meteoblue Forecast Accuracy Report

meteoblue continuously monitors forecast quality through rigorous verification, Comparing our forcast to data from over 100,000 global weather stations to ensure highest forecast accuracy. Our routine verification is the most comprehensive global study available. We choose proven data over expensive marketing campaigns.

Ground Truth Verification

Actual weather stations serve as our ground truth. Our independent baseline spans all national weather services. We also include over 100,000 partner network stations. Strict quality control filters out unreliable meteorological measurements. Only 60% of stations meet our standards. This rigorous screening ensures a reliable ground truth dataset.

Our benchmarking study evaluates every single continent. We verify meteoblue forecasts against major global models:

• IFS (ECMWF)
• AIFS (ECMWF)
• GFS (NOAA)
• NEMSGLOBAL (NOAA/meteoblue)
• ICON (DWD)
• ARPEGE (Météo-France)
• UKMO (UK Met Office)
• GEM (Environment Canada)

Precision Hourly Methodology

We compute all statistical scores at an hourly resolution. Predicting a precise temperature at 15:00 UTC is difficult. You must get the exact timing right. Daily maximums are much easier to predict. As a Swiss company, precision timing is our standard.

We calculate these daily scores using hourly data:

Mean absolute Error (MAE) tracks the average error magnitude.

\(MAE=\frac{1}{n}\sum _{i=1}^{n}|\^{y}_{i}-y_{i}|\)

Root mean square Error (RMSE) similar to MAE but emphasizes larger forecasting errors.

\(RMSE=\sqrt{\frac{1}{n}\sum _{i=1}^{n}(\^{y}_{i}-y_{i})^{2}}\)

• Squaring mechanism: Magnifies large discrepancies.
• Same units: Matches the original data units.
• Variance indicator: Highly sensitive to occasional outliers

Bias or Mean Error (ME) reveals systematic directional errors

\(ME=\frac{1}{n}\sum _{i=1}^{n}(\^{y}_{i}-y_{i})\)

• \(\^{y}_{i}\): Predicted forecast value.
• \(y_{i}\): Actual observed value.
• **\(n\): Total data points.

• Positive value: Systematic overestimation.
• Negative value: Systematic underestimation.
• Cancellation effect: Positive and negative errors offset.

Global Station Distribution

Global weather station density is not uniform.

• Europe & USA: Over 20,000 verified stations each.
• Africa: Approximately 1,000 verified stations total.

Consequently, verification data is much more robust in high-density regions. We share these infrastructure differences openly to provide the most transparent accuracy assessments possible.

Stations per Continent

Regional Forecast Accuracy – Temperature

Forecast accuracy varies by location. The map provides a localized performance overview.

This report aggregates data at the continental level. It evaluates every individual weather station. We cannot list every country or smaller administrative zone here.

Please contact us for specific regional insights. We can access verification data for any individual station worldwide in real time.

Performance Overview

Forecast Day Analysis – Temperature

Forecasting error generally increases with lead time. The further into the future, the larger the expected error. Despite this, meteoblue mLM remains highly competitive: on day 4 it is still as accurate as the best raw model on day 1.

The general ranking of models over time stays quite stable, so for the remainder of this report we use forecast day 1 as the basis for all comparisons.

Global Summary – Temperature

Understanding Forecast Variability

Monthly summaries can create the impression that one weather model is always superior. In reality, a model that performs perfectly today may fluctuate tomorrow as weather situations change. These graphs illustrate how forecast errors shift over time. Because these figures represent continental averages, variability at individual station levels is much higher.

Managing Risk and Uncertainty

Weather forecasting is highly complex. The meteoblue Learning MultiModel (mLM) achieves the highest overall accuracy in these summaries by leading in most locations, yet no single model wins every time.

This variability is exactly why we provide dozens to hundreds of alternative forecasts from other institutions alongside our own best estimate. Having access to all available forecasts ensures you are covered even when a specific model underperforms. Depending on your business needs and technical expertise, evaluating this uncertainty is often critical for managing operational risk. Our experts are available to help you build a specially tailored solution.

Europe – Temperature

Daily verification scores and overall model ranking

Asia – Temperature

Daily verification scores and overall model ranking

Africa – Temperature

Daily verification scores and overall model ranking

North America – Temperature

Daily verification scores and overall model ranking

South America – Temperature

Daily verification scores and overall model ranking

Australia – Temperature

Daily verification scores and overall model ranking