Session GDB4

[Programme]

GDB4 | Early Warnings for All (EW4ALL) Initiative: science needs for the global ambition to protect everyone by 2027

Early Warnings for All (EW4ALL) Initiative: science needs for the global ambition to protect everyone by 2027

Convener: Elena Xoplaki | Co-conveners: Ilias Pechlivanidis, Monique Kuglitsch, Maria-Helena Ramos

Programme

| Thu, 18 Apr, 10:45–12:30 (CEST)

Room E1

Vimeo: GDB4 – Early Warnings for All (EW4ALL) Initiative: science needs for the global ambition to protect everyone by 2027

The "Early Warnings for All (EW4ALL)" initiative announced by the United Nations (UN) Secretary General in March 2022 is a ground-breaking effort to ensure that everyone on Earth is protected from hazardous weather, water or climate events through life-saving early warning systems by the end of 2027. Delivering on this call requires global collaboration: the initiative is co-led by the World Meteorological Organization (WMO) and the UN Office for Disaster Risk Reduction (UNDRR), and supported by the International Telecommunication Union (ITU) and the International Federation of Red Cross and Red Crescent Societies (IFRC).

EW4ALL is built around four key pillars:
1. Disaster risk knowledge and management
2. Detection, observation, monitoring, analysis, and forecasting
3. Warning dissemination and communication
4. Preparedness and response capabilities

Despite advancements related to weather, water and climate, there are still scientific and technological challenges that need to be addressed in order to improve the provision of accurate and effective early warnings for a variety of hazards. For instance, advancements are needed to improve impact-based, people-centered forecasting and warning systems, as well as to enhance our understanding of how climate will continue to change and vary in order to ensure that multi-hazard early warnings are effective not only in today’s conditions, but also in the future. Research is also needed to better understand how to best communicate warnings and uncertainty to ensure that warnings are understood and acted upon, or to understand how machine learning and artificial intelligence can help setting high-quality impact-focused and timely operational forecasts.

In this debate, we will discuss those challenges for scientists working in the field of natural hazards monitoring and forecasting, impacts and disaster prevention, and social sciences. We will discuss how to address them through scientific and technological innovations, enabling this action plan and providing early warnings for all.