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  \textbf{\textsc{STANFORD UNIVERSITY}}\\[5pt]
  \textbf{\textsc{DEPARTMENT OF STATISTICS}}\\[5pt]
  \Large{\textbf\textsc{{DEPARTMENTAL SEMINAR}}}
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\begin{center}
  4:15 p.m., Tuesday, May 2, 2006\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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\begin{center}
  \textsl{Michael Stein    }\\
University of Chicago
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\begin{center}
  \textbf{ Seasonal variations in the spatial-temporal dependence \\
of total column ozone
   } 
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\noindent
The Total Ozone Mapping Spectrometer (TOMS) is
a satellite-based instrument that measures total
column ozone on a daily basis over a
fairly dense spatial grid with near global coverage.  Statistical models for
the spatial-temporal variations in total column ozone provide insights into
ozone dynamics, are valuable for obtaining inferences on long-term trends in
ozone levels, and can be used to predict ozone levels at unobserved points in
space-time.
However, developing such a model is complicated by the seasonally varying
nature of the space-time dependence and the partial confounding of spatial and
temporal variation caused by the sun-synchronous orbit of the satellite.
I will consider methods for
describing, modeling and estimating the seasonal patterns in the dependence
structure for measurements at a single latitude.
Applying one of these models to the synoptic prediction of total column ozone
at a latitude shows that there is at least the potential for substantially
improved predictions by exploiting the high spatial density of observations
TOMS provides.
I will also take a brief look at a less processed version of the
TOMS data in an effort to disentangle smaller scale
spatial and temporal variation.

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