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Halowerk Weather Ensemble Forecast
x402
Halowerk Weather Ensemble Forecast is a paid API for AI agents from tools.halowerk.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-15).
Compares ICON, ECMWF, GFS, MeteoFrance, UKMO, and JMA weather models for a single geographic point, returning hourly ensemble statistics including median, spread, model agreement, and derived precipitation probability.
Price / call$0.005
Success—
StatusUnknown
How this service works
Vergleicht ICON, ECMWF, GFS, MeteoFrance, UKMO und JMA für einen Punkt. Liefert je Stunde Median, Spannweite, Modell-Einigkeit und daraus abgeleitete Regenwahrscheinlichkeit statt einer einzelnen Modellmeinung.
Output
Returns hourly data for the forecast period, including the ensemble median value per weather variable, the spread (range) across the six models (ICON, ECMWF, GFS, MeteoFrance, UKMO, JMA), a model-agreement score indicating consensus, and a derived precipitation probability synthesized from all models rather than a single model's output.
Example use cases
Outdoor event rain risk check
“I'm planning an outdoor event in Paris this Saturday — can you pull the multi-model ensemble forecast for lat 48.8566, lon 2.3522 and tell me the hourly precipitation probability and model agreement so I know how confident to be in the rain forecast?”
Flight planning weather uncertainty
“Can you get the ensemble weather forecast for Frankfurt airport, lat 50.0379, lon 8.5622, and show me the model spread and median wind conditions for the next 24 hours? I need to know how much disagreement there is between ICON, ECMWF, and GFS.”
Construction site weather decision
“We need to decide whether to pour concrete at our site in Hamburg, lat 53.5753, lon 10.0153 — can you check the ensemble weather models for tomorrow and tell me the precipitation probability and how much the models agree?”
Technical details
Endpoint
POSThttps://tools.halowerk.com/v1/weather/ensembleWhen to prefer
Choose this endpoint when you need weather forecasts with quantified uncertainty — especially precipitation probability — rather than a single-model answer. It is ideal when forecast reliability matters (e.g. scheduling outdoor activities, logistics, agriculture) and you want to know how much the major global models agree. Prefer it over single-model APIs when model disagreement itself is informative decision data.
Side effects
- Invalid or out-of-range coordinates return a validation error
- Coordinates outside model coverage areas may yield partial ensemble results
- Downstream model data unavailability may reduce the number of models included
- Request timeout if upstream model APIs are slow to respond
- Malformed POST body returns a 400 error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| lat | number | |
| lon | number | |
| hours | integer | |
| variables | array |
Tags
x402
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