Homogenization of the Probability Distribution of Climatic Time Series: A Novel Algorithm

The aim of the homogenization of climatic time series is to remove non-climatic biases from the observed data, which are caused by technical or environmental changes during the period of observations. This bias removal is generally more successful for long-term trends and annual means than for monthly and daily values. The homogenization of probability distribution (HPD) may improve data accuracy even for daily data when the signal-to-noise ratio favors its application. HPD can be performed by quantile matching or spatial interpolations, but both of them have drawbacks. This study presents a new algorithm which helps to increase homogenization accuracy in all temporal and spatial scales. The new method is similar to quantile matching, but section mean values of the probability distribution function (PDF) are compared instead of individual daily values. The input dataset of the algorithm is identical with the homogenization results for section means of the studied time series. The algorithm decides about statistical significance for each break detected during the homogenization of the section means, and skips the insignificant breaks. Correction terms for removing the inhomogeneity biases of PDF are calculated jointly by a Benova-like equation system, a low pass filter is used for smoothing the prime results, and the mean value of the input time series between two consecutive detected breaks is preserved for each of such sections. This initial version does not deal with seasonal variations either during HPD or in other steps of the homogenization. The method has been tested connecting HPD to ACMANTv5.3, and using overall 8 wind speed and relative humidity datasets of the benchmark of European project INDECIS. The results show 4 to 12 percent RMSE reduction by HPD in all temporal scales, except for the extreme tails where a part of the results are weaker.