ellipse. This fit to the ellipses was found to be better than both a center-of-mass estimate or the intersection of perpendicular bisectors from the ring edge. Absolute velocity estimates were derived from adjacent pairs of ring centers. The velocity of the slope water was determined by a subjective tracking of small SST features in pairs of images (horizontal velocity estimation is discussed in more detail below), and the difference between the velocity estimates was the desired result. The uncertainties in all of the motion estimates were quite large. A related problem is the determination of the ring characteristics and frequency of occurrence based on a series of line samples (as from a radar altimeter subtrack), where the spacing between tracks is as large as a ring diameter and the time between successive tracks is comparable to the time required to move to another track (an “aliasing” problem).

Mariano (1990) developed a method for combining different types of data to produce a map of a field that preserves typical feature shapes, rather than smearing them out as in an optimal estimate. Optimal estimates (generally known as “objective” maps in oceanography) minimize the expected squared error of the field value; Mariano’s contour analysis produces instead an optimal estimate of the location of each contour of the field values. Thus it preserves the typical magnitudes of the field gradients; i.e., it preserves the shapes and sizes of rings and ocean fronts. Because the gradients affect the dynamics of the field in the simulation, the analyzed contour fields give more realistic input for assimilation into numerical simulation models. Mariano’s method requires a pattern recognition algorithm to first delineate the contours in each type of data, before the optimal estimate of the final contour location can be made.

All of these statistical characterizations using images have in common the problem of detecting features in the presence of extensive cloud contamination or instrument noise; subjective methods have probably been most successful because the human eye can compensate for slight changes in the values of the field and locate a feature by its shape. The problem with subjective methods is that they tend to be labor intensive. A successful automated technique is highly desirable, especially for the case of analyzing large quantities of data (e.g., satellite observations or numerical model output). Ring studies have the additional problem of isolating an elliptically shaped feature that has numerous streamers and smaller eddies attached to it. The delineation of fronts is similar to a contouring problem: a single line must be designated in a noisy field, and the presence of closed contours must be determined to distinguish a ring from the front.


There are several problems in feature identification in sea ice for which good statistical estimators are needed. Some examples are given here. The motion of pack ice, using a feature-tracking method to determine velocities from a sequence of images, is similar to that of cloud motion or movement of water parcels (e.g., Ninnis et al., 1986). This problem is closely related to ocean velocity estimation, which is discussed below. Feature identification algorithms are needed to characterize ice floes (Banfield and Raftery, 1991;

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