WMO AI Webinars
Advancing AI for weather, climate and water is a collective endeavor. The WMO AI Community of Practice is the shared space where Members collaborate to turn promising AI/ML research into operational value. Here, practitioners can discover and contribute models, tools, datasets, benchmarks and case studies across forecasting, climate services and hydrology. Through the WMO AI Webinars and this page, experts connect across regions to exchange lessons learned, compare results and co‑develop trustworthy, standards‑based solutions. Join us to share your work, learn from peers and help accelerate reliable AI across the WMO system.
(Please note that the WMO AI Community of Practice page plans to be created soon. Meanwhile, the information on AI is presented via this WMO AI Webinars page.)
WMO AI Webinars
| Time (UTC) | Presentation | Presenter | Resources |
|---|---|---|---|
| Second AI Webinar for 0800-0900 UTC on 27 April 2026 - entire recording | |||
| 0800-08:30 | WAS-NextGen: An Objective Multi-Method Framework for Seasonal Climate Forecasting in West Africa Abstract: Seasonal climate forecasting in West Africa has traditionally relied on consensus-based methods that lack reproducibility and high-resolution detail. This presentation introduces WAS-NextGen, an automated, multi-method framework that integrates machine-learning-calibrated multi-model ensembles (SV–ML–CMME), statistical–dynamical CCA calibration, lagged-predictor components, and analogue-year methods. Implemented via the open-source Python package wass2s, the system ensures a fully reproducible workflow from data acquisition to probabilistic mapping. | Mandela HOUNGNIBO and Abdou ALI (AGRHYMET RCC-WAS, Niger) | presentation(pdf), recording |
| 08:30-09:00 | Advancing Nowcasting with Deep Learning techniques (ANDeL) for West Africa Abstract: Accurate short-term rainfall prediction (0–6 hours lead time) remains a critical challenge across much of Africa, where sparse observational networks and the limitations of conventional numerical weather prediction systems hinder the representation of localized convective processes. The Advancing Nowcasting with Deep Learning techniques (ANDeL) project leverages deep learning architectures (convolutional LSTM and attention-based models) to predict the spatio-temporal evolution of rainfall using multi-source datasets [satellite-derived precipitation (IMERG) and reanalysis (ERA5)]. Initial model training was conducted using IMERG to establish a robust baseline; however, due to its latency (~4 hours), current operational testing employs Rain-over-Africa (RoA) data, which provides low-latency, high spatio-temporal resolution inputs suitable for near-real-time applications. The framework incorporates transfer learning and adaptive fine-tuning to enable efficient deployment across diverse regions, while maintaining a strong operational focus on low-compute environments. | Jeffrey N. A. Aryee (CDAI Lab, Dep't of Meteorology & Climate Science, FPCS, COS, KNUST, Ghana ) | presentation(pdf), recording |
| First AI Webinar for 1300-1400 UTC on 22 January 2026 - entire recording | |||
| 13:00-13:30 | Skilful long-lead nowcasting with NowAlpha in operations Achieving skilful, long-lead precipitation nowcasting remains challenging, particularly when relying on a single observation source. Here we present NowAlpha, an operational radar-only precipitation nowcasting system that extends skilful prediction to 410 minutes. NowAlpha formulates nowcasting as latent-space diffusion video generation: sequences of radar reflectivity are encoded by a continuous visual tokenizer, and a diffusion model generates future latent trajectories that are decoded back to physically plausible reflectivity evolutions. We adopt NVIDIA’s Cosmos tokenizer with pretrained weights, and find that reusing the pretrained tokenizer improves forecast quality compared with training the tokenizer from scratch, indicating that large-scale, general-purpose visual tokenization transfers effectively to the weather domain. In midlatitude regimes, NowAlpha reduces spurious westward tendencies and more faithfully reproduces organized wintertime coastal convective bands and cyclonic precipitation structures. Finally, NowAlpha is validated in operations through a research-to-operations cycle, incorporating iterative feedback from professional forecasters to improve reliability for decision support. | Hyesook Lee (KMA, Republic of Korea) | presentation(pdf), recording |
| 13:30-14:00 | Integration of AI/ML into operational weather and environmental forecasting systems at ECCC Environment and Climate Change Canada (ECCC) is integrating AI/ML into operational weather and environmental forecasting systems. This presentation will summarize the progress on the AI-physics hybrid Global Deterministic Prediction System with Spectral Nudging (GDPS-SN) and describe its path to operationalisation. The presentation will also describe progress made on the AI-based PARADIS weather model and feature other AI/ML projects for weather and environmental forecasting currently underway at ECCC. | Emilia Diaconescu and Stéphane Beauregard (ECCC, Canada) | Presentation(pdf), recording |