AI in Climate Science
Harness AI to Decode, Predict, and Protect Our Planet
Early access to e-LMS included
About This Course
AI in Climate Science is a future-ready, domain-specific course designed to bridge environmental science with artificial intelligence. This program introduces participants to AI models, tools, and datasets used to monitor climate change, model weather patterns, optimize resource usage, and support sustainable decision-making. Whether you are a researcher, policy analyst, engineer, or student, this course enables you to apply AI in real-world climate solutions and scientific research.
Aim
To equip learners with interdisciplinary knowledge and applied skills to use Artificial Intelligence in solving pressing challenges in climate modeling, prediction, environmental monitoring, and sustainability.
Program Objectives
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To introduce AI methodologies relevant to climate science
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To build interdisciplinary fluency between environmental and computational fields
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To foster practical skill development for real-world climate problem-solving
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To encourage ethical and data-driven climate decision-making using AI
Program Structure
Week 1: Foundations of AI and Climate Systems
Module 1: Understanding Climate Science Basics
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Chapter 1.1: Key Concepts in Climate Science (GHGs, Carbon Cycle, Feedback Loops)
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Chapter 1.2: Climate Data Sources (Remote Sensing, Ground Stations, Models)
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Chapter 1.3: Types of Climate Models (GCMs, Earth System Models)
Module 2: Introduction to AI in Scientific Contexts
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Chapter 2.1: Overview of Machine Learning and Deep Learning
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Chapter 2.2: Why AI for Climate? Use Cases and Opportunities
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Chapter 2.3: Ethical AI in Environmental Research
Week 2: Tools and Techniques for AI-Driven Climate Analysis
Module 3: Data Handling and Modeling
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Chapter 3.1: Preprocessing Climate Data (Time Series, Spatial Grids, Missing Data)
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Chapter 3.2: Predictive Modeling for Climate Trends (Temperature, Precipitation, Sea Level)
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Chapter 3.3: Remote Sensing and Image Analysis with AI
Module 4: AI for Climate Impact and Risk Assessment
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Chapter 4.1: Extreme Weather Prediction (Floods, Droughts, Heatwaves)
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Chapter 4.2: Land Use and Ecosystem Monitoring
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Chapter 4.3: Emissions Tracking and Carbon Accounting with AI
Week 3: From Research to Policy and Action
Module 5: Decision Support and Simulation
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Chapter 5.1: AI in Climate Policy Modeling and Simulation
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Chapter 5.2: Socioeconomic Scenario Modeling and Risk Communication
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Chapter 5.3: Urban Climate Adaptation Tools using AI
Module 6: Future of AI in Climate Science
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Chapter 6.1: Open Datasets and Collaborative Platforms (Copernicus, CMIP, Google Earth Engine)
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Chapter 6.2: Challenges in Scaling AI for Global Climate Modeling
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Chapter 6.3: Career Paths, Research Opportunities, and Innovations Ahead
Who Should Enrol?
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Open to students, researchers, engineers, environmental scientists, and data analysts
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Basic knowledge of Python, statistics, or environmental science is helpful
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Ideal for professionals in sustainability, urban planning, or ESG
Program Outcomes
By the end of this course, participants will:
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Understand how AI can analyze and predict climate phenomena
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Gain practical skills in working with environmental datasets
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Build AI models to support climate forecasting and sustainability efforts
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Contribute to climate resilience and action through AI tools
Fee Structure
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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