SST Fronts in the Southern Ocean

Draft,… work in progress

Mati Kahru,


The location of fronts in the sea-surface temperature (SST) images provides information on a variety of processes in the ocean. Automated objective methods to detect SST fronts have been in the development for many years. Typical edge-detection methods such as the Sobel operator are discrete approximations to the gradient. Gradient-based edge detectors are characterized by spurious responses when applied to noisy data. These so-called local operators use one or another fixed threshold to distinguish an edge from "normal" variability. Gradient operators are known to have many problems and are therefore not recommended (Holyer and Peckinpaugh, 1989). Among the several more advanced methods the single-image edge detection (SIED) method of Cayula and Cornillon (1992) has been shown to be superior in tests (Cayula et al., 1991). The method has been used by Peter Cornillon’s group at URI (Ullmann and Cornillon 1999, 2000, 2001) and others (Kahru et al., 1995) for objective mapping of SST fronts.

The basic idea of the SIED method is to use overlapping windows to investigate the statistical likelihood of an edge by (1) performing a histogram analysis to detect bimodality of the histogram and (2) detecting the cohesiveness of the potential edge. A modification to the SIED method proposed by Diehl et al (2002) uses variogram analysis in two directions to find the best window size in x and y directions instead of a fixed window size.


The SIED method as modified by Diehl et al. (2002) was implemented. Tests were run on various SST datasets: MODIS-Terra daily, 8-day and monthly 4.6 km datasets (available for 2000-2004) as well as AVHRR Pathfinder version 5 (PF5) daily, 7-day and monthly data (available for 1985-2004). While the MODIS data has probably higher accuracy, the AVHRR Pathfinder data have the advantage of a much longer time period. Both MODIS and PF5 data are distributed in a form that includes pixels of variable quality levels.

·        The data sets were screened to exclude low-quality pixels and keep only the best (quality 0 for MODIS, quality 7 for PF5). The pixels with lower quality are usually contaminated by clouds and interfere with edge detection (the SIED method performs additional extra steps for cloud detection and elimination). Programs were developed for screening large amounts of SST data (wam_screen_modis for MODIS and wam_screen_pf for PF5). For MODIS I used the Level-3 daytime (ascending) mapped product MO04MD and the corresponding quality product MO04QD (11 micrometer SST).

·        The data in the global equal angle projection were mapped to a projection suitable for polar data. A polar stereographic projection with nominal pixel size of 5.0 km was created with the master program of Terascan™.  A WAM script wam_remap2 was used to remap all the daily SST data to that projection.

·        The Diehl et al (2002) method with variable window size was used for edge detection on daily images in polar stereographic projection. The threshold value of the between-cluster variance to the within-cluster variance was increased from the default value 0.76 used by Diehl et al. (2002) to 0.8 to detect only the more significant edges.

·        The daily edge images were then composited for each calendar month with a WAM script edge_accumulate and three products were created as HDF files for each calendar month: valid SST count, front count and front frequency. The valid SST count image shows the count number that a pixel had a valid SST value (during that month). As clouds and ice are a serious problem in the Southern Ocean, some areas (pixels) did not have a single valid SST pixel during a whole month. The edge count image shows the number of times that a pixel was detected as an edge pixel. The front frequency image is a ratio of the edge count to the valid SST count and shows the frequency of that a front was detected for a pixel. The frequency range is from 0 to 1 and pixels with no valid SST (and no detected fronts) were assigned pixel value -1.


Preliminary Results


The following figures show the monthly front patterns detected using MODIS daily SST for 2000-2004 for three selected months: December, January and February.



Fig. 1. Count of valid SST pixels from daily daytime MODIS Terra for December (left column), January (middle column), February (right column) for 2000-2001 (top), 2001-2002 (2nd from top), 2002-2003 (2nd from bottom), 2003-2004 (bottom). Black means no valid data, red is the highest number of valid data.



Fig. 2. Front counts from daily daytime MODIS Terra for December (left column), January (middle column), February (right column) for 2000-2001 (top), 2001-2002 (2nd from top), 2002-2003 (2nd from bottom), 2003-2004 (bottom). White means no detected fronts, red shows the detected fronts.



Fig. 3. Front frequency from daily daytime MODIS Terra for December (left column), January (middle column), February (right column) for 2000-2001 (top), 2001-2002 (2nd from top), 2002-2003 (2nd from bottom), 2003-2004 (bottom). White means no data, darker blue means more frequent fronts.


More analysis is needed to find any connections between the front patterns and other variables.


Tasks to do:







Cayula, J.-F. and P. Cornillon (1992) Edge detection algorithm for SST images. Journal of Atmospheric and Oceanic Technology 9: 67-80.

Diehl, Scott F., Judith W. Budd, David Ullman and Jean-Francois Cayula (2002) Geographic Window Sizes Applied to Remote Sensing Sea Surface Temperature Front Detection. Journal of Atmospheric and Oceanic Technology, 19(7): 1115-1113.

Holyer, R. J. and S.H. Peckinpaugh (1989) Edge detection applied to satellite imagery of the oceans. IEEE Transactions on Geoscience and Remote Sensing 27: 46 56.

Kahru, M., B. Håkansson, O. Rud, Distributions of the sea-surface temperature fronts in the Baltic Sea as derived from satellite imagery. Cont. Shelf Res., 15(6):  663-679, 1995.

Ullman, D.S., and P.C. Cornillon (1999) Surface temperature fronts off the East Coast of North America from AVHRR imagery, J. Geophys. Res., 104(C10), 23459-23478.

Ullman, D.S., and P.C. Cornillon (2000) Evaluation of front detection methods for satellite-derived SST data using in situ observations, J. Atmos. Oceanic Tech., 17(12), 1667-1675.

Ullman, D.S., and P.C. Cornillon (2001) Continental shelf surface thermal fronts in winter off the northeast US coast, Cont. Shelf Res., 21(11-12), 1139-1156.