An advanced system known as “data4web” has been producing such localised forecasting data for several years, and is responsible for the forecasts that are displayed on the MeteoSwiss app and website. The graphics and numeric values are generated by seamlessly combining all available numeric models along with the best post-processing data sources (see the PostprocVeri project) and the Nowcasting system.
In order to convert model grids with varying resolutions into a detailed localised forecast, data4web uses various sophisticated interpolation and correction techniques that enable local effects to be taken into account. Furthermore, data4web succinctly summarises the complex probability information for ensemble models by calculating statistics such as quantiles.
The data4web data are updated every 10 minutes. Every day, data4web produces a total of around 1 billion data sets for 6,000 locations in Switzerland.
Continuous time series
The first challenge faced by data4web is to produce weather data from the start of the day in question for up to 10 days into the future without interruption, for each of the points for which a forecast is available. In order to cover the entire range of parameters as accurately as possible, data4web combines the highest-performing forecasting systems available to it.
The sources used in the production chain are as follows:
- From the start of the day in question for up to 6 hours into the future: INCA, the nowcasting system that provides immediate forecasts
- For up to 5 days into the future: the high-resolution probability models COSMO-1E and COSMO-2E
- Then for up to 10 days into the future: the global ensemble model IFS-ENS from the ECMWF
For certain parameters where data from statistical post-processing are available and create added value, such as for wind and temperature, data4web uses these to improve the quality of the forecasts. Data4web thus draws on the following complementary data:
- MOSMIX, from the German meteorological forecasting service, used for adjusting the temperature data for the models close to measurement stations
- Data from the internal PostprocVeri project, which replaces the raw data from COSMO and the ECMWF for wind variables. Other parameters from statistical post-processing are introduced little by little, such as cloud cover, precipitation and temperature
The role of data4web is therefore to combine these different sources optimally to ensure that they are coherent, and, in certain cases, to improve the quality of the data in a fully automated manner.
Local point data
Even the high-resolution models do not allow for an exact reproduction of the subtleties of spatial structures established as a result of the complex topography of the Alps. In order to forecast local weather conditions as accurately and as detailed as possible, the results obtained from models with a range of resolutions (between 1 km and 18 km) need to be corrected and harmonised. The use of interpolation and downscaling techniques makes it possible to transform a parameter calculated at the resolution of the model grid to a more appropriate resolution for the user.
The aim of data4web is to provide local forecasts that reflect the weather conditions as perceived by a user in a precise location.
To do this, data4web deploys several approaches, for example by integrating very high-resolution (50 m) topographic indices, but also by spatially incorporating certain parameters such as cloud cover (since the user can only see the sky as it appears right above their head).