Weather and Climate Extremes in a Changing Climate. Regions of ...

The U.S. Climate Change Science Program Chapter 2
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Direct application of GEV methods is often
inefficient because they only use very sparse
summaries of the data (typically one value per
year), and need reasonably long time series
before they are applicable at all. Alternative
methods are based on exceedances over
thresholds, not just counting exceedances but
also fitting a distribution to the excess over
the threshold. The most common choice of
distribution of excess is the Generalized Pareto
distribution or GPD, which is closely related to
the GEV (Pickands, 1975; Davison and Smith,
1990). Some recent overviews of extreme value
distributions, threshold methods, and a variety
of extensions are by Coles (2001) and Smith
(2003).
Much of the recent research (e.g., Wehner, 2005;
Kharin et al., 2007) has used model output
data, using the GEV to estimate, for example,
a 20-year return value at each grid cell, then
plotting spatial maps of the resulting estimates.
Corresponding maps based on observational
data must take into account the irregular spatial
distribution of weather stations, but this is also
possible using spatial statistics (or kriging)
methodology. For example, Cooley et al. (2007)
have applied a hierarchical modeling approach
to precipitation data from the Front Range of
Colorado, fitting a GPD to threshold exceedances
at each station and combining results
from different stations through a spatial model
to compute a map of 25-year return values.
Smith et al. (2008) applied similar methodology
to data from the whole contiguous United
States, producing spatial maps of return values
and also calculating changes in return values
over the 1970-1999 period.