Can AI outperform traditional physics-based models in forecasting complex atmospheric variables? This web-tool allows for a performance comparison of three weather forecast models: Google Deepmind's GraphCast (AI-based), ECMWF Integrated Forecasting System (IFS) (physics-based), and ECMWF AIFS (AI-based). The comparison is based on verification results between each forecasting model against the ERA5 reanalysis dataset. Verification metrics include: RMSE (root mean square error), MAE (mean absolute error), MBE (mean bias error), and R (correlation coefficient). The evaluation covers the first 10 days of each month in 2024, with a focus on the performance of the forecast models across the lead times (10 days in 6 hour increments). The following weather variables have been assessed for their accuracy across the selected regions and lead times:
- 2-meter temperature T2M
- Mean sea level pressure MSL
- 10-meter u-component of wind U10
- 10-meter v-component of wind V10
- Specific humidity at 1000 hPa pressure Q
The main objective of this comparative analysis is to evaluate whether the forecasting models perform differently in different geographic regions. Four geographic regions are examined:
- Global
- Tropics 23.5°S – 23.5°N
- Subtropics 23.5° – 35° N/S
- Northern Temperate 35°N – 60°N
- Southern Temperate 35°S – 60°S
- Polar >60°N and >60°S
- Africa 37°N – 35°S, 17°W – 51°E