The Annual Meetings of the EMS aim at fostering exchange and cross-fertilization of ideas in the meteorological, climatological, and related communities. Facilitating interactions, integration, and engagement of science, applications, and actors is our core objective. The session programme highlights these goals and offers many opportunities for enhancing collaboration across the entire weather and climate enterprise (public, private, academic, users, and NGOs) to benefit societies in Europe and worldwide.

In addition, a particular focus of the 2025 Annual Meeting, reflecting the interests and activities of the host institutions, will be on the opportunities but also the challenges of the Growing use of Artificial Intelligence and Machine Learning.

Growing use of AI/ML in atmospheric sciences and meteorological applications

With a particular focus on flooding and warnings

The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) is reshaping our understanding of the complex interaction between the atmosphere, climate, and human society. The conference theme, Growing use of AI/ML in atmospheric sciences and meteorological applications, delves into the transformative power of AI in enhancing weather and climate prediction, environmental monitoring and meteorological research and applications.

AI/ML empowers scientists to process vast amounts of atmospheric data with unprecedented speed and accuracy. This enables the identification of intricate patterns within atmospheric systems, leading to more precise short-term and long-term forecasts and to enhanced understanding of the atmosphere.

AI/ML-driven models are revolutionizing the way we predict and respond to severe weather events, such as hurricanes, tornadoes, and floods. The urgency for further improvement of this is shown by recent high-impact events in and outside Europe and recognized by the EW4ALL Early Warnings for All initiative that has recently been started by WMO and others. By analysing real-time data from various sources, including satellites, radar, and ground sensors, AI/ML algorithms can probably help to generate timely and accurate warnings, enabling authorities to take proactive measures to protect lives and property.

Furthermore, AI/ML is unlocking valuable insights into the drivers and consequences of climate change. By analysing historical climate data and modelling future scenarios, scientists can better understand the impacts of human activities on the environment and develop strategies to mitigate climate risks.

However, we are not there yet. A huge amount of person years has to be invested to fully exploit the potentials of AI/ML in the fields that are mentioned. This will be a challenge for the years to come.