A new data mining tool developed by Forrest Hoffman and colleagues could lead to a better understanding of Earth's climate systems and to more accurate models. Using a novel cluster analysis technique to classify and group data, the researchers are able to take extract and compare relevant patterns from enormous datasets. For this study, researchers used data for the Southern Great Plains Atmospheric Radiation Measurement (ARM) site in Lamont, Okla., and projections from the National Center for Atmospheric Research's Community Climate System Model. One finding of note is that the model fails to capture an observed atmospheric state characterized by very low wind shear under high humidity and temperature conditions at the ARM site. Others involved in this project are Salil Mahajan of Texas A&M University, Sigurd Christensen and Richard Mills of ORNL and Bill Hargrove of the U.S. Department of Agriculture Forest Service. The research was funded by the Department of Energy's Office of Biological and Environmental Research, Climate Change Research Division.
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