Hemsedal Snow Forecast — Viken, Norway

Hemsedal (Viken, Norway) expects 0.0 cm of snow in the next 72 hours and 0.0 cm over the next 7 days. Current snow depth at the summit (1450 m): 1 cm. Forecast confidence: 95%. Updated 2026-06-21T12:00:25.495Z UTC from a weighted consensus of 4 weather models (GFS, ECMWF, ICON, GEM).

16-day weighted-consensus forecast for Hemsedal
DateSnowfall (cm)Min/Max °CConditions
Sun 21 Jun0.04.3° / 10°Slushy
Mon 22 Jun0.02.8° / 11.4°Slushy
Tue 23 Jun0.03.9° / 11.6°Slushy
Wed 24 Jun0.07.5° / 14.9°Slushy
Thu 25 Jun0.07.1° / 15.6°Slushy
Fri 26 Jun0.06.5° / 22.8°Slushy
Sat 27 Jun0.011.8° / 18.7°Slushy
Sun 28 Jun0.010° / 19.4°Slushy
Mon 29 Jun0.013° / 16.6°Slushy
Tue 30 Jun0.09.1° / 14°Slushy
Wed 1 Jul0.07.4° / 9.9°Slushy
Thu 2 Jul0.08.1° / 11.5°Slushy
Fri 3 Jul0.06.4° / 9.8°Slushy
Sat 4 Jul0.05.2° / 11.3°Slushy
Sun 5 Jul0.07.3° / 15°Slushy
Mon 6 Jul0.04° / 9.7°Slushy

7-day totals per model: GFS 0.0 cm · ECMWF 0.0 cm · ICON 0.0 cm · GEM 0.3 cm — regional weights for Europe: GFS ×0.85 · ECMWF ×1.25 · ICON ×1.35 · GEM ×0.75

FAQ

How much snow will fall at Hemsedal this week?
0.0 cm of snowfall is forecast at Hemsedal between 2026-06-21 and 2026-06-27, based on a weighted consensus of the GFS, ECMWF, ICON and GEM weather models.

What is the current snow depth at Hemsedal?
The latest reported snow depth at Hemsedal (summit 1450 m) is 1 cm, updated 2026-06-21T12:00:25.495Z UTC.

How reliable is this forecast?
Confidence is 95% (high), based on agreement between 4 independent weather models (GFS, ECMWF, ICON, GEM) blended with Europe regional weights. Days 1-7 are the most reliable; days 8-16 indicate the trend.

More in Norway: Geilo · Trysil · Best powder this week · All 188 resorts

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Cross-referenced from 4 models: GFS, ECMWF, ICON & GEM

Regional model weighting

The forecast you see is a weighted consensus of 4 weather models, and the weights change by region — because each model is demonstrably stronger in certain mountains. ICON (DWD, Germany) gets the highest weight in the Alps and Europe; GEM (Canadian Meteorological Centre) is boosted across North America; GFS (NOAA) gets a home-turf bonus in the USA; and ECMWF carries the strongest global baseline weight everywhere. Example — Europe: ICON ×1.35, ECMWF ×1.25, GFS ×0.85, GEM ×0.75.

How we calculate Snow Softness %

Each day gets a softness score (higher = softer, better for skiing). We calculate hardpack risk internally, then invert it: Softness = 100 - Hardpack Risk.

Confidence % is calculated day-by-day across all 4 models, weighted by snow volume and by each model's regional skill. Days with more predicted snow have more impact on the confidence score, and disagreement from a model that is weak in that region hurts confidence less. Models agreeing on a 20cm powder day matters more than agreeing on a 0cm dry day.

0. Snow base check — If accumulated snow on the ground is <3cm, softness drops to 5% (nothing to ski on). Below 10cm base, stays around 10-25%.

1. Fresh snow × temperature — Fresh snow boosts softness, but only if it's cold enough to stay fluffy. At <-2°C you get the full softness bonus. At 0-3°C only 40% counts (wet/heavy snow). Above 6°C it melts on contact — zero softness benefit.

2. Freeze-thaw cycles — When the temperature crosses 0°C (above during day, below at night), the surface melts and refreezes into a hard crust. The wider the swing (e.g. +4° to -3°), the harder the surface.

3. Days since last snowfall × temperature — Dry days only cause hardpack if there's melt-refreeze happening. With freeze-thaw: +7% per dry day. Near 0°C but constant: +3%. Deep cold (<-3°C constant): only +1% — cold preserves snow texture, it's the thawing that compacts.

4. Snowmaking vs temperature — Snowmaking is context-dependent: on cold dry days (<0°C), cannons add snow volume and keep surfaces fresher (up to -8% hardpack). But when temps rise above 0°C, artificial snow is denser than natural and compacts into hardpack much faster (up to +18%). The worst combo is freeze-thaw + heavy snowmaking — gets an extra penalty.

5. Extreme cold — Very cold temps (<-8°C) without fresh snow create an icy surface crust, adding +10%. Very warm temps (>6°C) create slushy conditions instead of hardpack.

Reading the score: 75%+ Soft/Powder — 55-74% Good softness — 35-54% Firming up — <35% Hard/Icy

Frequently Asked Questions

Each forecast is a weighted consensus of 4 weather models: GFS (NOAA), ECMWF, ICON (DWD) and GEM (CMC). The weights are regional — ICON is strongest in the Alps, GEM is boosted in North America, and ECMWF provides the best global baseline. The 7-day window is the most reliable; accuracy decreases at longer ranges.
Global Ski Forecast covers 188 ski resorts in 38 countries on 6 continents, including Whistler Blackcomb, Zermatt, Chamonix, Niseko, Vail, St. Anton am Arlberg, Valle Nevado and Perisher.
Our server cache is refreshed every 3 hours from Open-Meteo, so every visit is served fresh data instantly. The underlying weather models themselves update every 6 to 12 hours.
Confidence measures day-by-day agreement between the 4 weather models, weighted by snow volume and by each model's regional skill. High confidence means the models predict similar snowfall on the same days; disagreement from a model that is weak in that region lowers confidence less.
Each model's vote is scaled by its documented skill in that region. In Europe, for example, the weights are ICON ×1.35, ECMWF ×1.25, GFS ×0.85 and GEM ×0.75, so forecasts in the Alps lean on ICON while North America leans on GEM.
The live ranking of the snowiest resorts for the next 7 days is at /best-powder, recalculated every 3 hours from the latest model runs.
Yes — no API key required. /api/resorts lists all 188 resorts, /api/forecast/{slug} returns the full multi-model forecast for one resort, and /api/top-powder ranks upcoming snowfall. The OpenAPI spec is at /openapi.json and plain-text docs are at /llms.txt.
Yes. A remote MCP server at https://globalskiforecast.com/mcp exposes three tools — list_resorts, get_snow_forecast and find_best_powder — and /llms.txt provides documentation written for language models.
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