Principal-component analysis of sea-ice motion from satellite data
Abstract:The two-dimensional wavelet transform is a highly efficient band-pass filter, which can be used to track features in satellite images from sequential paths. Wavelet analysis of NASA scatterometer and Special Sensor Microwave/Imager data has been used to obtain daily sea-ice drift information for the Arctic region. Comparison with ice motion derived from ocean buoys shows good quantitive agreement. Furthermore, the scatterometer results definitely complement passive-microwave radiometer results when there are cloud or surface effects. This outcome allows three sets of sea-ice-drift daily results from scatterometer, radiometer and buoy data to be merged as a composite map by data-fusion techniques. Based on the composite maps, the ice-flow streamlines are highly correlated with surface air-pressure contours. In order to quantify the wind effects on ice motion, empirical orthogonal functions are used in the principal-component analysis to isolate generalized patterns inherent in 6 months (fall/winter) of daily sea-ice motion data. It is found that 30% of sea-ice motion is highly correlated with 50% of the pressure field in modes 1and 2. For the higher modes, sea-ice motion is also affected by ocean current, bathymetry and coastal boundary and therefore is not highly correlated with the wind field.
Document Type: Research Article
Publication date: January 1, 2001
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