Weather and Climate: Applications of Machine Learning and Artificial Intelligence, Volume 13 provides a comprehensive exploration of machine learning in the context of weather forecasting and climate research. Sections begin with an introduction to the fundamentals and statistical tools of machine learning and 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 climate research. The book explores the interface of data assimilation and machine learning for weather forecasting, showcasing case studies of machine learning applied to environmental monitoring data. Final sections look 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 modeling - 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 Provides critical understanding of machine learning in weather and climate science, from statistical tools to building models 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. 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 Cambridge 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 researche