly used to locate edges in satellite-derived SST images

Evaluation of Front Detection Methods for Satellite-Derived SST Data Using In Situ Observations

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 (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