Veranstaltungen

As the national meteorological and climatological service MeteoSwiss has the mandate to provide weather and climate services for the safety and benefit of Switzerland. Being one of the founding members of ECMWF in 1975, Switzerland benefits from global medium-range weather forecasts and climate reanalysis since the beginnings of ECMWF. However, the country’s complex orography and local weather phenomena in the Alps impose a special challenge in providing accurate and timely local weather and climate information across scales.
In this talk I will present examples of seamless weather and climate services tackling the challenges imposed by complex orography. An emphasis will be laid on products arising from European collaboration in particular with ECMWF and EUMETSAT, but also examples of in-house developments of climate datasets. Some of the latest developments concern seamless workflows for weather and climate monitoring, wind climatology and the provision of a national drought platform as well as satellite applications. 

(1) Improving ICON-DREAM Cloud Information through AI-driven Data Assimilation (2) Extreme precipitation climate change factors over Germany determined from the NUKLEUS regional climate model ensemble (3) Partial Analysis Increment of Swabian Moses observations duringSummer 2023 (4) tbd
 

(1) Online bias correction in data-driven weather forecast models (2) European Hail Risk under Climate Change (3) Improved representation of surface layer processes in numerical models (4) tbd

(1) Testing a new hybrid storyline-pseudo global warming (PGW) approach to estimate the extent of the Ahr Flood event in a warmer climate with ICON-CLM (2) KITsonde observations during ASCCI (3) tbd (4) Benchmarking Deep Learning Architectures for Climate Data Downscaling

Starting in 2020, a new modeling initiative has been launched as a joint project between climate modeling institutes and the Deutscher Wetterdienst. The initiative integrates NWP, climate predictions, climate projections, and atmospheric composition modeling based on the ICON framework and targets a unified treatment of the respective subgrid-scale parameterizations. It aims at the development of coupled model configurations of ICON to conduct operational weather and ocean forecasts for several days, climate predictions with timescales up to 10 years ahead as well as climate projections, and further provides a model baseline for joint research for NWP and climate (Müller et al., 2025).
ICON XPP is an outcome of this initiative and provides the baseline for the next generation of climate predictions and projections, and global climate research (where XPP stands for eXtended Predictions and Projections). ICON XPP comprises the atmospheric component as used for the numerical weather prediction (ICON NWP), the ICON ocean model, the land surface component ICON Land, and a data assimilation system, all adjusted to an Earth System model for pursuing climate research and operational climate forecasting.
Here, I give a survey, from the strategic decision made for unifying ICON for NWP and climate, to a first assessment of the global climate in baseline experiments of ICON XPP, to future directions of model developments such as the upcoming national contribution to Coupled Model Intercomparison Project (CMIP7) and an effort for an ultra-high-resolution workhorse configuration (13km atm; 5km oce).
 
Müller, W. A., and Coauthors, 2025: ICON: Towards vertically integrated model configurations for numerical weather prediction, climate predictions and projections. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-24-0042.1, in press.

(1) Significance and Robustness of Climate Change Signals for Extreme Indices over Germany in a Convection-permitting Climate Model Ensemble (2) tbd (3) Extreme fire weather in a changing climate: Projections for Europe based on bias adjusted EURO-CORDEX simulations (4) Near-real-time probabilistic Hail Detection based on polarimetric radar quantities and environmental conditions using machine learning methods

tbd

(1) tbd (2) tbd (3) tbd (4)  Evaluation of AI Weather Models for Sub-seasonal Forecasts