About the Workshop

Recent advances in machine learning are creating new opportunities for weather forecasting and nowcasting, from short-term precipitation prediction to large-scale weather models and impact-based forecasting.

This one-day workshop will bring together researchers and practitioners from academia, research institutes, public organizations, and industry to exchange ideas, discuss recent developments, and explore opportunities for future collaboration.

The workshop will be hosted at Utrecht University and is intended to provide an informal and interdisciplinary setting for discussion between experts working on weather prediction, machine learning, hydrology, operational forecasting, and related applications.

Topics

The workshop covers topics including:

• AI and machine learning for weather forecasting and nowcasting

• Radar-based precipitation nowcasting and remote sensing

• Foundation models and physics-informed machine learning

• Probabilistic forecasting, uncertainty quantification, and downscaling

• Extreme weather, hydrological forecasting, and impact-based applications

• Operational applications and evaluation of AI forecasting systems

Registration

Registration for the workshop is now open.

Register for the Workshop

Participation is subject to venue capacity, so early registration is encouraged.

Preliminary Programme

Date and Venue

Date: 10 November 2026

Venue: Utrecht University
Utrecht, The Netherlands

Detailed information about the room, directions, and accessibility will be provided closer to the event.

Organizing Team

1. Siamak Mehrkanoon, Assistant Professor, Utrecht University

2. Ruben Imhoff, hydrometeorologist, Deltares

3. Joep Bosdijk, CTO, Weather Impact 

4. Kirien Whan, KNMI

Contact

For questions about the workshop, participation, or potential contributions, please contact:

Siamak Mehrkanoon:   s.mehrkanoon@uu.nl

Updates

The workshop program, confirmed speakers, registration information, and practical details will be added to this page in the coming weeks.