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Climate and weather expert to speak at global energy conference

Prof. Alberto Troccoli Co-founder and CEO, Inside Climate Service (srl) and World Energy & Meteorology Council, UK Prof. Alberto Troccoli Co-founder and CEO, Inside Climate Service (srl) and World Energy & Meteorology Council, UK
Prof. Alberto Troccoli Co-founder and CEO, Inside Climate Service (srl) and World Energy & Meteorology Council, UK

A scientist with over 30 years of experience in meteorology and climate services, and whose work has taken him from NASA to the United Nations, will speak at the Energies 3.0 Conference, a virtual international event running from April 22 to 24, 2026.

The conference, organised by the Green Institute of Nigeria, runs daily from 10:00 AM to 4:00 PM online. Registration is open at www.greeninstitute.ng/energies2026.

Prof. Alberto Troccoli is the co-founder and CEO of Inside Climate Service and the co-founder and Managing Director of the World Energy and Meteorology Council, both based in the United Kingdom. His career has taken him through institutions including NASA, the European Centre for Medium-Range Weather Forecasts in the UK, the University of Reading, CSIRO in Australia and the University of East Anglia, where he has held a visiting professorship. He holds a PhD in Physical Oceanography from the University of Edinburgh and has published four books as chief editor and author. He has also authored several United Nations World Meteorological Organization publications, including the Global Framework for Climate Services Energy Sector Exemplar in 2017, the Integrated Weather and Climate Services in Support of Net Zero Energy Transition in 2023, and three editions of the Year in Review: Climate-driven Global Renewable Energy Potential Resources and Energy Demand covering 2022 to 2024. He leads the Copernicus Climate Change Service’s Energy operational service and developed the Teal tool, a free interactive visualisation platform at tealtool.earth that makes global historical and projected climate and carbon emissions data accessible to users worldwide.

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Asked how climate intelligence and weather-informed energy planning can strengthen resilience in emerging economies, Prof. Troccoli said the shift it enables is fundamental.

“Climate intelligence shifts energy systems from reactive to anticipatory. By combining weather data, forecasts and climate projections, countries can better manage variability, prepare for extremes, and invest smarter. As highlighted in recent WMO-IRENA reports from 2022 to 2024, this is key to building energy systems that are not just low-carbon, but truly resilient,” he said.

He said weather-informed planning supports more resilient grid operations, improves resource adequacy assessments and reduces the likelihood of disruptions. He added that it enables more efficient deployment of distributed energy systems, which are particularly relevant in many Global South contexts, and that embedding climate intelligence into decision-making helps ensure energy transitions are robust, adaptive and aligned with local environmental conditions.

On the role of meteorological data in improving the reliability of renewable energy systems across the Global South, Prof. Troccoli was direct.

“Meteorological data is the backbone of renewable energy. Without it, you can’t reliably forecast solar and wind generation or operate stable grids. Better weather and climate data, combined with AI and satellite observations, can dramatically improve reliability, reduce risk, and accelerate clean energy deployment in the Global South,” he said.

He said solar and wind power are inherently weather-dependent, and that accurate meteorological information across timescales ranging from minutes to decades is essential for forecasting generation, balancing supply and demand and planning system expansion. In the Global South, where observational networks can be sparse, he said combining satellite data, reanalysis products and local measurements together with machine learning and artificial intelligence is crucial to building reliable datasets.

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