Thursday, 27 January 2011: 2:30 PM
4C-4 (Washington State Convention Center)
Extreme cold spikes in wintertime temperatures increase demand for heating, which in turn, leads to greater usage of commodities such as oil and natural gas. Short-term temperature extremes, both hot and cold, are highly sensitive to climate time scales as climate variability affects both the mean and variance structure of daily temperatures as they evolve over a season. Large-scale climate and weather information can be used to condition medium-range weather forecasts in order to gain skill in predicting these extreme temperature events. This study considers wintertime cold snaps over a large region of the Northeastern and Midwestern United States with respect to 500mb synoptic precursors. Forecasting tools are designed for straightforward operational application by practicing meteorologists working in energy load forecasting.
Common synoptic precursors to severe cold events are identified as likely indicators of an upcoming cold snap, offering potential for enhancing energy load forecasting. Specifically, several patterns identified using principal components analysis applied to hemispheric 500 mb geopotential height data are shown to have significant leading relationships with severe cold weather. Leading up to a cold outbreak, these patterns tend to evolve and organize in a way that produces a similar atmospheric state at the start of the cold event, which is typically marked by a strong center of high pressure over western North America. When these patterns are present and highly anomalous (e.g. 2SD events), the probability that a severe cold episode will follow increases as compared to 1SD or zero-threshold events. The results of the probabilistic analysis show the potential for increasing lead-times of severe cold forecasts on the order of 1-2 weeks or, in some cases, longer.
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