Dr. Jon Woody
Associate Professor of Statistics
Research interests: stochastic modeling, time series, changepoint problems, and statistical climatology.
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My current research is devoted to developing statistical methods and theory towards understanding the interaction between a changing climate and cryospheric processes. A cryospheric process is one that involves permafrost, seasonally frozen soil, snow, ice, or any physical entity that freezes and thaws. As of yet, most data driven trend estimates on cryoshperic processes come from simple linear trend estimation, ignoring statistical correlation of time series, changepoint effects, and the zero modified support set issue-snow depths cannot be negative.
One of my current research projects introduces proper statistical modeling expertise to the snow depth trends community. This is not an easy task, as some of the most cited work essentially applied simple linear regression on yearly snow depth averages to compute trends. The problem is statistically much more problematic than current methods are equipped to handle. Note than one can replace snow depths with Snow Water Equivalents (SWE’s) if one has such data without having to change the statistical methodologies developed.