A comparison of imputation methods for the consecutive missing temperature data

Consecutive missing values are likely to occur in long climate data due to system error or defective equipment.Furthermore, it is difficult to impute missing values. However, these complicated problems can be overcameby imputing missing values with reference time series. Reference time series must be composed of similartime series to time series that include missing values. We performed a simulation to compare three missingimputation methods (the adjusted normal ratio method, the regression method and the IDW method) tocomplete the missing values of time series. A comparison of the three missing imputation methods for thedaily mean temperatures at 14 climatological stations indicated that the IDW method was better thanxothers at south seaside stations. We also found the regression method was better than others at moststations (except south seaside stations).Keywords: consecutive missing value, missing value imputation, adjusted normal ratio methods, regressionmethod, IDW method