Weather Models
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Wildflyer uses data from multiple numerical weather prediction (NWP) models to provide fire weather forecasts. Different models have different strengths — high-resolution local models capture fine wind patterns and convection, while global models provide extended outlooks.
Your organisation editor can choose which models to use in Settings > Weather.
How it works
Section titled “How it works”Weather models solve the equations of atmospheric physics on a grid covering part or all of the Earth. Each grid cell produces forecasts for temperature, relative humidity, wind, precipitation, and other variables. Smaller grid cells (higher resolution) capture more local detail but require more computation and are limited to shorter forecast horizons.
For fire weather, model resolution matters most for:
- Wind — local terrain channelling, valley winds, and sea breezes are only captured by high-resolution models (< 3 km grid spacing)
- Convection — thunderstorm development and precipitation patterns require high resolution to resolve explicitly rather than being approximated
- Terrain effects — slope and aspect create microclimate variations that coarse models cannot represent
High-resolution local models
Section titled “High-resolution local models”Best for short-term forecasts (0–48 hours) — fine wind patterns, convection, precipitation detail.
| Model | Provider | Coverage | Resolution | Horizon |
|---|---|---|---|---|
| AROME | Meteo-France | France | ~1.3 km | 48h |
| HARMONIE-AROME | KNMI | Netherlands / W. Europe | ~2.5 km | Short range |
| HARMONIE-AROME | DMI | Northern Europe | ~2.5 km | Short range |
| ICON-D2 | DWD | Germany + neighbours | ~2.2 km | 48h |
These models capture local phenomena — valley winds, sea breezes, convective cells — that global models miss. For fire weather, this local wind detail can be the difference between a calm day and a dangerous one.
Regional models
Section titled “Regional models”Good for medium-range context (1–5 days) at the European scale.
| Model | Provider | Coverage | Resolution | Horizon |
|---|---|---|---|---|
| ICON Europe | DWD | Europe | ~7 km | Short/medium range |
| ARPEGE | Meteo-France | Europe / France | ~10 km (EU) | Medium range |
| ECMWF HRES (IFS) | ECMWF | Europe | ~9 km | Medium range |
Global models
Section titled “Global models”Useful for extended outlook (5–10+ days) and for comparing multiple independent forecasts.
| Model | Provider | Resolution | Horizon |
|---|---|---|---|
| ECMWF IFS 0.25° | ECMWF | ~25 km | Medium range |
| ECMWF AIFS 0.25° | ECMWF (AI-enhanced) | ~25 km | Medium range |
| GFS | NOAA (USA) | 13–25 km | Up to 16 days |
| GEM Global | Canada | ~25 km | Medium range |
| ICON Global | DWD | ~13 km | Medium range |
| UKMO Global | Met Office (UK) | ~10 km | Medium range |
| JMA GSM | Japan | ~20 km | Medium range |
| CMA GRAPES | China | ~25 km | Medium range |
Reanalysis
Section titled “Reanalysis”For historical analysis and post-event review.
| Model | Provider | Resolution | Period |
|---|---|---|---|
| ERA5 | ECMWF | ~25 km (hourly) | 1979–present |
ERA5 provides consistent historical data for climatological comparisons and post-event analysis. The EFFIS percentile-based FWI calibration (Vitolo et al., 2020) is built on ERA5 data.
How to read it in Wildflyer
Section titled “How to read it in Wildflyer”When choosing models:
- Day-to-day assessment — use the highest-resolution local model available for your region (AROME for France, ICON-D2 for Germany, HARMONIE for the Netherlands)
- 2–5 day planning — compare the regional model with ECMWF HRES
- Extended outlook — look at multiple global models. When they agree, confidence is higher; when they diverge, uncertainty is high
- Post-event analysis — use ERA5 reanalysis for a consistent, quality-controlled view of what conditions were
Sources
Section titled “Sources”- ECMWF (2023). IFS Documentation. European Centre for Medium-Range Weather Forecasts.
- Vitolo, C., Di Giuseppe, F., Krzeminski, B., & San-Miguel-Ayanz, J. (2020). ERA5-based global meteorological wildfire danger maps. Scientific Data, 7: 216.