Application of nearest-neighbor resampling techniques for homogenizing temperature records on a daily to sub-daily level
Published in Int. J. Climatology 25, 75-89, doi:10.1002/joc.1236 in 2006
T. Brandsma and G. P. Können
Nearest-neighbor resampling is introduced as a means for homogenizing temperature records on a daily to sub-daily level. Homogenization refers here to the problem of calculating daily mean and sub-daily temperatures from a time series subject to irregular observation frequencies and changing observation schedules. The method resamples diurnal temperature cycles from an observed hourly temperature subrecord at the station. Unlike other methods, the technique maintains the variance in a natural way. This property is especially important for the analysis of trends and variability of extremes. For a given day, the resampling technique does not generate a single-valued solution but this peculiarity is of no effect in the applications considered here. The skills of the nearest-neighbor resampling technique, in terms of bias, RMSE, and variability, are compared with those of four other methods: a sine-exponential model, a model that uses the climatological mean daily cycle, a regression model for calculating daily values, and a deterministic version of the nearest-neighbor technique. The series used in the tests is the 1951-2000 meteorological record of De Bilt (The Netherlands). The emphasis in the comparisons is on the reconstruction of daily mean temperatures. The analysis shows important differences in performance between the models. The regression-based method performs best with respect to the calculation of the individual daily mean temperatures; the day-to-day variability is best reproduced with the nearest-neighbor resampling technique. The performance of the models improves when cloudiness is used as an extra predictor. The improvement is, however, small compared to the intermodel differences. The type of model that should be used depends on the desired application. For trend and variability studies, the nearest-neighbor resampling technique performs best. Nearest-neighbor resampling can successfully be performed even in situations where the length of the hourly subrecord is an order of magnitude less than the length of the series to be homogenized.