Background
The programme contributes to the development of an African-led AI and climate-modelling community working closely with NMHSs, RCCs, universities, research institutions, relevant sectors and the global AI and climate community.
Overall purpose
To strengthen Africa’s capacity to apply artificial intelligence in weather and climate modelling in order to improve climate information services, early warning and disaster risk reduction.
Objectives
The Summer School will help participants understand, evaluate and apply AI and machine-learning approaches in African weather and climate-service contexts.
Understand
Introduce the concepts and principles of artificial intelligence and machine learning for weather and climate modelling.
Connect
Explain how AI can complement numerical prediction systems, climate models, observations and expert forecasting.
Explore
Examine practical applications in forecasting, bias correction, downscaling, nowcasting and extreme-event monitoring.
Apply
Provide hands-on experience with AI and machine-learning workflows using African weather and climate datasets.
Evaluate
Build participants’ ability to assess model accuracy, bias, uncertainty, limitations and operational suitability.
Collaborate
Promote sustained collaboration among NMHSs, RCCs, universities, researchers and relevant climate-service sectors.
Eligibility
This programme is designed for early- to mid-career professionals working at the intersection of climate science and data. We welcome applicants from National Meteorological and Hydrological Services (NMHSs), universities, and research institutions across Africa who are ready to apply machine learning methods to real climate and weather challenges.
- Affiliation with an NMHS, RCC, university, school, research institution or climate-related technical agency.
- Background in meteorology, climatology, hydrology, computer science, data science, geography or a related field.
- Basic knowledge of Python, or willingness to complete a pre-course Python module
- Demonstrated interest in AI applications for weather, climate or early-warning services.
- Institutional support or a clear plan for applying the training after the Summer School.
- Commitment to participate in all sessions, practical exercises and group project activities.
Programme Structure
Explain
The role, opportunities and limitations of AI in weather and climate services.
Prepare
Weather and climate data for AI and machine-learning workflows.
Foundation sessions
Practical sessions using state-of-the-art weather and climate models.
Hands-on Modelling labs
Practical sessions using state-of-the-art weather and climate models.
Applied Al modules
Machine learning and Al for climate applications across key sectors.
Group projects
Collaborative projects tackling real climate challenges
Trainers
Dr. Ousemane Ndiaye
Director General ACMAD Lead FacilitatorOver 26 years of experience, Dr Ndiaye is a champion in delivering climate services, he won the African prize of best national meteorological services in 2023. Before joining ACMAD, Dr. Ndiaye served as the Director of Senegal’s Meteorological Services at ANACIM, where he spearheaded several innovative projects, including a major partnership with NOAA to develop heatwave modeling systems. This initiative significantly enhanced early warning systems for heatwaves in Senegal, improving the resilience of communities to climate impacts.
Dr. Patrick Kinyua
AI and Impact-Based Forecasting Expert ACMAD InstructorModule: Machine Learning and AI
Dr. Patrick Kinyua is an AI and data science expert currently leading Artificial Intelligence and Impact-Based Forecasting initiatives at the African Centre of Meteorological Applications for Development (ACMAD). He has over 15 years of experience applying machine learning, predictive analytics, and responsible AI to climate services, early warning, agriculture and in humanitarian settings. His work focuses on developing AI-enabled forecasting systems, strengthening institutional capacity, and supporting anticipatory action for vulnerable communities across Africa.
Dr. Redouane Lguensat
Machine Learning & Climate Scientist Institut Pierre-Simon Laplace Instructor View bio
Dr. Redouane Lguensat
Machine Learning & Climate Scientist Institut Pierre-Simon Laplace InstructorDr. Redouane Lguensat is a senior climate data scientist at the Institut Pierre-Simon Laplace (IPSL) Climate Modeling Center, Sorbonne University, Paris, France. His work focuses on the application of machine learning to numerical climate modeling, model tuning, statistical downscaling, and bias correction. He holds a PhD in Computer Vision from IMT Atlantique and previously held a Make Our Planet Great Again (MOPGA) postdoctoral fellowship. Redouane is strongly engaged in applying machine-learning-based climate tools to challenges relevant to the Global South, particularly in North and West Africa, with a focus on machine learning capacity building and improved access to climate information.
