Biography Dr. Richman has a wide range of interests, including analysis of global climate models, examination of the climate dynamics associated with El NiÃ±o/Southern Oscillation (ENSO), interaction of planetary- and synoptic-scale features, analysis of climate variability on both the intra-seasonal and interannual time scales, application of data mining to different radar platforms and statistical methodology. His work has involved analysis of four-dimensional climate models on supercomputers, using high-performance and massively parallel algorithms. Additionally, his expertise in statistical meteorology has led to development of multivariate techniques that summarize very large data sets, identifying their modal patterns, as well as eigentechniques that search for theoretical patterns in observed and modeled data. He has served several terms on the American Meteorological Society's Committee on Probability and Statistics as well as their Committee on Artificial Intelligence. Most recently, Dr. Richman's research efforts have focused on satellite data thinning and tornado outbreak forecasting using support vector regression. That work was recently awarded the Best Theoretical Paper Award and the Best Novel Smart Engineering Award at the 2010 ANNIE meeting. His other work is determining the predictability of the climate system for relevance to the energy industry, North American climate variability, including development of climate scenarios, examination of low frequency variability in the climate system, linkages between ENSO and global circulation, and devising multivariate methodologies to determine how well climate models depict observed variability.