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Sudden changes in local mean values demarcate geophysical regimes

ScholarsArchive at Oregon State University

Field Value
Title Sudden changes in local mean values demarcate geophysical regimes
Names Howell, James F., 1965- (creator)
Mahrt, Larry (advisor)
Date Issued 1995-12-08 (iso8601)
Note Graduation date: 1996
Abstract Sudden changes occur where the mean values associated with two adjacent non-overlapping
windows of data are anomalously different, and the transition between the
window means occurs over a scale that is small relative to the scale of the windows.
Positions of sudden changes can be economically retrieved. The sudden change positions
demarcate the data in a manner that can be physically interpreted. Associated with this
thesis, are data analyses in terms of the scales, positions, and magnitudes of sudden
changes in local (window) mean data values.
A sudden change ideally includes an anomalously steep small scale gradient that
is associated with change on a much larger scale. Preserving this structure when filtering
small scale variance requires an adaptive cutoff scale, as constructed in the third study.
The filter adapts a local cutoff scale to the scales, locations and relative magnitudes of the
local extremes in the Haar transform, which ideally responds to sudden changes. In the
fourth study a filter using a variable cutoff scale is applied in order to partition a nine hour
time series of wind velocity. The variable cutoff scale filter separated a transport mode
from an isotropic small scale mode more cleanly, in terms of traditional statistics, than
did a constant cutoff scale filter. Generally, the positions of sudden changes distinguish
windows of data. Windows can be centered on the sudden changes or between them. In
the fifth study the sudden changes define boundaries of data windows. The within-window
data then contains less variance associated with sudden changes, which deterministically
occur between adjacent windows. A sampling procedure based on the locations of the
sudden changes is applied in the sixth study in an analysis of surface layer measurements.
The "non-random" sampling helps to clarify spatial and temporal patterns in samples of
the mean wind and the turbulence stress; the "mesoscale effect" is less ambiguous.
Genre Thesis/Dissertation
Topic Atmosphere -- Observations
Identifier http://hdl.handle.net/1957/29063

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