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Full Description
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.
Contents
1. Introduction to Machine Learning - Fundamentals and Statistical Tools
2. Introduction to Machine Learning Models
3. Emulation and machine learning of sub-grid scale parametrisations
4. AI/ML in weather forecasting and climate models
5. XAI - explainable AI methods for understanding ML and AI models
6. Generative AI in weather and climate research
7. The interface of Data Assimilation and Machine Learning for Weather Forecasting
8. Case studies of Machine Learning applied to Environmental Monitoring Data
9. Future of ML and AI in climate and weather-related research
10. References/Further Reading
