The Writer
As climate extremes intensify across Africa, the need for accurate and timely weather prediction has become increasingly urgent.
Floods, heatwaves, droughts, and air pollution events are placing a growing strain on communities, infrastructure, and public health systems—often in regions with limited forecasting capacity. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI)–based weather models are now offering promising new tools to help close this gap.
Research led by Ghanaian atmospheric scientist Dr. Kwesi Twentwewa Quagraine, in collaboration with scientists across Africa and international institutions, demonstrates how ML/AI approaches can significantly enhance weather and climate prediction over data-sparse regions of the continent.
These models, trained on decades of reanalysis and observational data, have the potential to improve early warning systems for extreme weather events and environmental hazards.
One area where Dr. Quagraine and his colleagues, applying these techniques, are already showing impact is air quality monitoring.
In a recent study published in PLOS Climate, researchers from Kwame Nkrumah University of Science and Technology (KNUST), Ghana, and collaborators applied satellite-based MODIS observations combined with machine learning models to estimate aerosol optical depth (AOD) over Ghana from 2003 to 2019.
The study addressed a major challenge in environmental health research: the lack of dense ground-based air quality monitoring networks.
The results revealed elevated aerosol concentrations in southwestern Ghana, linked to biogenic emissions and small-scale surface mining activities, with implications for both urban and rural public health.
Notably, a hybrid approach that combined artificial neural network (ANN) structures with multiple linear regression (MLR) outperformed other models, demonstrating the value of ML-informed statistical frameworks for environmental monitoring.
Beyond air quality, ML/AI weather models are also proving effective for forecasting climate extremes. Ongoing research involving Dr. Quagraine and other collaborators from Lawrence Berkeley National Laboratory, the University of California (Berkeley and Irvine), NVIDIA Corporation, and the National Energy Research Scientific Computing Center (NERSC) has shown that advanced AI architectures—such as NVIDIA’s Spherical Fourier Neural Operator–based weather model—can skillfully capture the tails of extreme heat distributions over regions like North Africa. This capability is particularly important for anticipating rare but high-impact heat events, which pose serious risks to human health, agriculture, and energy systems.
The broader implications of this work are significant. More than 70 percent of African countries fall within the low- to middle-income range, and the Intergovernmental Panel on Climate Change identifies the continent as one of the most climate-vulnerable regions globally.
Limited infrastructure, planning capacity, and mitigation resources mean that extreme weather events often have disproportionate societal impacts. By improving early warning systems and environmental monitoring, ML/AI-based weather forecasting offers a pathway to reduce climate risk, support adaptation planning, and enhance resilience.
As this body of research shows, machine learning is not replacing traditional physics-based models, but rather complementing them—offering computationally efficient, scalable tools that can be tailored to regions where conventional forecasting systems and observations face limitations.
Continued collaboration, capacity building, and responsible deployment will be essential to ensure these technologies deliver tangible benefits across Africa.
By Thomas Kwasi Kwarteng
