A Method of Homogenizing the Extremes and Mean of Daily Temperature Measurements

Abstract

To be confident in the analyses of long-term changes in daily climate extremes, it is necessary for the data to be homogenized because of nonclimatic influences. Here a new method of homogenizing daily temperature data is presented that is capable of adjusting not only the mean of a daily temperature series but also the higher-order moments. This method uses a nonlinear model to estimate the relationship between a candidate station and a highly correlated reference station. The model is built in a homogeneous subperiod before an inhomogeneity and is then used to estimate the observations at the candidate station after the inhomogeneity using observations from the reference series. The differences between the predicted and observed values are binned according to which decile the predicted values fit in the candidate station’s observed cumulative distribution function defined using homogeneous daily temperatures before the inhomogeneity. In this way, adjustments for each decile were produced. This method is demonstrated using February daily maximum temperatures measured in Graz, Austria, and an artificial dataset with known inhomogeneities introduced. Results show that given a suitably reliable reference station, this method produces reliable adjustments to the mean, variance, and skewness.