Clustering techniques identify discrete groups of atmospheric and oceanic structures that occur more frequently than would be expected based on a background distribution, such as a multivariate Gaussian distribution. Some of the techniques identify states that are also unusually long-lived (or persistent).
Examples of atmospheric states identified from cluster analysis include seasonal-mean midlatitude response patterns to El Niño events, and the North Atlantic Oscillation and the Pacific–North America patterns. On weather timescales, cluster analysis has been used to objectively identify a number of typical synoptic patterns familiar to forecasters.
Dr. Straus’s research has applied cluster analysis to better understand the effects of sub-seasonal tropical heating on mid-latitude circulation, and to help categorize extreme precipitation events over North America.