Time series analysis of summer hot spells in northeastern Illinois and southeastern Wisconsin : an application for utilities

Abstract

Includes bibliographical references (pages [65]-67)Recent changes in the utility industry brought about by changes in government policy are promoting more efficient management. As a result, some utility managers now view climate analysis as a tool that may help them gain a competitive edge in the energy marketplace. This thesis used time series analysis to examine summer hot spells in northeastern Illinois and southeastern Wisconsin. The intent was to provide utility managers with useful information about summer hot spells that would allow for improved management decisions and strategies. The goals of this research are fourfold: (1) construct deterministic, dynamic-stochastic time series models of summer daily maximum air temperatures for twenty-two sites within the study region; (2) generate hot spell counts and duration probabilities for each site; (3) assess the accuracy of the models; and (4) examine summer hot spells geographically and temporally. The expectation is that the information from this hot spell analysis could be used in conjunction with long-range climate forecasts. It was found that Lake Michigan had a pronounced effect on the geography of summer hot spell frequencies and cumulative duration probabilities. Those sites closest to the lake experienced the highest total number of hot spells, but also had the lowest probabilities of a hot spell lasting for more than one day. This is probably due to the fact that lakeshore sites had lower over all average maximum temperatures and greater maximum temperature variance. Average frequencies of summer hot spell durations for warm, average, and cool summers were generated. The three summer types varied considerably. At inland sites, warm summers average 6.61 more hot spells and 19.5 more hot spell days than cool summers. It was found that the first-order autoregressive model which relied only on the autocorrelation coefficient and a random error term could not reproduce synthetic hot spells with the same frequency and magnitude that was exhibited in the empirical data. It is surmised that anticyclones or highs which may persist for days and cause hot spell conditions may not be modeled well by a firstorder autoregressive process.M.S. (Master of Science