Using Different Wind Forecast Models
- Disaster Science
- Bushfire.io pro
- July 29, 2025
Table of Contents
Bushfire.io Pro users can access multiple wind forecast models, including the Australian Digital Forecast Database (ADFD) from the Bureau of Meteorology (BOM). ADFD includes local terrain and weather observations in its forecasts, which makes it especially reliable for Australian conditions.
Info
Requires a subscription to Bushfire.io Pro, Business or Enterprise.
Free vs. Pro Model Support
Free Users:
- GFS 1°: a global model provided by the National Oceanic and Atmospheric Administration (NOAA), updated 4 times per day.
Pro Users:
- GFS 0.25° (Global): a higher-resolution version of GFS for more precise forecasts.
- HRRR (USA): a high-resolution model optimised for short-term and localised weather events in the United States.
- NAM (USA): a regional North American model that balances resolution and forecast duration.
- ADFD (Australia): a detailed Australian model used in BOM’s own MetEye forecasts and official bulletins.
- ECMWF IFS, AIFS and AIFS Ensemble (Global): highly detailed global models.
ADFD often outperforms GFS in Australia thanks to its finer resolution and integration of local features such as terrain, coastlines, and regional weather patterns. GFS, however, remains useful for its global reach and frequent updates, especially in areas lacking high-resolution regional models. Among global models, ECMWF’s IFS is typically more accurate than GFS. The newer ECMWF AIFS (Single) improves on IFS. The AIFS Ensemble is currently considered state of the art, with leading performance across a range of forecast parameters.
Wind Model Comparison
The table below lists our wind models, with their authors, coverage, resolution and update frequency.
| Model | Availability | Authors | Location / Domain | Approx. Resolution | Forecasts |
|---|---|---|---|---|---|
| Global Forecast System (GFS) 1° | Free | National Oceanic and Atmospheric Administration (NOAA) | Global | ~111 km (1 degree) | 4x daily |
| Global Forecast System (GFS) 0.25° | Pro | National Oceanic and Atmospheric Administration (NOAA) | Global | ~30 km | 4x daily |
| High-Resolution Rapid Refresh (HRRR) | Pro | National Oceanic and Atmospheric Administration (NOAA) | Continental US | ~3 km | hourly |
| North American Mesoscale (NAM) | Pro | National Oceanic and Atmospheric Administration (NOAA) | North America | ~12 km | 4x daily |
| Australian Digital Forecast Database (ADFD) | Pro | Bureau of Meteorology (BOM) | Australia | ~6 km (0.06 degrees) | 2x daily |
| Integrated Forecasting System (IFS) | Pro | European Centre for Medium-Range Weather Forecasts (ECMWF) | Global | ~9 km | 4x daily |
| Artificial Intelligence Forecasting System (AIFS Single) | Pro | European Centre for Medium-Range Weather Forecasts (ECMWF) | Global | ~25 km | 4x daily |
| Artificial Intelligence Forecasting System Ensemble (AIFS Ens) | Pro | European Centre for Medium-Range Weather Forecasts (ECMWF) | Global | ~31 km | 4x daily |
Why Offer Multiple Models?
Note
Some models have finer resolution but cover smaller areas or shorter forecast periods. A model with frequent updates can be less detailed, but it can capture rapidly changing conditions better.
Different models work best in different conditions. High-resolution models like HRRR or ADFD can capture local effects more accurately. More frequent updates from a global model can better reflect changes on the ground, especially where local data is sparse. If they are available, compare forecasts with ground-truth wind barbs or live observations to get the clearest picture of current conditions.
By using a combination of global coverage (GFS / ECMWF) and regional insights (ADFD, HRRR, NAM), you have the best possible data wherever you are. Whether you’re monitoring a broad region days ahead or need precise forecasts over a shorter window, you can choose the model that best fits your scenario.
In July 2025, we added the suite of ECMWF forecasts. Of these, the ECMWF AIFS Ensemble outperforms existing forecasts by up to 20% on some parameters. Learn more about AIFS Ensemble.
