Homogeneity assessment of Swiss snow depth series: Comparison of break detection capabilities of (semi-) automatic homogenisation methods

Abstract

Abstract. Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed for temperature and precipitation exist, but with only little experience for snow. We undertook a homogeneity assessment of Swiss snow depth series by running and comparing the results from three well-established semi-automatic break point detection methods (ACMANT, Climatol, and HOMER). Break points identified by each method allowed us to compare the results of the different methods, and by only treating break points as valid if detected in reasonably close proximity by at least two methods, we increased the robustness of the results. We investigated 184 series, of various length between 1930 and 2021 and ranging from 200 to 2500 m a.s.l. and found 45 valid break points. Of those 45, 71 % could be attributed to relocations or observer changes. Metadata are helpful, but not sufficient for break point verification as more than 90 % of recorded events did not lead to valid break points. Using such a combined approach (2 out of 3 methods) is highly beneficial, as it increases the confidence in identified break points in contrast to any single method, with or without metadata.

A quantile‐based approach to improve homogenization of snow depth time series

“…How to cite this article: Resch, G., Koch, R., Marty, C., Chimani, B., Begert, M., Buchmann, M., Aschauer, J., & Schöner, W. (2022). A quantile-based approach to improve homogenization of snow depth time series.…”

“…While this is well researched for variables such as temperature and precipitation (Venema et al, 2012), fewer methods have been developed and tested for homogenizing snow series (Marcolini et al, 2017, 2019; Schöner et al, 2019). A new study focusing on improvements in break detection is currently in review (Buchmann et al, 2022, under review). Regarding the adjustments, the SNHT (Marcolini et al, 2017, 2019) and a modified version of INTERP (Vincent et al, 2002; Schöner et al, 2019), which is implemented in HOMOP (Nemec et al, 2013), have been applied to daily snow depth series.…”