SAGE - TMR Joint Workshop on AI/ML Application on S2S
Enhancing Sub-Seasonal Predictions with AI: Insights from the Joint SAGE–TMR Workshop
Enhancing sub-seasonal to seasonal prediction and advancing tropical meteorology research were recognized as vital for strengthening early warning systems and improving climate-sensitive services in agriculture, health, energy, and disaster risk reduction — essential steps toward building resilience amid increasing climate extremes. At the same time, integrating Artificial Intelligence (AI) and Machine Learning (ML) into forecasting was seen as offering powerful new opportunities to boost prediction skill, operational efficiency, and sectoral service delivery.
To explore these emerging frontiers, a hybrid joint workshop was hosted on 26 May 2025 on "Enhancing Sub-Seasonal Predictions with AI for Early Warnings" was convened by the Sub-seasonal to Seasonal Applications for Agriculture and Environment (SAGE) project and the Working Group on Tropical Meteorology Research (WGTMR), under the World Weather Research Programme (WWRP) of the World Meteorological Organization (WMO), with Shandong University as the local host.
Invited webinars and panel discussions have been included, focusing on:
S1- AI&ML application in S2S and tropical related predictions
S2 - S2S application in serving sectors
S3 - AI/ML for DRR and Early Warning
Agenda and links to the available presentations can be found below:
Time (UTC+8) | Theme | Presentation and Speakers | |
09:00-09:30 |
| Opening Ceremony | |
09:30-09:45 |
| Introduction about EW4All, WWRP, TMR and SAGE. Presentation
| Kunio Yoneyama (WWRP Scientific Steering Committee) |
09:45-10:15 | S1 | FuXi Weather: A Machine Learning System Surpassing Traditional Global Forecasting in Data-Sparse Regions. Presentation
| Xiaohui Zhong (Fudan University) |
10:15-10:45 | S3 | A multi-hazard early warning system framework for the SADC region. Presentation | Dewald van Niekerk (African Centre of Disaster Studies/North-West University) |
10:45-11:15 |
| Coffee Break | |
11:15-11:45 | S3 | Text as Data: LLM-Driven Disaster Warning Dataset and Optimization | Chao LI (China Warning Center of CMA) |
11:45-12:15 | S1 | Statistical and ML approaches in air pollution prediction: Interpreting the multiscale time-series characteristics and physicochemical processes | Tao LI (Shandong University) |
12:15-12:45 | S2 | Automate, anticipate accelerate – harnessing machine learning to mitigate climate risks in agriculture. Presentation
| Catherine Jones (FAO Regional Office for Asia and the Pacific) |
12:45-14:00 |
| Lunch Break | |
14:00-14:30 | S2 | Monthly to seasonal forecast of Global Wind and Solar Power Generation Capacity | Yunyun Liu (National Climate Centre of CMA) |
14:30-15:00 | S2 | Integration of S2S predictions in early warning systems for climate-sensitive health risks. Presentation
| Chloe Fletcher (Barcelona Supercomputing Center) |
15:00-15:30 | S1 | Developing data-driven S2S prediction at ECMWF. Presentation
| Steffen Tietsche (ECMWF) |
15:30-16:00 |
| Coffee Break | |
16:00-16:30 | S1 | Using interpretable gradient-boosted decision-tree ensembles to uncover novel dynamical relationships governing monsoon low-pressure systems. Presentation
| Kieran Hunt (University of Reading) |
16:30-17:00 | S2 | Statistical and AI/ML methods to improve subseasonal to seasonal (S2S) wind speeds over India. Presentation
| Aheli Das (University of Reading) |
17:00-17:45 |
| Panel Discussion: Gap and advances in Science to Application (In-person)
Closure
| |
Looking Ahead
As science and technology evolve, the workshop emphasised the importance of international collaboration—underpinned by the SAGE Project and WG TMR—to ensure that AI-enabled S2S forecasts and services contribute meaningfully to the "Early Warnings for All" initiative. Continued dialogue across disciplines will be key to unlocking the full potential of S2S predictions in a changing climate.