The International Training Course on AI Empowered Early Warnings for All kicked off in Shanghai, a practical site for the demonstration projects of the World Meteorological Organization's multi-hazard warning systems, on Monday, with 22 participants from 21 countries and regions, including Jordan, Chad and Mauritius.

The five-day training program adopts a diverse approach combining technology sharing, case studies, and hands-on experiences. Experts from the WMO, the China Meteorological Administration, universities, and financial institutions shared their expertise in the application of AI in universal early warning, the development of urban multi-hazard early warning toolbox, and the intersection of green finance and disaster risk management. Workshops on practical exercises with the urban multi-hazard early warning toolbox are also part of the curriculum.

"This training program is timely and crucial. With the backdrop of frequent extreme weather and climate events, the need for a people-centered universal early warning system becomes urgent. Global cooperation is essential to enhance warning accuracy through AI technology," said Cyrille Honoré, deputy director of the WMO Services Department.

Ra'ed Ahmad Subhi Rafid, director of the Jordan Meteorological Department, who is one of the course participants, said he looks forward to learning from China's experience to create more development opportunities for developing countries.

Shanghai has been continuously enhancing its capabilities in disaster impact forecasting, risk warning, and disaster prevention and reduction in recent years. Currently, the city is constructing an urban multi-hazard early warning center and upgrading its urban multi-hazard early warning toolbox to facilitate global early warning technology sharing and deepen cooperation in universal early warning.

"We look forward to more collaborative efforts in advancing the development of the urban multi-hazard early warning toolbox 2.0 and global meteorological AI technology," said Feng Lei, director of the Shanghai Meteorological Service.