How raceday.ski calculates the snow temperature

Wax charts refer to the snow temperature — yet in everyday practice it is almost always the air temperature that is measured. The two can be several degrees apart: at night the snow surface radiates heat towards the sky and cools below the air temperature; during the day, sun position, aspect and slope angle warm the surface again. That is why raceday.ski does not estimate the snow temperature from rules of thumb but calculates it physically — hour by hour, from the previous evening up to the selected start time.

Data sources

• Open-Meteo — hourly weather forecasts (temperature, humidity, cloud cover, wind, precipitation, snowfall, snow depth, direct and diffuse radiation) plus temperature and geopotential on eight pressure levels.
• WSL Institute for Snow and Avalanche Research SLF — measurements from the IMIS stations, including the measured snow surface temperature (data: WSL/SLF, CC BY 4.0).
• Digital elevation models (Copernicus GLO-30, SRTM, EU-DEM) — for horizon profiles, shading and the validation of the calculation points.

The 3-layer energy-balance model

The core of the calculation is a snowpack model with three layers (surface ~3 cm, intermediate layer ~17 cm, base ~80 cm) that solves the energy balance of the surface hour by hour:

• Shortwave radiation: direct and diffuse solar radiation, corrected for slope angle, aspect and horizon shading; the albedo ages with the snow (CLASS scheme) and is reset by fresh snow and the morning slope grooming.
• Longwave radiation: atmospheric counter-radiation after Prata (1996) with the Unsworth-Monteith cloud correction; plus the emission of the snow surface itself — the mechanism that makes clear nights so cold.
• Latent heat: sublimation and condensation as a function of humidity, wind and altitude (saturation vapour pressure over ice).
• Heat conduction: between the layers, with density-dependent conductivity after Sturm et al. (1997) — groomed piste snow conducts very differently from loose fresh snow.

The snow type itself (fresh snow, old snow, slush, machine-made snow …) is classified from the last 48 hours of weather history and determines the density, albedo and conductivity of the model. The moisture class follows the international snow classification (ICSSG).

Terrain: altitude, horizon, shading

The air temperature is converted to the target altitude via a dynamic lapse rate derived from pressure-level data — instead of a fixed gradient. This also detects inversions, where it is warmer up high than down in the valley.

For every location in the catalogue, a horizon profile is stored: horizon angles in 36 compass directions computed from digital elevation models, together with the sky-view factor. This tells the model when a slope lies in the shadow of a mountain massif and how much sky the piste “sees” for night-time outgoing radiation. The calculation points of all 1,100+ catalogue entries were validated against digital elevation models, and the horizon profiles were computed from terrain data (sampling from 150 m distance in 11 steps) — catalogue-wide quality assurance, documented in the internal QA report.

Measuring-station calibration (SLF-IMIS)

Physical models have systematic residual errors. That is why raceday.ski continuously checks the calculated snow temperature against real measurements: more than 130 IMIS stations of the SLF provide measured snow surface temperatures from the Swiss Alps. If a station is close to the selected ski resort, a Kalman filter learns the local model bias per station and time of day and corrects the forecast accordingly — with strict selection of the measurement pairs (only near-time measurements on the forecast day, no duplicates). A rule-based residual correction additionally catches known model weaknesses.

In Austria, raceday.ski additionally validates against the surface-temperature stations of the Tyrol Avalanche Warning Service (Datenquelle: Land Tirol - data.tirol.gv.at, CC BY 4.0). Outside these measurement networks, the model runs without measurement anchoring — this is honestly reflected in the displayed uncertainty.

Uncertainty: the ± band

Every calculation yields a confidence score from the factors cloud cover, wind, temperature stability, measuring-station proximity, terrain profile, lapse-rate quality and snow-classification confidence; special situations (foehn, rain on snow, inversion) apply fixed deductions. Instead of an abstract percentage, raceday.ski displays this as an uncertainty band on the snow temperature (e.g. −6.5 ±1.5 °C) in steps from ±0.5 to ±4 °C. The mapping is deliberately conservative and is continuously recalibrated with the collected measurement-model pairs. Special situations are additionally displayed as a warning flag.

From snow temperature to wax recommendation

The recommendation selects from 127 products by Swix, Toko, Holmenkol, HWK and Rex. Scoring is based on temperature fit (centring within the manufacturer's range), snow type, moisture and user feedback; curated brand templates take precedence when they match exactly. The temperature ranges in the database were verified product by product against published manufacturer specifications — every verified product carries its source and verification date. Where manufacturers publish moisture bands (currently only Swix World Cup), they feed directly into the scoring.

The result is deliberately verifiable: the transparency block of every recommendation shows the derivation — from the air temperature via the night minimum to the snow temperature, including physics parameters and score breakdown.

Limits of the model

• The calculation is based on weather forecasts — their errors propagate, especially in unstable situations.
• Race-specific preparation (water injection, salting) can change the piste locally to a large degree and is not modelled.
• Foehn, rain on snow and inversions violate model assumptions — they are detected and flagged with a wider uncertainty band, but remain difficult.
• The systematic comparison of model vs. measuring stations runs as an ongoing measurement campaign over the winter season — the evaluation is disclosed on the accuracy page and fills in automatically as soon as season data are available.

References

• Prata, A. J. (1996): A new long-wave formula for estimating downward clear-sky radiation at the surface. Q. J. R. Meteorol. Soc. 122, 1127–1151.Atmospheric emissivity (clear sky)
• Unsworth, M. H. & Monteith, J. L. (1975): Long-wave radiation at the ground. Q. J. R. Meteorol. Soc. 101, 13–24.Cloud correction of the incoming longwave radiation
• Juszak, I. & Pellicciotti, F. (2013): A comparison of parameterizations of incoming longwave radiation over melting glaciers. J. Geophys. Res. Atmospheres 118, 3066–3084.Validation of the longwave parameterisation in high mountains
• Verseghy, D. L. (1991): CLASS — A Canadian land surface scheme for GCMs. I. Soil model. Int. J. Climatol. 11, 111–133.Ageing scheme of the snow albedo
• Sturm, M., Holmgren, J., König, M. & Morris, K. (1997): The thermal conductivity of seasonal snow. J. Glaciol. 43(143), 26–41.Thermal conductivity as a function of snow density
• Alduchov, O. A. & Eskridge, R. E. (1996): Improved Magnus form approximation of saturation vapor pressure. J. Appl. Meteor. 35, 601–609.Saturation vapour pressure over water and ice (sublimation, dew point)
• Fierz, C. et al. (2009): The International Classification for Seasonal Snow on the Ground (ICSSG). UNESCO-IHP, Paris.Moisture classification of the snow
• NOAA Solar Position Algorithm (nach Meeus, Astronomical Algorithms).Solar position (elevation/azimuth) per hour

Snow data: WSL Institute for Snow and Avalanche Research SLF (www.slf.ch), CC BY 4.0 · Weather data: Open-Meteo · Terrain data: Copernicus GLO-30, SRTM, EU-DEM (© European Union).