AI in Climate Science
Machine Learning for Environmental Modeling and Prediction
A comprehensive guide to applying artificial intelligence to understanding and predicting Earth's climate system
About the Book
This comprehensive volume bridges the critical knowledge gap between AI/ML communities and climate scientists, spanning from fundamental data processing to advanced predictive modeling with real-world applications.
15 Comprehensive Chapters
From foundational concepts to cutting-edge applications, covering atmospheric systems, ocean dynamics, terrestrial ecosystems, and societal impacts.
Physics-Informed AI
Learn to integrate physical constraints with data-driven learning for reliable climate predictions that respect conservation laws.
Practical Implementation
Code examples, case studies, and best practices for applying machine learning to real climate challenges.
Expert Insights
Written by leading researchers combining expertise in AI, climate science, and environmental modeling.
Multi-Scale Modeling
Address phenomena spanning turbulent eddies to planetary waves, sub-daily weather to multi-decadal climate variability.
Uncertainty Quantification
Master Bayesian methods, ensemble techniques, and uncertainty estimation for high-stakes climate applications.
Authors
Prof. (Dr.) Sandeep Gupta
Professor (Research)
Department of Artificial Intelligence and Data Science
Poornima Institute of Engineering & Technology, Jaipur, India
Distinguished researcher in AI and nanotechnology with expertise in environmental sensing, hydrogen energy systems, and advanced sensor technologies.
ORCID: 0000-0002-2046-115X
Prof. (Dr.) Budesh Kanwer
Professor
Department of Artificial Intelligence and Data Science
Poornima Institute of Engineering & Technology, Jaipur, India
Prominent researcher specializing in MANETs, AI, ML, Cloud Computing, and IoT with significant contributions to biomedical signal processing.
ORCID: 0009-0001-6545-0497
Prof. (Dr.) Badrul Hisham Ahmad
Professor
Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer
Universiti Teknikal Malaysia Melaka
Leading researcher in RF and Microwave Engineering with over 5,500 citations and extensive interdisciplinary research experience.
ORCID: 0000-0001-8014-2444
Book Contents
Part I: Foundations and Methodologies
- Chapter 1: Introduction to AI in Climate Science
- Chapter 2: Climate Data Challenges and Preprocessing
- Chapter 3: Machine Learning Fundamentals for Climate Applications
Part II: Atmospheric and Weather Systems
- Chapter 4: AI-Enhanced Weather Prediction
- Chapter 5: Extreme Weather Event Prediction
- Chapter 6: Atmospheric Composition and Air Quality
Part III: Ocean, Cryosphere, and Terrestrial Systems
- Chapter 7: Ocean Modeling with AI
- Chapter 8: Cryosphere Monitoring and Prediction
- Chapter 9: Vegetation and Ecosystem Dynamics
- Chapter 10: Carbon Cycle and Greenhouse Gas Modeling
Part IV: Climate Impacts and Applications
- Chapter 11: Renewable Energy and Climate Variability
- Chapter 12: Agricultural and Food Security Applications
- Chapter 13: Climate Risk Assessment and Adaptation
- Chapter 14: Regional and Seasonal Climate Prediction
- Chapter 15: Emerging Technologies and Future Directions
Resources
Contact
Have questions or feedback? We'd love to hear from you.
Email: contact@climate-ai-book.org
Publisher: Bentham Science Publishers
Publication Year: 2025