AI weather forecasting drives NiMet’s next-generation system
The Nigerian Meteorological Agency (NiMet) is accelerating the adoption of AI weather forecasting through a structured operational framework designed to transform how weather predictions, climate analysis and early warning services are delivered across Nigeria.
At the centre of this transformation is the development of the MeteoAI platform, alongside the evaluation of leading global AI weather prediction models. The initiative is aimed at improving forecast accuracy, strengthening climate resilience and delivering faster early warnings for agriculture and disaster preparedness.
Director-General and Chief Executive Officer of NiMet, Prof. Charles Anosike, outlined the Agency’s progress during the World Meteorological Organization (WMO) Executive Council Side Event on Scaling AI-Powered Weather Services for Farmers at the WMO Headquarters. His remarks highlighted a shift from discussion to structured implementation of AI systems in operational meteorology.
Prof. Anosike said NiMet inaugurated its AI Research Team in January 2026 to lead the identification, evaluation and deployment of Artificial Intelligence and machine learning technologies within the Agency. Since then, NiMet has moved to establish a clear governance structure to guide AI adoption across all operational units.
He explained that the Agency has developed AI Terms of Reference (TOR), Standard Operating Procedures (SOP), an AI Operational Framework with phased milestones, and documented AI use cases. These tools ensure that AI deployment is not experimental but structured, measurable and operationally integrated.
A major pillar of the initiative is the MeteoAI platform, which is being designed to enhance weather forecasting, climate prediction, forecast verification and early warning services. NiMet is also assessing globally recognised AI weather models to determine those best suited for Nigeria’s environmental and operational conditions.
In addition, the Agency is reviewing cost-effective computing infrastructure to support large-scale AI deployment. This step is critical to ensure that advanced forecasting systems remain sustainable while maintaining high performance.
Speaking during the session, Prof. Anosike emphasised that AI-driven forecasting depends heavily on reliable meteorological observations.
“Effective AI-driven weather forecasting begins with quality observations. We must address observation and data gaps across Africa because investment in observation systems is fundamental to unlocking the full potential of AI in meteorology,” he said.
He noted that without strong observation networks, even the most advanced AI systems would produce limited results. Therefore, strengthening data infrastructure remains central to improving forecast reliability and climate services across Africa.
The WMO side event brought together meteorological experts, global partners and development organisations to explore how AI can strengthen weather and climate services for agriculture. Discussions focused on how improved forecasting systems can support food security by delivering more timely and actionable information to farmers.
Prof. Anosike reaffirmed NiMet’s commitment to integrating Artificial Intelligence and modern geospatial technologies into its operations. He stressed that the Agency remains aligned with the Early Warnings for All initiative, ensuring vulnerable communities receive accurate and timely weather information.
“NiMet remains committed to leveraging AI and modern geospatial technologies to strengthen operational meteorology and advance the Early Warnings for All initiative across Africa, ensuring farmers and vulnerable communities benefit from more timely, accurate and actionable weather and climate information,” he said.
The structured approach to AI adoption positions NiMet as one of the leading meteorological agencies in Africa exploring operational AI integration. Therefore, the combination of AI weather forecasting systems, improved observational data, and scalable computing infrastructure is expected to significantly enhance forecasting accuracy and climate resilience in the region.
