Modelling snow in ski resorts with SNOWPACK

Modelling snow in ski resorts with SNOWPACK

SNOWPACK is a sophisticated snow cover model developed by the Swiss Federal Institute for Snow and Avalanche Research (SLF) to assess the physical properties and stability of snowpacks for avalanche warning purposes.

SNOWPACK Model Overview

SNOWPACK uses advanced mathematical techniques to simulate the physical processes occurring within the snowpack.  It employs a Lagrangian finite element implementation to solve non-stationary equations showing how heat moves through the snow (heat transfer equations) and how the snow layers compact and settle over time (settlement equations.) The equations are "non-stationary" because these processes change over time rather than remaining constant.

This method allows the model to track individual snow particles as they move and deform over time. This approach provides a detailed, particle-level view of the snowpack's evolution. 

SNOWPACK also incorporates several crucial processes that contribute to its comprehensive modelling capabilities.  These allow for detailed analysis of energy and mass flows between the atmosphere and the snowpack, which is crucial for understanding snow stability, melt patterns, and potential avalanche risks. 

1. Phase transitions:

   The model simulates how water changes between its solid (ice), liquid (water), and gaseous (water vapour) states within the snowpack. This is crucial for understanding processes like melting, freezing, sublimation, and condensation, which significantly affect snow properties and stability.

2. Liquid water transport:

   SNOWPACK calculates how liquid water moves through the snow layers. This includes modelling processes such as percolation (water seeping downward through the snowpack), water retention in different snow layers, and the formation of impermeable ice layers that can lead to instabilities.

3. Mechanical and physical properties:

   The model focuses on key snow characteristics that influence its behaviour:

   - Thermal conductivity: How efficiently heat is transferred through the snow.

   - Viscosity: The snow's resistance to deformation under stress.

   These properties are critical for understanding how the snowpack responds to temperature changes and physical stresses.

4. Snow metamorphism:

   SNOWPACK simulates the transformation of snow crystals over time. This includes processes like:

   - Rounding of snow grains due to vapour transport

   - Formation of depth hoar (large, cup-shaped crystals) in temperature gradient conditions

   - Wet snow metamorphism when liquid water is present

   These transformations significantly affect snow stability and avalanche risk.

5. Atmospheric interaction:

   The model examines how the snowpack interacts with the atmospheric boundary layer (the lowest part of the atmosphere in direct contact with the snow surface). This includes:

   - Heat exchange between the snow and air

   - Moisture exchange (evaporation and condensation)

   - Wind effects on snow distribution and compaction

6. Radiation penetration:

   SNOWPACK considers how shortwave radiation (primarily from the sun) penetrates the snowpack. This is important because:

   - It affects the energy balance within the snow

   - It can cause internal melting, especially in spring conditions

   - It influences the rate of snowpack warming and subsequent melt processes

By incorporating these processes, SNOWPACK provides a comprehensive simulation of snowpack evolution.

How SNOWPACK is deployed

SNOWPACK is operationally deployed across a network of approximately 160 automatic weather and snow measuring stations throughout Switzerland. These stations are equipped with sensors to measure various environmental parameters:

- Wind speed and direction

- Air temperature

- Relative humidity

- Snow depth

- Surface and soil temperature

- Reflected shortwave radiation

- In select locations, temperatures at three different depths within the snowpack

The stations transmit hourly data to the Swiss Federal Institute for Snow and Avalanche Research (SLF). This real-time data serves as input for the SNOWPACK model, allowing it to generate up-to-date simulations of snowpack conditions at each station location.

By using SNOWPACK in conjunction with these automated stations, the SLF can obtain detailed information about snowpack conditions that goes beyond what the sensors alone can measure. This includes data on internal snowpack structure, stability, and potential avalanche risk factors.

The SNOWPACK system is integrated with a relational database that stores both the raw measurements from the stations and the output from the model simulations. This database allows for efficient data management, analysis, and retrieval of historical information.

While initially developed for use in Switzerland, SNOWPACK has gained recognition internationally. It is now operationally deployed in several other countries, contributing to snow and avalanche monitoring efforts worldwide.

Snowpack Model Validation

Extensive validation studies have been conducted on SNOWPACK, focusing on several key areas:

1. Mass balance calculations: These have been shown to be reliable, accurately tracking snow accumulation and ablation.

2. Energy budget simulations: The model's predictions of energy fluxes within the snowpack have been verified as accurate.

3. Snow metamorphism: SNOWPACK has demonstrated the ability to simulate important processes such as:

   - Formation of depth hoar (large, cup-shaped crystals that often form weak layers)

   - Development of surface hoar (frost crystals that can create unstable interfaces when buried)

These validations have confirmed SNOWPACK's capability to model critical snowpack processes and structures that are relevant to avalanche forecasting.

Summary

The ongoing development of SNOWPACK represents a significant advancement in avalanche safety, promising more accurate and reliable forecasts for ski resorts, backcountry enthusiasts, and avalanche warning services.

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Read how SNOWPACK is being used to predict avalanches in our article : Combining Artificial Intelligence with Human Expertise to Predict Avalanche Danger Levels

You can read more about SNOWPACK on the SLF website.

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