Daily heating and cooling degree days, weather anomalies and ready-made demand features by store, postcode or region, for retailers, utilities and gas traders.
Data product · Listing updated 11 Nov 2026
+1
Japan
Austria, Belgium, France, Germany, Italy, Netherlands, Poland, Spain, Sweden, Switzerland, United Kingdom
Canada, United States
Australia
Daily by 05:00 UTC
Indices for 15 countries plus features for any site
Small and medium businesses, Mid-market, Enterprises
Alpenglow weather history and forecasts; 1 km population grid for weighting
Daily computation from blended observations and forecasts against 1991–2020 normals
The weather inputs demand-forecasting models actually need, pre-computed for your locations so analysts do not have to engineer them from raw weather.
Features are computed every day from Alpenglow's blended observations and forecasts. Population weighting uses a 1 km population grid, and site-level features use your coordinates and opening hours. Normals follow the 1991–2020 reference period. Forecast values keep their issue date, so models can be trained on what was known at the time rather than on later observations. The 15 national indices cover the United Kingdom, Germany, France, Italy, Spain, the Netherlands, Belgium, Austria, Switzerland, Poland, Sweden, the United States, Canada, Japan and Australia; site-level features are available anywhere.
12 fields as documented by Alpenglow Weather Systems.
| Field | Type | Description | Example |
|---|---|---|---|
| location_id | string | Your site ID or an Alpenglow region code | — |
| location_type | string | store, postcode or region | — |
| date | date | Calendar date, local time | — |
| hdd | number | Heating degree days (default base 15.5 °C) | — |
| cdd | number | Cooling degree days (default base 22 °C) | — |
| temp_anomaly_c | number | Mean temperature versus 1991–2020 normal | — |
| precip_anomaly_mm | number | Precipitation versus normal | — |
| sunshine_hours | number | Hours of sunshine | — |
| rain_hours_trading | integer | Hours with rain during opening hours | — |
| is_forecast | boolean | True for forecast values | — |
| forecast_issued | datetime | Issue time for forecast values | — |
| population_weighted | boolean | True for population-weighted regional indices | — |
Real rows from the provider's sample file. Personal data is masked before anything is shown.
| location_idstring | location_typestring | datestring | hddstring | cddstring | temp_anomaly_cstring | rain_hours_tradingstring | is_forecaststring |
|---|---|---|---|---|---|---|---|
| store_DEMO_014 | store | 2026-11-12 | 9.8 | 0.0 | -1.6 | 3 | true |
| store_DEMO_027 | store | 2026-11-12 | 7.2 | 0.0 | 0.8 | 0 | true |
| GB_POPW | region | 2026-11-10 | 8.1 | 0.0 | 0.4 | null | false |
Prices in USD, as published by Alpenglow Weather Systems
up to 500 sites
national indices for 15 countries plus 5,000 sites
Converted amounts use DataStoa display rates and are approximate. Alpenglow Weather Systems quotes the final price, taxes and terms.
Request detailed pricing15 countries across 4 regions.
Time coverage: 1991 to present.
Provider-reported
Latency: Daily by 05:00 UTC
DataStoa has not audited these figures. Ask the provider how they are measured, or test them on a sample.
The licence is agreed directly between you and Alpenglow Weather Systems. This summary is provider-reported; the linked terms prevail.
Not listed: use in commercial products, AI / ML model training, AI retrieval and grounding, redistribution allowed, marketing and outreach, research use. Ask Alpenglow Weather Systems if you need any of these.
Weather, climate risk and air quality data for any location on Earth
Alpenglow Weather Systems runs a global weather API, physical climate risk models and a European air-quality service from Zurich. Its forecasts blend national weather service models with machine-learning post-processing, and every historical series is backed by station observations and reanalysis.
Also available on: Snowflake Marketplace, AWS Data Exchange, Databricks Marketplace, Microsoft Azure
What buyers say about Alpenglow Weather Systems. Reviews cover the provider, not only this listing.
from 3 reviews
Adding degree days and the weather-driven demand features to our model noticeably reduced forecast error for heat-sensitive products. Setup took a day since site coordinates were all they needed.
Lukas W., Demand planning manager, Beverage distributor
Each hazard score came with the underlying model resolution, the scenario and the time horizon, which let our actuaries decide where to rely on it. Subsidence scores are less granular than flood. Their scientists joined two calls to explain the method.
Isabel T., Catastrophe modeller, Specialty insurer
We started on the Starter tier and moved to Business when we launched. Response times are consistently fast, the history endpoint uses the same schema as forecasts, and the docs have examples in three languages.
Noah F., CTO, Agritech start-up