EGU23-12384, updated on 09 Jan 2024
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Simulating snow drift in WRF – First results and future plans of a novel module

Manuel Saigger1, Thomas Mölg1, Christina Schmid, and Tobias Sauter2
Manuel Saigger et al.
  • 1Institute of Geography, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
  • 2Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany

We present a new framework to simulate snow drift in the Weather Research and Forecasting (WRF) model. Here, we show the basic structure of the module, first results of several test applications as well as our future plans with the model.

In past studies, wind-driven redistribution of snow has been shown to greatly influence the spatial structure of snow accumulation. Additionally, sublimation from blowing snow particles can - depending on the atmospheric conditions - act as an important process for mass loss of snow. Hence, to improve our understanding of snow accumulation an accurate representation of snow drift in our models is needed.

Our new module calculates snow drift inside WRF which allows for a more direct coupling to the fields of wind and turbulence. Additionally, with our approach drifting snow sublimation can also feed back into the model’s fields of temperature and moisture and consequently the wind field.

The model has been tested extensively both in idealized and realistic settings in the Alps and provided physically reasonable results consistent with our basic understanding of drifting snow.

With the model we intend to get a deeper understanding on the role of snow drift for glacier mass balance. Our future plans with the model are twofold. For specific case studies high-resolution simulations of drifting snow events will be carried out. Apart from that we plan to expand the evaluation to a (multi-)seasonal perspective. In order to keep a high degree of complexity while staying computationally feasible (also with regard to climatological time scales), we plan to train a deep-learning model on the WRF-simulated fields of snow drift. We intend to use this trained model to reconstruct high-resolution seasonal snow accumulation including drifting snow.

How to cite: Saigger, M., Mölg, T., Schmid, C., and Sauter, T.: Simulating snow drift in WRF – First results and future plans of a novel module, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12384,, 2023.

Supplementary materials

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