Compare long-term winter snow depth, temperature stability and upper-slope reliability across the Top 500 qualifying…
The ranking combines average lower- and upper-mountain snow depth, upper-slope variability, the share of high-snow days, winter temperatures, resort altitude and an outlier penalty. Cluster and minimum-snow filters are applied locally to one cached ranking dataset.
| Rank | Ski resort | Altitude · m | Snow reliability | Temperature · °C | Cluster | History | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Base | Top | Lower | Upper | SD* | >150 cm | Low | High | ||||
The score uses winter observations from week 52 through week 10. It rewards persistent snow depth on lower and upper slopes, colder conditions and resort altitude, while penalising upper-slope variability, exceptional outliers and temperature variability.
A resort needs more than 100 qualifying historical observations, more than 34 km of pistes, a representative resort altitude above 450 m and meaningful interannual upper-snow variation to enter the ranking.
All filters use one canonical Top-500 dataset. The costly decade-long aggregation is preferably rebuilt during prewarming and stored in a materialized ranking table plus an atomic JSON cache.
If the cache expires, a visitor can refresh it from the materialized ranking. If that is also stale, the canonical historical query remains available as a locked fallback, so prewarming improves latency but is not required for correctness.