EGU2020-17211, updated on 18 Nov 2021
https://doi.org/10.5194/egusphere-egu2020-17211
EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Comparing methods for gap filling in historical snow depth time series

Johannes Aschauer1, Mathias Bavay1, Michael Begert2, and Christoph Marty1
Johannes Aschauer et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 2Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland

Switzerland has a unique dataset of long-term manual daily snow depth time series ranging back more than 100 years for some stations. This makes the dataset predestined to be analyzed in a climatological sense. However, there are sometimes shorter (weeks, months) or longer (years) gaps in these manual snow depth series, which hinder a sound climatological analysis and reasonable conclusions. Therefore, we examine different methods for filling data gaps in daily snow depth series. We focus on longer gaps and use different methods of spatial interpolation, temperature index models and machine learning approaches to fill the data gaps. We assess the performance of the different methods by creating synthetic data gaps and set the applicability of the methods in relation to the density of the available neighboring stations, elevation and climatic setting of the target station.

How to cite: Aschauer, J., Bavay, M., Begert, M., and Marty, C.: Comparing methods for gap filling in historical snow depth time series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17211, https://doi.org/10.5194/egusphere-egu2020-17211, 2020.

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