Abstract
A method is described whereby climatological time series of temperature and precipitation can be adjusted for station inhomogeneities using station history information. The adjusted data retains its original scale and is not an anomaly series. The methodology uses the concepts of relative homogeneity and standard parametric (temperature) and nonparametric (precipitation) statistics. The technique has been tested in Monte Carlo simulations, and is shown to product climatological time series more consistent with the concept of a homogeneous climate record than would other be the case. Additionally, the technique provides an estimate of the confidence interval associated with each adjustment. It has been applied to over 1200 stations in the United States. In many instances the adjustments in temperature time series are substantial (as large as actual climate fluctuations during the twentieth century) often leading to a more consistent pattern of regional climate change than would otherwise be surmised from inspection of the unadjusted data.
