Meteogram MultiModel Ensemble
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The MultiModel Ensemble meteogram is available on the meteoblue website for either 7- or 14-day forecasts. It compares predictions from multiple high-resolution models with those from a traditional ensemble to show uncertainty more clearly.
Why it matters
Traditional ensembles (e.g. GFS) often underestimate forecast uncertainty in the first 3–5 days, giving a false sense of confidence.
They run at lower resolution, missing some local weather patterns visible in high-resolution models.
All members of a traditional ensemble have the same likelihood of being correct — there is no early way to know which will perform best.
High-resolution models vary in accuracy depending on location and weather conditions.
How to read it
Temperature graph:
Yellow lines = high-resolution models
Green lines = GFS ensemble members (same model run multiple times with varied initial conditions)
Black line = average of all forecasts
Dashed line = meteoblue consensus forecast
Precipitation: Accumulated totals from today onwards; blue bars show hourly amounts.
Cloud cover: Light blue = high-resolution models, green = GFS ensemble members, in %.
Wind forecast: Light blue = high-resolution models, green = ensemble members. A wind rose shows daily wind direction probability — large segments mean higher likelihood. Many evenly sized segments indicate uncertainty; two opposite segments often signal thermal wind circulation (different daytime vs night-time winds).
Behind the data
meteoblue high-resolution models cover most populated areas (3–10 km grid) and the whole world at moderate resolution (30 km).
Forecasts combine multiple weather models, statistical analysis, measurements, radar, and satellite telemetry for the most probable outlook.
Weather models simulate atmospheric physics in grid-cells 4–40 km wide, 100 m–2 km high, with 60 vertical layers reaching into the stratosphere (10–25 hPa / up to 60 km altitude).The MultiModel Ensemble meteogram is available on the meteoblue website for either 7- or 14-day forecasts. It compares predictions from multiple high-resolution models with those from a traditional ensemble to show uncertainty more clearly.
Why it matters
• Traditional ensembles (e.g. GFS) often underestimate forecast uncertainty in the first 3–5 days, giving a false sense of confidence.
• They run at lower resolution, missing some local weather patterns visible in high-resolution models.
• All members of a traditional ensemble have the same likelihood of being correct — there is no early way to know which will perform best.
• High-resolution models vary in accuracy depending on location and weather conditions.
How to read it
• Temperature graph:
o Yellow lines = high-resolution models
o Green lines = GFS ensemble members (same model run multiple times with varied initial conditions)
o Black line = average of all forecasts
o Dashed line = meteoblue consensus forecast
• Precipitation: Accumulated totals from today onwards; blue bars show hourly amounts.
• Cloud cover: Light blue = high-resolution models, green = GFS ensemble members, in %.
• Wind forecast: Light blue = high-resolution models, green = ensemble members. A wind rose shows daily wind direction probability — large segments mean higher likelihood. Many evenly sized segments indicate uncertainty; two opposite segments often signal thermal wind circulation (different daytime vs night-time winds).
Behind the data
• meteoblue high-resolution models cover most populated areas (3–10 km grid) and the whole world at moderate resolution (30 km).
• Forecasts combine multiple weather models, statistical analysis, measurements, radar, and satellite telemetry for the most probable outlook.
• Weather models simulate atmospheric physics in grid-cells 4–40 km wide, 100 m–2 km high, with 60 vertical layers reaching into the stratosphere (10–25 hPa / up to 60 km altitude). -
Thank you, Martin!
