Introduction in Artificial Intelligence for Weather and Climate Modelling

Over 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. Mouhamadou Bamba Sylla is the AIMS Network Research Chair in Climate Change Science based at the African Institute for Mathematical Sciences, Research and Innovation center (AIMS-RIC) in Kigali, Rwanda, where he leads a group of researchers, postdoctoral fellows, doctoral students, and master’s students. Previously, he was the Director of Research and Senior Scientist in Climate Modeling and Climate Change at the WASCAL Competence Center in Ouagadougou, Burkina Faso. At the same institution, he was also appointed as the Head of Climate Change and Climate Variability Research Cluster and Acting Coordinator of Observation Networks at different periods. He has completed a postdoctoral fellowship at the International Centre for Theoretical Physics (ICTP) in Trieste, Italy, and was a researcher and lecturer at Loyola Marymount University | Los Angeles (LMU|LA), California, USA. His research interests are climate change and impacts, climate modeling, scenarios and uncertainties, climate hazards and risks, climate information for adaptation and DRR, climate services, climate policy, AI/ML for climate science among others. With over 15 years of post-PhD experience, he has authored/co-authored more than 100 publications in international journals and books on climate science (see my Google Scholar:
, taught various courses related to climate change and climate modeling at various universities (Africa and the United States), and contributed to numerous training courses, workshops, and conferences worldwide.

Dr. 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.

I am currently Associate Professor in Actuarial Science in the Mathematics Department at the University of Manchester. My research centers around applications of machine learning in climate risk management. I am a Co-Founder of AfriClimate AI a grassroots research community dedicated to harnessing the power of Artificial Intelligence for a sustainable, prosperous and climate-resilient Africa.

I was previously the Google DeepMind Academic Fellow in Machine Learning in the School of Electrical Engineering and Computer Science at the Queen Mary University of London and an Associate Professor in Actuarial Science at the University of Witwatersrand.

I completed my PhD at the University of Johannesburg under the supervision of Professor Tshilidzi Marwala and Dr Ilyes Boulkaibet concentrating on Shadow Hamiltonian Monte Carlo Methods within Bayesian Neural Networks. I was a recipient of the 2019 Google Africa PhD fellowship, which supported my PhD work. I am the author of the book Hamiltonian Monte Carlo Methods in Machine Learning.

I am a qualified actuary holding fellowships of Institute and Faculty of Actuaries and the Actuarial Society of South Africa.

I am an associate professor at the University of Chicago’s Department of Geophysical Sciences, Committee of Computational and Applied Mathematics, Committee on Data Science (https://codas.uchicago.edu/people/faculty/), and Data Science Institute. received my Ph.D. (geophysical turbulence) and M.A. (applied mathematics) from UC Berkeley in 2013 and was a Ziff Environmental Fellow at the Harvard University Center for the Environment and Department of Earth and Planetary Sciences from 2013 to 2016. I received a CAREER Award from NSF in 2021 and a Young Investigator Award from the Office of Naval Research in 2020.

I lead the Climate Extremes Theory and Data (CeTD) group focused on integrating theory, simulations, observations, and machine learning techniques to understand the dynamics and future changes of extreme weather events in a changing climate. I also lead the AI for Climate (AICE) Initiative, a joint program of the Data Science Institute and Institute for Climate and Sustainable Growth (ICSG), which is focused on inter-disciplinary integration of AI with fundamental domain knowledge to accelerate and transform climate research with a focus on both scientific advances and societal impacts. With Michael Kremer and Amir Jina, I co-direct ICSG’s Human-centered Weather Forecasts (HCF) Initiative, which leverages the power of AI to innovate, benchmark, and democratize weather forecasts that can target what citizens on the ground need to know.

Nishadh 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.

My name is Wessel. I am currently a Research Lead for the Machine Learning Group at the University of Cambridge. Our team’s goal is to advance environmental forecasting with end-to-end machine learning. I am also a Quantative Researcher for Cedalion Capital. Previously, I was a Research Lead for the The Alan Turing Institute and a Senior Researcher on the AI for Science initiative at Microsoft Research Amsterdam. We developed the Aurora model and open sourced the model too.

I hold a PhD in Machine from the University of Cambridge, supervised by Dr Richard Turner, an MPhil in Machine Learning and Machine Intelligence from the University of Cambridge, and a BSc in Electrical Engineering from the University of Delft. My curriculum vitae can be found here.

My research interests include machine learning for environmental science and forecasting, probabilistic modelling with a focus on time series, Bayesian nonparametrics with a focus on Gaussian processes, approximate inference, probabilistic programming, probability theory, and real analysis.

My research focusses on developing lightweight statistical tools to model complex weather and climate processes. I work a lot with understanding the spatio-temporal regularities within noisy weather structures so as to be able to approximate them in a realistic/consistent manner. This allows better understanding of the uncertainty space, and more importantly, agile exploration of it. I employ a range of techniques for this, going from traditional statistical techniques to more recent artificial intelligence techniques.

