Weather and Climate: Applications of Machine Learning and Artificial Intelligence
Simon Driscoll · Kieran M R Hunt · Laura Mansfield · Henry Moss · Ranjini Swaminathan · Hong Wei · Eviatar Bach · Alison Peard
Apr 2026 · Developments in Weather and Climate Science Book 12 · Elsevier
Ebook
300
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infoThis book will become available on April 1, 2026. You will not be charged until it is released.
About this ebook
Weather and Climate: Applications of Machine Learning and Artificial Intelligence provides a comprehensive exploration of machine learning in the context of weather forecasting and climate research. The authors begin with an introduction to the fundamentals and statistical tools of machine learning, followed by an overview of various machine learning models. Emulation and machine learning of sub-grid scale parametrizations are discussed, along with the application of AI/ML in weather forecasting and climate models. Next, the book delves into the concept of explainable AI (XAI) methods for understanding ML and AI models, as well as the use of generative AI in weather and climate research. It explores the interface of data assimilation and machine learning for weather forecasting, showcasing case studies of machine learning applied to environmental monitoring data. The book concludes by looking ahead to the future of ML and AI in climate and weather-related research, providing references for further reading. This comprehensive guide offers valuable insights into the intersection of machine learning, artificial intelligence, and atmospheric science, highlighting the potential for innovation and advancement in weather and climate research.• Provides a concise, singular resource for understanding machine learning and fundamental statistical tools relevant to weather and climate modelling• Examines state-of-the-art AI and ML approaches and their implementation in weather and climate, with extensive Python and Jupyter Notebooks for readers• Discusses future directions and the latest, most cutting-edge developments and applications of AI and ML to weather and climate science
About the author
Dr. Simon Driscoll has a background training in pure and applied mathematics, and a DPhil from the University of Oxford. Initially specialising in volcanic eruptions, stratospheric dynamics and geoengineering, he was part of the UK's Stratospheric Particle for Climate Engineering project. Accordingly he has extensive experience in atmospheric physics and climate modelling. His research has been covered in various newspapers and books around the world and he has featured in a documentary on geoengineering for VRT (Belgian national TV). Excited by the power and revolutionary potential of machine learning and AI techniques he refocused his research on machine learning methods as part of the Schmidt Sciences funded Scale Aware Sea Ice project. Based at the University of Reading he primarily focuses on building emulators of sub-grid scale parametrisations as well as conducting research into the AI weather forecasting models and other applications of ML and AI in weather and climate.Dr. Kieran Hunt is a NERC independent research fellow in tropical meteorology and AI at the University of Reading and National Centre for Atmospheric Science. His career has largely focused on developing and using methods to understand how extreme weather events develop over South Asia and the Himalaya, with implications for weather forecasting, water security, and energy systems. In recent years, he has shifted his focus to tackling these problems with explainable machine learning. This has led to a diverse range of applications, including: new dynamical understanding of monsoon systems, developing the first operational machine learning hydrology forecasts, vast improvements to paleoclimate modelling, and a state-of-the-art energy demand model for India. He has taught extensively and supervised numerous research projects on the applications of machine learning in weather.Dr. Laura Mansfield is a postdoctoral researcher with an interest in how machine learning and Bayesian statistics can improve climate prediction. Currently based at Stanford University, she uses machine learning to enhance the representation of subgrid-scale atmospheric gravity waves and the resulting stratospheric circulation in climate models. She develops machine learning approaches to both replace physics-based parametrisations and to aid calibration of physics-based parametrisations. She is passionate about uncertainty quantification and will soon be a Schmidt AI in Science research fellow at the University of Oxford, where she will be developing machine learning stochastic parametrisations, with the goal of estimating model uncertainty. She has a PhD from the University of Reading, where she focused on machine learning emulators of climate models to predict the climate response changing emissions.Dr Henry Moss is an Early Career Advanced Fellow in the Institute of Computing for Climate Science within the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. His primary focus is on developing scalable Bayesian ML models that help scientists understand the world around us and he has worked on applications in engineering, biology, chemistry, and physics.Dr Ranjini Swaminathan is a Senior Research Scientist at the Department of Meteorology and the National Centre for Earth Observation at the University of Reading. She has a Ph.D. in Computer Science from the University of Arizona and has previously been a postdoctoral research scientist at the University of Auckland and Texas Tech University. Her research expertise is in AI, focused on pattern recognition for computer vision and natural language processing applications. She is a core development team member of the UK's flagship climate model, the UK Earth System Model (UKESM) and her research interests are in designing AI algorithms for climate model development and evaluation using observational data.
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