Articles | Volume 16, issue 6
https://doi.org/10.5194/tc-16-2147-2022
https://doi.org/10.5194/tc-16-2147-2022
Research article
 | 
09 Jun 2022
Research article |  | 09 Jun 2022

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

Moritz Buchmann, John Coll, Johannes Aschauer, Michael Begert, Stefan Brönnimann, Barbara Chimani, Gernot Resch, Wolfgang Schöner, and Christoph Marty

Related authors

The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland
Moritz Buchmann, Gernot Resch, Michael Begert, Stefan Brönnimann, Barbara Chimani, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 17, 653–671, https://doi.org/10.5194/tc-17-653-2023,https://doi.org/10.5194/tc-17-653-2023, 2023
Short summary
Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements
Moritz Buchmann, Michael Begert, Stefan Brönnimann, and Christoph Marty
The Cryosphere, 15, 4625–4636, https://doi.org/10.5194/tc-15-4625-2021,https://doi.org/10.5194/tc-15-4625-2021, 2021
Short summary

Related subject area

Discipline: Snow | Subject: Seasonal Snow
Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?
Benjamin Poschlod and Anne Sophie Daloz
The Cryosphere, 18, 1959–1981, https://doi.org/10.5194/tc-18-1959-2024,https://doi.org/10.5194/tc-18-1959-2024, 2024
Short summary
Which global reanalysis dataset represents better in snow cover on the Tibetan Plateau?
Shirui Yan, Wei Pu, Yang Chen, Yaliang Hou, Xuejing Li, Yuxuan Xing, Dongyou Wu, Jiecan Cui, Yue Zhou, and Xin Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-82,https://doi.org/10.5194/egusphere-2024-82, 2024
Short summary
Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry
Zachary Hoppinen, Shadi Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, and Carrie Vuyovich
The Cryosphere, 18, 575–592, https://doi.org/10.5194/tc-18-575-2024,https://doi.org/10.5194/tc-18-575-2024, 2024
Short summary
Spatiotemporal snow water storage uncertainty in the midlatitude American Cordillera
Yiwen Fang, Yufei Liu, Dongyue Li, Haorui Sun, and Steven A. Margulis
The Cryosphere, 17, 5175–5195, https://doi.org/10.5194/tc-17-5175-2023,https://doi.org/10.5194/tc-17-5175-2023, 2023
Short summary
Evaluation of snow cover properties in ERA5 and ERA5-Land with several satellite-based datasets in the Northern Hemisphere in spring 1982–2018
Kerttu Kouki, Kari Luojus, and Aku Riihelä
The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023,https://doi.org/10.5194/tc-17-5007-2023, 2023
Short summary

Cited articles

Aguilar, E. and Llanso, P.: Guidelines on climate metadata and homogenization, World Meteorological Organization, WCDMP-No. 53, https://library.wmo.int/doc_num.php?explnum_id=10751 (last access: 8 June 2022), 2003. a
Alexandersson, H.: A homogeneity test applied to precipitation data, J. Climatol., 6, 661–675, https://doi.org/10.1002/joc.3370060607, 1986. a
Alexandersson, H. and Moberg, A.: Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends, Int. J. Climatol., 17, 25–34, https://doi.org/10.1002/(sici)1097-0088(199701)17:1<25::aid-joc103>3.0.co;2-j, 1997. a, b, c
Aschauer, J. and Marty, C.: Providing Data Provision for a Sensitivity Analysis of Snow Time Series, resreport, WSL Institute for Snow and Avalanche Research SLF, research Report for GCOS Switzerland, https://www.meteoschweiz.admin.ch/content/dam/meteoswiss/en/Forschung-und-Zusammenarbeit/Internationale-Zusammenarbeit/GCOS/doc/Final_report_Poviding_Data_Provision_for_a_Sensitivity_Analysis_of_Snow_Time_Series.pdf (last access: 8 June 2022), 2020. a
Begert, M., Schlegel, T., and Kirchhofer, W.: Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000, Int. J. Climatol., 25, 65–80, https://doi.org/10.1002/joc.1118, 2005. a
Download
Short summary
Knowledge about inhomogeneities in a data set is important for any subsequent climatological analysis. We ran three well-established homogenization methods and compared the identified break points. By only treating breaks as valid when detected by at least two out of three methods, we enhanced the robustness of our results. We found 45 breaks within 42 of 184 investigated series; of these 70 % could be explained by events recorded in the station history.