Adjustment Methods Applied to Precipitation Series with Different Starting Times of the Observation Day

Abstract

The study of long precipitation series constitutes an important issue in climate research and risk assessment. However, long datasets are affected by inhomogeneities that can lead to biased results. A frequent but sometimes underestimated problem is the definition of the climatological day. The choice of different starting times may lead to inhomogeneity within the same station and misalignment with other stations. In this work, the problem of temporal misalignment between precipitation datasets characterized by different starting times of the observation day is analyzed. The most widely used adjustment methods (1 day and uniform shift) and two new methods based on reanalysis (NOAA and ERA5) are evaluated in terms of temporal alignment, precipitation statistics, and percentile distributions. As test series, the hourly precipitation series of Padua and nearby stations in the period of 1993–2022 are selected. The results show that the reanalysis-based methods, in particular ERA5, outperform the others in temporal alignment, regardless of the station. But, for the periods in which reanalysis data are not available, 1-day and uniform shift methods can be considered viable alternatives. On the other hand, the reanalysis-based methods are not always the best option in terms of precipitation statistics, as they increase the precipitation frequency and reduce the mean value over wet days, NOAA much more than ERA5. The use of the series of a station near the target one, which is mandatory in case of missing data, can sometimes give comparable or even better results than any adjustment method. For the Padua series, the analysis is repeated at monthly and seasonal resolutions. In the tested series, the adjustment methods do not provide good results in summer and autumn, the two seasons mainly affected by heavy rains in Padua. Finally, the percentile distribution indicates that any adjustment method underestimates the percentile values, except ERA5, and that only the nearby station most correlated with Padua gives results comparable to ERA5.

Keywords

• daily precipitation
• time of observation
• time series
• aggregation method
• adjustment method
• hourly dataset