Strengthening early warning systems for anticipatory action - SEWAA project: Mid term achievements

This report presents the mid-term achievements of the Strengthening Early Warning Systems for Anticipatory Action (SEWAA) project, a collaboration between WFP, the University of Oxford, and regional institutions including ICPAC, KMD, and EMI, with catalytic support from Google.org. By applying advanced machine learning techniques—specifically Conditional Generative Adversarial Networks (cGANs)—the project enables the generation of high-resolution, probabilistic rainfall forecasts on personal computers. This innovation reduces reliance on costly supercomputers, accelerates forecast production, and empowers meteorological services in resource-constrained settings to provide more accurate and actionable climate information for anticipatory action.

The report highlights major achievements, including faster forecast generation, reduced costs through cloud-based systems, and strengthened local expertise through training and institutional ownership. Forecasts that once took hours can now be produced in under an hour, with multiple updates daily, enabling more timely and informed decisions. Looking ahead, SEWAA aims to expand its reach with longer-range forecasts, improved accessibility through web platforms, rigorous model validation, and regional capacity-building. By proving that low-cost, AI-driven forecasting is both feasible and scalable, SEWAA demonstrates a transformative shift in how countries can prepare for and respond to extreme weather events.