Homogenization of Daily Temperature Data

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 d...

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Bibliographic Details
Published in:Journal of climate 2017-02, Vol.30 (3), p.985-999
Main Authors: Hewaarachchi, Anuradha P., Li, Yingbo, Lund, Robert, Rennie, Jared
Format: Article
Language:English
Subjects:
Anomalies
Autocorrelation
Change detection
Computational efficiency
Computer applications
Computer simulation
Daily
Daily temperatures
Data points
Data processing
Detection
Economic models
Genetic algorithms
Homogenization
Metadata
Methods
Monthly temperatures
Precipitation
Simulation
Temperature
Temperature anomalies
Temperature data
Temperature effects
Time series
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Summary: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.
ISSN:0894-8755
1520-0442
DOI:10.1175/JCLI-D-16-0139.1