By Renata Dmowska, Barry Saltzman
This sequence offers a venue for longer experiences of present advances in geophysics. Written at a degree obtainable to graduate scholars, the articles serve to expand wisdom of assorted fields and will be worthy in classes and seminars.
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Extra info for Advances in Geophysics, Vol. 41
The application of the MTM-SVD methodology to these sparser spatial networks reveals a surprising insensitivity to the precise subsampling of the spatial domain, though the relative prominence and detectability of signals depends on whether or not regions where the signal is strongest are included in the spatial network ŽFig. 15.. The ‘‘checkerboard’’ network grid of case I containing 13 of the 25 gridpoints exhibits an LFV spectrum which is virtually indistinguishable from that of the full grid.
OSCILLATORY SPATIOTEMPORAL SIGNAL DETECTION 47 FIG. 14. Various spatial subsets of the domain used for testing sensitivity to spatial sampling. From Mann Ž1998.. Similar observations hold for the ‘‘Northern Hemisphere’’-only sparse network of 8 gridpoints in case IV. In this case, the sparseness of ‘‘tropical’’ sampling, where the interdecadal signal is most prominent, leads to decreased detectability of the signal; it is just barely isolated at the 99% level of signiﬁcance. Only with the very sparse network of 5 gridpoints in case V does the signal detection procedure suffer markedly.
Both the spatial and the temporal reconstructions are faithful to the exact counterparts ŽFig. , resolving much of the complicated spatial variations in amplitude and phase of the true signals. Since the signals were immersed in spatially correlated red noise, some 42 MICHAEL E. MANN AND JEFFREY PARK OSCILLATORY SPATIOTEMPORAL SIGNAL DETECTION 43 degree of noise contamination is unavoidable, and small spatially correlated errors in both amplitude and phase are evident in the reconstructed spatial patterns.