A Dynamic Method for Gap Filling in Daily Temperature Datasets

A regression-based approach for temperature data reconstruction has been used to fill the gaps in the series of automatic temperature records obtained from the meteorological network of the Veneto Region (North-eastern Italy). The method presented is characterised by a dynamic selection of the reconstructing stations and of the coupling period which can precede or follow the missing data. The best sampling size is determined through an inference procedure, permitting a highly specific selection of the parameters used to fill each gap in the time series. With a proper selection of the functioning parameters, the average errors of reconstruction are close to 0 and those corresponding to the 95th percentile are typically around 0.1 °C. Even considering the propagation error related to the length of the gap to be filled, the reliability of the method remains very high. In comparison with similar regression-based approaches, the errors are lower, particularly for minimum temperatures, and the method limits inversions between minimum, mean and maximum temperatures to a minimum.