Abstract:
This paper develops a method for homogenizing daily temperature series. While daily temperatures are
statistically more complex than annual or monthly temperatures, techniques and computational methods
have been accumulating that can now model and analyze all salient statistical characteristics of daily temperature
series. The goal here is to combine these techniques in an efficient manner for multiple changepoint
identification in daily series; computational speed is critical as a century of daily data has over 36 500 data
points. The method developed here takes into account 1) metadata, 2) reference series, 3) seasonal cycles, and
4) autocorrelation. Autocorrelation is especially important: ignoring it can degrade changepoint techniques,
and sample autocorrelations of day-to-day temperature anomalies are often as large as 0.7. While daily homogenization
is not conducted as commonly as monthly or annual homogenization, daily analyses provide
greater detection precision as they are roughly 30 times as long as monthly records. For example, it is relatively
easy to detect two changepoints less than two years apart with daily data, but virtually impossible to flag
these in corresponding annually averaged data. The developed methods are shown to work in simulation
studies and applied in the analysis of 46 years of daily temperatures from South Haven, Michigan.
