The ECMWF Ensemble and SEAS5 Forecast Systems
ECMWF's weather forecasting modules, namely the ECMWF Ensemble (ENS Extended) and SEAS5, provide detailed and accurate predictions for both sub-seasonal and seasonal periods. They harness the power of ensemble forecasting to ensure a more comprehensive and robust weather prediction.
The European Centre for Medium-Range Weather Forecasts (ECMWF) is a premier meteorological institution renowned for its accuracy in weather prediction.
⚙️ Model Details
- ECMWF Ensemble (ENS Extended)
- ECMWF SEAS5
- Further Reading
- Model Type:Ensemble (51 members (101 coming soon!))
- Forecast Duration: 46 days
- Timestep: 6 hours
- Resolution: 0.4 degree (36 km)
- Update Frequency: Twice a week (Mondays and Thursdays, daily coming soon!)
- Geographic Coverage: Switzerland and Eastern Ontario
- Products: [Optimality Only] We currently do not offer raw data from ECMWF
- Model Type:Ensemble (25 members)
- Forecast Duration: 210 days
- Timestep: 6 hours
- Resolution: 0.4 degrees (36km)
- Variables: 15 different variables
- Geographic Coverage: Switzerland and Eastern Ontario
- Products: [Optimality Only] We currently do not offer raw data from ECMWF
🔍 More About ECMWF's Forecast Systems
ECMWF Ensemble (ENS Extended)
The ECMWF Ensemble Extended (ENS) is a sub-seasonal forecast system that offers detailed weather predictions for up to 46 days. It leverages the power of ensemble forecasting, incorporating 51 different model runs to ensure robustness in predictions. Being a sub-seasonal, it is more important to have numerous model runs instead of high resolution, and, therefore, this model is better suited to forecasts at extended ranges. See NOAA's GFS Model for better short term models.
ECMWF SEAS5
SEAS5 is ECMWF's seasonal forecast system. It gives a long-range view of the weather, forecasting up to 7 months into the future. This system relies on 25 ensemble members and considers a wide array of variables to present a comprehensive weather outlook. It is considered one of the world's premier models for forecasting seasonal weather.
Why ECMWF?
ECMWF's forecasting systems are globally recognized for their accuracy. By combining various models in an ensemble setup, they ensure a more comprehensive look at possible weather scenarios, enhancing the reliability of the forecast.
📝 cknowledgments and Legal Information
Data Source
The data provided through this API is based on the forecasts and models of the European Centre for Medium-Range Weather Forecasts (ECMWF).
License
ECMWF's data policies and licensing requirements can vary. Users should consult the ECMWF's Data Policies for specifics.
Disclaimer: ECMWF strives for accuracy, but like all forecast models, there's inherent uncertainty. Users should consult multiple sources and use the forecast data judiciously.
Modifications
Where applicable, if the data has been modified, those modifications will be indicated in this section.