Prof. Pedram Hassanzadeh
Climate Dynamics & AI Scientist Instructor
Dr. Nishadh Kalladath
Data Science and Machine Learning NORCAP expert in ICPAC InstructorNishadh is a NORCAP expert in Data Science and Machine Learning at the IGAD Climate Prediction and Applications Center (ICPAC) in Nairobi, Kenya. He is actively involved in research and development of impact-based forecasting (IBF) systems for the East African region. Currently, Nishadh is working on a CRAF’d-funded project within the Disaster Risk Management Programme at ICPAC, aiming to operationalize impact-based forecasting through hazard and impact modeling using the storylines approach and leveraging Bayesian networks.His work focuses on enhancing climate resilience and preparedness against weather-related disasters across East Africa. Nishadh holds a PhD in Environmental Sciences from Bharathiar University in Tamil Nadu, India. His doctoral thesis, titled “Particulate Air Pollution Data for Coimbatore, India: Real-Time Monitoring and Modeling with Data-Interoperability Measures,” emphasized the critical need for open, standardized real-time data for effective pollution monitoring, weather forecasting, and air quality risk management.
Schedule
Morning session
Foundations of AI for Weather and Climate
- Inauguration and opening remarks
- Keynote on AI in weather and climate modelling
- Introduction to artificial intelligence and machine learning
- AI, machine learning and deep-learning fundamentals
Afternoon session
- Opportunities for African weather and climate services
- Limitations, uncertainty and responsible use
- How AI complements numerical models and expert forecasting
- Examples of operational and research applications
Morning session
Weather and Climate Data for AI
- Station observations
- Satellite products
- Reanalysis datasets
- Forecast datasets
- Climate-model outputs
- Data quality, gaps and representativeness
Afternoon session
Weather and Climate Data for AI
- Introduction to CSV, NetCDF and GRIB
- Basic data exploration using Python
- Spatial and temporal subsetting
- Data cleaning and preprocessing
- Preparing predictors and target variables
Morning session
Building a Machine-Learning Model
- Defining a weather or climate prediction problem
- Selecting predictors and target variables
- Training, validation and testing datasets
- Model selection and baseline methods
- Building a simple machine-learning model
Afternoon group work
Building a Machine-Learning Model
- Forecasting
- Bias correction
- Statistical downscaling
- Nowcasting
- Extreme-event monitoring
- Impact-based forecasting
Morning session
Applied AI Workflows and Model Evaluation
- AI workflow for temperature or rainfall prediction
- Feature preparation
- Model training and prediction
- Review and improvement of group prototypes
Afternoon session
Applied AI Workflows and Model Evaluation
- Accuracy and forecast skill
- Mean Absolute Error and Root Mean Square Error
- Forecast bias
- Precision, recall and F1 score
- Interpretation of results
- Uncertainty and model limitations
Projects, Institutional Plans and Certification
- Finalisation of group prototypes
- Group project presentations
- Peer and facilitator feedback
- Institutional application plans
- Participant reflections
- Post-training assessment
- Community-of-practice launch
- Course evaluation
- Certification and closing ceremony
Mon, 19 Oct 2026
Morning session
Foundations of AI for Weather and Climate
- Inauguration and opening remarks
- Keynote on AI in weather and climate modelling
- Introduction to artificial intelligence and machine learning
- AI, machine learning and deep-learning fundamentals
Afternoon session
- Opportunities for African weather and climate services
- Limitations, uncertainty and responsible use
- How AI complements numerical models and expert forecasting
- Examples of operational and research applications
Concept Note 2026
The concept note outlines the objectives, target participants, training modules and expected outcomes for the upcoming ACMAD Summer School.
- ✓ Objectives
- ✓ Training modules
- ✓ Target participants
- ✓ Expected outcomes
Sponsors, Organizers & Partners
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