Artificial Intelligence for Detection and Attribution of Extreme Weather Events in Europe Webinar – World Meteorological Organization WMO

WMO Initiative Leverages Artificial Intelligence to Advance Sustainable Development Goals through Enhanced Climate Services

Introduction: Fostering Global Partnerships for Climate Resilience

The World Meteorological Organization (WMO) Working Group on Research has initiated the Research to Operations Exchange (ROX) lecture series. This initiative is designed to create a collaborative platform for researchers, practitioners, and policymakers, directly supporting SDG 17 (Partnerships for the Goals). The primary objective is to translate scientific innovations into strengthened hydro-meteorological services, a critical component for achieving global resilience against climate-related hazards as outlined in SDG 13 (Climate Action) and SDG 11 (Sustainable Cities and Communities).

Inaugural Lecture: AI for Extreme Event Detection and Attribution

The first lecture, conducted on 24 September 2025, focused on the XAIDA (Extreme Events: Artificial Intelligence for Detection and Attribution) project. This project, funded by the EU Horizon 2020 programme, exemplifies SDG 9 (Industry, Innovation, and Infrastructure) by applying cutting-edge artificial intelligence to improve the detection and attribution of extreme weather events. The application of this technology provides a robust, science-based foundation for decision-making, which is essential for mitigating the impacts of climate change and protecting vulnerable populations.

Key Outcomes and Contributions to Sustainable Development Goals

The discussions highlighted the significant potential of AI to accelerate progress on several SDGs:

• Enhanced Climate Action (SDG 13): By improving the scientific understanding and attribution of extreme weather events, AI tools like those developed in the XAIDA project provide policymakers with clearer evidence to develop effective climate adaptation and mitigation strategies.
• Resilient Infrastructure and Communities (SDG 11 & SDG 9): Advanced forecasting and detection capabilities are fundamental to building resilient infrastructure and developing early-warning systems that protect human settlements from climate-related disasters.
• Improved Health and Food Security (SDG 3 & SDG 2): Better hydro-meteorological services contribute to safeguarding public health and ensuring food security by enabling proactive measures against droughts, floods, and other extreme weather phenomena.

Identified Challenges in Operational Integration

To fully realize these contributions to the SDGs, the lecture identified several critical challenges that must be addressed to transition research into operational services:

1. Data Readiness: A significant need exists for well-structured, high-quality, and AI-readable data to train and validate machine learning models effectively.
2. Communication of Uncertainty: Developing clear and transparent methods for communicating the uncertainties inherent in AI-based forecasts is crucial for maintaining public trust and ensuring appropriate responses.
3. Investment in Capacity: Substantial investment in both human capital and technical infrastructure is required to bridge the gap between research innovation and its successful integration into daily operational workflows.

Resources for Further Engagement

A recording of the inaugural lecture is available for public access, providing a valuable resource for capacity building and knowledge sharing in line with the principles of SDG 17. The material can be accessed on the WMO YouTube channel: Artificial Intelligence for Detection and Attribution of Extreme Weather Events in Europe.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 9: Industry, Innovation and Infrastructure: The article focuses on innovation, specifically the application of artificial intelligence (AI) to improve hydro-meteorological services. The mention of the “XAIDA project (Extreme Events: Artificial Intelligence for Detection and Attribution)” and the need for “significant investment in human and technical resources” directly relates to enhancing scientific research and upgrading technological capabilities.
• SDG 13: Climate Action: The core subject of the article is strengthening the ability to detect and attribute “extreme weather events.” This directly supports efforts to combat climate change and its impacts by improving resilience and adaptive capacity through better forecasting and science-based decision-making.
• SDG 17: Partnerships for the Goals: The article describes a collaborative effort. The “WMO Working Group on Research has launched a new lecture series” designed to bring “researchers, practitioners, and policymakers together.” This initiative, along with the EU-funded XAIDA project, exemplifies a multi-stakeholder partnership to achieve common goals.

2. What specific targets under those SDGs can be identified based on the article’s content?

• Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries, in particular developing countries, including, by 2030, encouraging innovation and substantially increasing the number of research and development workers per 1 million people and public and private research and development spending.
  • Explanation: The article’s focus on the XAIDA project, which uses AI to advance service capabilities, and the discussion on the need for “significant investment in human and technical resources to ensure research results can be successfully integrated into operational services” directly aligns with this target of enhancing research and encouraging innovation.
• Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.
  • Explanation: The entire purpose of applying AI to “detect and attribute extreme weather events” is to strengthen hydro-meteorological services. Better detection and forecasting directly contribute to building resilience against climate-related hazards.
• Target 17.16: Enhance the global partnership for sustainable development, complemented by multi-stakeholder partnerships that mobilize and share knowledge, expertise, technology and financial resources, to support the achievement of the sustainable development goals in all countries, in particular developing countries.
  • Explanation: The ROX lecture series is a clear example of this target in action. It is a platform designed to “bring researchers, practitioners, and policymakers together” to share knowledge (“how innovations can strengthen hydro-meteorological services worldwide”). The “high level of participation from across Europe and beyond” indicates a successful multi-stakeholder partnership.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Implied Indicator for Target 9.5: The article implies progress through the existence and funding of advanced research projects. The “XAIDA project… funded under the EU Horizon 2020 programme” serves as a qualitative indicator of investment in research and development (related to official indicator 9.5.1). The call for “significant investment” also points to this as a key metric for success.
• Implied Indicator for Target 13.1: The article implies that a key measure of progress is the successful operationalization of research. The goal to “ensure research results can be successfully integrated into operational services” is an indicator of enhanced capacity. This relates to the development and implementation of early warning systems for climate-related disasters.
• Implied Indicator for Target 17.16: The article provides direct qualitative indicators of partnership. The “launch of a new lecture series” and the “high level of participation” are measures of the establishment and engagement of multi-stakeholder partnerships focused on sharing knowledge and expertise.

4. Table of SDGs, Targets, and Indicators

Source: wmo.int