Snow on the ground is an important climate variable which is normally measured either as snow depth or height of new snow. Like any other meteorological variable, manually measured snow is prone to local influences, changes in the environment or procedure of the measurements. In order to investigate the robustness of snow measurement series towards such non‐climatic changes, a unique set of parallel manual snow measurements over 25 years from 23 station pairs between 490 and 1800 m a.s.l. was compiled. A sensitivity analysis based on typical snow climate indicators (e.g., mean snow depth, sum of new snow) from these parallel time series was carried out to find the most robust snow climate indicators for climatological analyses. Results show that there are only small differences in the sensitivity of the various snow climate indicators with regards to local changes. However, the indicators number of days with snow on the ground as well as the maximum snow depth are least affected by local influences and changes at station level. Median values of all station pairs reveal relative differences of about 7% for the number of days with snow cover and 11–16% for all other indicators. However, in extreme cases, the deviations within a single station pair can reach 25–40%.

中文翻译：

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使用一套独特的平行降雪测量系列评估降雪气候指标的稳健性

地面上的积雪是重要的气候变量，通常以积雪深度或新积雪高度来衡量。像任何其他气象变量一样，手动测量的积雪也容易受到局部影响，环境变化或测量过程。为了研究降雪系列对这种非气候变化的鲁棒性，编制了一套独特的平行人工降雪测量集，这些测量来自25个490至1800 m asl之间的23个站对。根据来自这些平行时间序列的典型降雪气候指标（例如，平均降雪深度，新雪总量）进行了敏感性分析，以找到用于气候分析的最可靠的降雪气候指标。结果表明，就局部变化而言，各种降雪气候指标的敏感性仅有很小的差异。但是，指标在地面上的积雪天数以及最大积雪深度受本地影响和站点级别变化的影响最小。所有测站对的中值显示有积雪天数的相对差异约为7％，而所有其他指标的相对差异为11-16％。但是，在极端情况下，单个站对内的偏差可能会达到25–40％。所有测站对的中值显示有积雪天数的相对差异约为7％，而所有其他指标的相对差异为11-16％。但是，在极端情况下，单个站对内的偏差可能会达到25–40％。所有测站对的中值显示有积雪天数的相对差异约为7％，而所有其他指标的相对差异为11-16％。但是，在极端情况下，单个站对内的偏差可能会达到25–40％。