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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. [ABSTRACT FROM AUTHOR]

Long term time series of climate observations measured at meteorological stations provide one of the most accurate records of climate in the past. Nevertheless, there are a number of factors that affect measurements of climatic parameters which may cause abrupt or more gradual shifts and trends in the corresponding time series. Thus in order to draw sound conclusions about the climate, homogenized data series are required. Because of the large number of different monthly homogenization methods a coordinated European initiative was launched namely the COST Action HOME ES0601: Advances in Homogenization Methods of Climate Series: an integrated approach ( HOME). Its main objective was to review and improve common homogenization methods and to assess their impact on climate time series. In this paper the homogenization method HOMER resulted by the COST Action ES0601, was applied to 52 monthly temperature series of synoptic stations for the period 1960-2004, belonging to the operational weather network of the Hellenic National Meteorological Service ( HNMS). Eight stations passed the homogeneity test successfully without any break point or outlier, for two stations only outliers were reported, but not any break points and the remaining 42 stations presented one or more breaks. Approximately 32% of the breaks could be explained by the stations' history. Station relocations as well as changes in observation practices caused most of the temperature anomalies. The adjustments in yearly temperature series were in the range from −1.2 to 2.0 °C. Seasonal and annual anomalies of raw and homogenized series were calculated using as reference the 30-year period 1961-1990. Also, seasonal and annual mean temperature trends before and after homogenization were analysed and their statistical significance calculated. A reduction of trend magnitude was found to be noticeable in all seasons. The most pronounced trend differences were found in summer followed by winter. [ABSTRACT FROM AUTHOR]