Statistical post-processing: improved forecasting
To improve forecasts, statistical methods are used to correct systematic errors in the models. One example of this is the ‘MOSMIX’ system of the German Weather Service (DWD), which is also used by MeteoSwiss. MOS stands for ‘Model Output Statistics’ and uses model predictions as its starting values.
The model forecasts are compared with actual measurements over a long training period. The correction process works similarly to an archer improving their technique. An experienced archer is already quite accurate under ideal conditions but must adjust their shots in real-world scenarios, such as to account for wind and lighting conditions. In the same way, MOSMIX adjusts the raw model forecasts by eliminating systematic deviations.
In addition to temperature forecasts, other parameters such as wind, cloud cover and ground conditions are included in the statistical equations. This allows systematic errors to be largely corrected, resulting in more accurate temperature predictions.
Interpolation and extrapolation: forecasts for every location
Since optimised forecasts are only available for certain locations, they must be temporally and spatially interpolated for other places. This is where model systems come in again to create continuous temperature trends. When extrapolating station values to the wider area, model grids and real landscape features, such as height above sea level or slope gradients, are taken into account.
This allows statistically corrected forecast values to be generated for every municipality in Switzerland. Thanks to these interpolation and extrapolation steps, we can achieve more reliable forecasts, even for places without weather stations.
Modern technology: forecasts updated hourly
MeteoSwiss calculates new forecasts every hour, integrating the latest measurements and prediction data from incoming weather models. The DWD’s MOSMIX and ECMWF forecasts are updated twice daily. In addition, the ICON-CH1-EPS model runs eight times a day, and the ICON-CH2-EPS model runs four times a day to ensure continually up-to-date forecasts.
Graphical preparation and distribution
In the final step, this data is immediately updated and displayed in visual form on the MeteoSwiss website and app (Local Forecast / My Locations). In the case of temperature, uncertainties are subtly displayed in the background of the trend graphs, helping to provide a clearer understanding of forecast accuracy.
Conclusion
The process of creating weather forecasts is far more complex nowadays than in the past. Thanks to modern weather models, statistical post-processing and continuous updating, precise and location-specific forecasts can be produced, enabling people to better plan their daily lives. Despite the complexity behind the scenes, these steps ensure that weather information remains easily accessible and understandable for everyone.