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Signals

WRSI Signals describe prevailing weather regimes and their persistence.

Typical use cases​

They are useful for:

  • Establishing baseline demand assumptions
  • Framing seasonal narratives
  • Distinguishing structural context from short-term noise

Signals are most informative when interpreted alongside short-term forecasts.

Accessing WRSI Signals​

from snowtrail import Snowtrail
client = Snowtrail(api_key="your-api-key")

# Fetch WRSI forecast stress signal
df = client.wrsi.forecast_stress(date_from="2024-01-01")

# All methods return pandas DataFrames
print(df.head())

For a conceptual overview, see Data Model → Signals.