EMS Annual Meeting 2025

A thematic focus of the 2025 Annual Meeting, reflecting the interests and activities of the host institutions in Slovenia, will delve into the transformative power of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing weather and climate prediction, environmental monitoring and meteorological research and applications. With a particular focus on flooding and warnings, we will be looking at the challenges and opportunities of this rapid evolution and how it is reshaping our understanding of the complex interaction between the atmosphere, climate, and human society.

The Annual Meetings of the EMS aim at fostering cross-fertilisation of ideas, feedback between science and applications, and the involvement of all the diverse actors in the fields of weather, climate, water and the environment. The session programme will offer many opportunities for collaboration across the entire weather and climate enterprise (public, private, academic, users, and NGOs) to benefit societies in Europe and worldwide.

The following sessions are organized by/of interest for people involved in ESMO:

Climate predictions on timescales of several weeks to months to years are becoming increasingly important for society, particularly in the context of adaptation to climate change. Advancing the quality of these forecasts requires further research on the physical processes acting on these different timescales and on how well prediction models capture these processes, as well as on methods extracting the most skilful information from these model forecasts. While contributions to both topics are welcome, the session will particularly focus on the latter aspect. More specifically, we invite contributions on:
i. advancing the climate forecasts with new initialization and ensemble strategies as well as improved model physics of the earth climate system,
ii. post-processing raw model output (e.g., bias correction, (re)calibration, or downscaling with classic or machine-learning-based statistical methods),
iii. translating physical knowledge on local and remote physical drivers of predictability into tools to detect and indicate “windows of forecast opportunity” (e.g., subsampling or weighting of ensemble members or models),
iv. coupling raw model forecasts to impact models to support early warning systems and adaptation strategies (related to extreme events and hazards in the atmosphere, biosphere, and lithosphere, to health, or to energy).