SST Fronts in the Southern Ocean
Draft,… work in progress
Mati Kahru, mkahru@ucsd.edu
Introduction
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.
Methods
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.
More analysis is needed to
find any connections between the front patterns and other variables.
Tasks to do:
- Find visualization methods to detect
interannual variability
- Find analysis methods for detecting relationships
between frontal frequency and other variables
- Extend this analysis to the AVHRR Pathfinder
version 5 data (1985-2004)
- Add data from multiple satellites (add Terra
Aqua, overlapping AVHRR and MODIS datasets)
References
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
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.