Toward seamless weather forecasts, MeteoSwiss’ first steps.

MeteoSwiss is developing a unified, probabilistic, gridded forecast system designed to deliver added value for specific user groups, such as hydrological modelers. Today, these users often need to run their models for each numerical weather prediction source separately. Following the example of forecasts in the national weather app—which already delivers daily 7-day forecasts for ~6,000 locations by integrating ICON (1 and 2 km), IFS, and INCA—the new system aims to simplify this by combining all relevant information into a seamless, high-quality forecast. This enables more efficient and consistent downstream applications, improving usability, accessibility, and decision-making for both internal and external users.

A key feature is the Seamless Rapid Update Cycle (S-RUC), a data-driven model architecture that extends forecasts up to 10 days and updates short-range guidance every 10 minutes, leveraging the latest observations such as radar and satellite data to maximize predictive power.

MeteoSwiss is also developing an operational MLOps platform to standardize and accelerate the use of machine learning. This infrastructure will support model development, training, validation, and deployment, ensuring scalability and maintainability of future AI applications.

The initial focus is on temperature, precipitation, wind, and cloud cover, with an emphasis on high-impact weather and hydrological relevance for Swiss territory.

As a result, the project will lead to the consolidation of existing nowcasting and postprocessing systems. This will reduce duplicated effort, streamline visualization workflows, and lower maintenance costs and dependencies through shared tools and common data formats.

This presentation outlines MeteoSwiss’ early steps toward seamless, machine-learning-enabled forecasting and highlights the methodological and operational innovations under development.