S52 Composite Analysis of Pittsburgh, PA

Sunday, 23 January 2011
Christopher Werner, California University of Pennsylvania, California, PA; and C. Kauffman

Composite Analysis is the monthly averages of temperature or precipitation data configured together with the CPC Niño 3.4 consolidated SST forecast so as to predict monthly temperature anomalies for a given locale. It is the goal of this study to determine if Composite Analysis can be successfully applied to a 60-year data set in western Pennsylvania for a winter period. In this study, monthly average temperatures at Pittsburgh, PA from 1950-2010 were manipulated in an analytic database. Through various formulae, tercile points were identified in the period from 1980-2010 to find the probability of above/below “normal.” For each of the months from January, February, and March of 2011, average monthly temperatures are expected to be above the “normal” temperature for the time period. A hypergeometric distribution was used to help determine if historical temperature anomalies were significantly deviant. A hypothetical risk analysis scheme was applied to assist in making the public aware of potential anomalous temperature patterns. A Composite Analysis of many initial regions for data ingestion can assist in uncovering future Synoptic weather patterns for other geographic zones. With the now abundant number of climatic oscillations available for analysis (e.g., PDO, ENSO, NAO, etc.), Composite Analysis should attempt to include as many of these variant oscillations as possible to determine the potential resultant influences they each engender in the climate system. Ultimately, an increase in Composite Analysis forecast precision may help save lives from climate-related hazards.
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