I also dabble around with cloud-optimised artificial intelligence based training on large datasets (while trying to not use up too many credits), and multi-resolutional image analysis and decomposition. My previous work was at an NGO, and I try to maintain a strong lens on stakeholder engagement and capacity building within my work.

Dr. David John Gagne II is a Machine Learning Scientist II and head of the Machine Integration and Learning for Earth Systems (MILES) group at the NSF National Center for Atmospheric Research (NCAR) in Boulder, Colorado. He has led the development of machine learning systems that enhance understanding and prediction of high impact weather and critical Earth system processes. He received his Ph.D. in meteorology from the University of Oklahoma in 2016 and completed an NCAR ASP Postdoctoral Fellowship before assuming his current role. He is a NSF AI2ES AI Institute leader and a NSF LEAP Science and Technology Center member. He is a WMO Nowcasting and Mesoscale Research Working Group member, chaired the American Meteorological Society Artificial Intelligence Committee, and serves as an editor for the AI for the Earth Systems journal, and has led summer schools, short courses, and hackathons.

I work at the intersection of artificial intelligence and Earth system science, exploring a range of dynamical and predictability questions at the interfaces of observations, numerical, and statistical models. I also serve as Scientific Coordinator for the ECMWF Machine Learning Training Program under the DestinE initiative, contributing to international efforts in AI-driven Earth system modeling.
My research focuses on developing physically consistent and interpretable AI models to better understand atmospheric processes across scales. In particular, I work on representation learning and generative modeling using satellite observations and km-scale numerical simulations. More broadly, my goal is to bridge the gap between predictive skill and scientific understanding, enabling trustworthy AI for weather forecasting and climate-scale applications.

David Hogg is Professor of Artificial Intelligence at the University of Leeds. He is internationally recognized for his work on computer vision, particularly in the areas of video analysis and activity recognition. He works extensively across disciplinary boundaries, applying AI in engineering design, biology, medicine and environmental sciences. He has been a visiting professor at the MIT Media Lab, Pro-Vice-Chancellor for Research and Innovation at the University of Leeds (2011-2016), Chair of the ICT Strategic Advisory Team at the Engineering and Physical Sciences Research Council (EPSRC) in the UK, Chair of an international review panel for Robotics and Artificial Intelligence commissioned by EPSRC (2017), a Turing Fellow, and Co-Director of the National Pathology Imaging Co-operative. Until 2018, he was Chair of the Academic Advisory Group of the Worldwide Universities Network (WUN), helping to promote collaborative research between over 20 prominent research-intensive universities from around the globe. He is Director of the UKRI Centre for Doctoral Training in AI for Medical Diagnosis and Care. David is a Fellow of the European Association for Artificial Intelligence (EurAI), a Distinguished Fellow of the British Machine Vision Association, and a Fellow of the International Association for Pattern Recognition.

As a dedicated research meteorologist and data scientist specializing in weather and climate, I focus on Hybrid AI+NWP forecasting - combining Numerical Weather Prediction (NWP) models like WRF, ICON, and WAVEWATCH III with AI-based methods for accurate weather predictions, extreme weather event analysis, and climate data assessment, using visualization and analysis tools like Python, R, GrADS, and NCL. I currently apply this approach at the Tanzania Meteorological Authority (TMA).

I’m Rich Turner, Professor of Machine Learning in the Machine Learning Group at the University of Cambridge.

I am the Cambridge Lead for the EPSRC Probabilistic AI Hub, Lead of the Cumulus AI Weather Forecasting Project, and Cambridge lead of the DVPS Foundation Models For Future AI Initiative.

My previous roles include Research Lead, AI for Weather Prediction, at the Alan Turing Institute, Visiting Researcher at Microsoft Research in the AI4Science and AI teams, Co-Director of the UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER CDT), and Course Director of the Machine Learning and Machine Intelligence MPhil programme. I have received over £35 million of funding as a Principal or Co-Investigator. I’ve been awarded the Cambridge Students’ Union Teaching Award for Lecturing. My research has featured in the Times, Guardian, New York Times, Washington Post, and on the BBC.

"Dr. Md. Abul Ehsan Bhuiyan received his Ph.D. in Civil and Environmental Engineering from the University of Connecticut. He currently serves as an AI/ML expert in Hydrology at the International Desk, Climate Prediction Center (CPC), NOAA. His research focuses on applying artificial intelligence and machine learning to weather forecasting, climate prediction, remote sensing, and hydrological modeling. His current work includes developing AI-based operational forecasting systems for floods, extreme precipitation, and heatwaves by integrating satellite observations, reanalysis data, and numerical weather prediction models. His research aims to advance early warning systems and improve decision-making for weather, climate, and water resources management."

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.