AI in Climate Science

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Table of Contents

Mentor Based

Program Syllabus

Module 1

About

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.

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Module 2

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.

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Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.

Limited SeatsClosing Soon

AI in Climate Science

Professional Certification Program

🎥

FormatLive + Recorded

📅

Duration8 Weeks

📜

CertificationVerified

Enroll Now

Module 3

Program Objectives

To introduce AI methodologies relevant to climate science
To build interdisciplinary fluency between environmental and computational fields
To foster practical skill development for real-world climate problem-solving
To encourage ethical and data-driven climate decision-making using AI

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Module 4

Program Structure

Module 5

Module 1: Understanding Climate Science Basics

Chapter 1.1: Key Concepts in Climate Science (GHGs, Carbon Cycle, Feedback Loops)
Chapter 1.2: Climate Data Sources (Remote Sensing, Ground Stations, Models)
Chapter 1.3: Types of Climate Models (GCMs, Earth System Models)

Module 6

Module 2: Introduction to AI in Scientific Contexts

Chapter 2.1: Overview of Machine Learning and Deep Learning
Chapter 2.2: Why AI for Climate? Use Cases and Opportunities
Chapter 2.3: Ethical AI in Environmental Research

Module 7

Module 3: Data Handling and Modeling

Chapter 3.1: Preprocessing Climate Data (Time Series, Spatial Grids, Missing Data)
Chapter 3.2: Predictive Modeling for Climate Trends (Temperature, Precipitation, Sea Level)
Chapter 3.3: Remote Sensing and Image Analysis with AI

Module 8

Module 4: AI for Climate Impact and Risk Assessment

Chapter 4.1: Extreme Weather Prediction (Floods, Droughts, Heatwaves)
Chapter 4.2: Land Use and Ecosystem Monitoring
Chapter 4.3: Emissions Tracking and Carbon Accounting with AI

Module 9

Module 5: Decision Support and Simulation

Chapter 5.1: AI in Climate Policy Modeling and Simulation
Chapter 5.2: Socioeconomic Scenario Modeling and Risk Communication
Chapter 5.3: Urban Climate Adaptation Tools using AI

Module 10

Module 6: Future of AI in Climate Science

Chapter 6.1: Open Datasets and Collaborative Platforms (Copernicus, CMIP, Google Earth Engine)
Chapter 6.2: Challenges in Scaling AI for Global Climate Modeling
Chapter 6.3: Career Paths, Research Opportunities, and Innovations Ahead

Module 11

Video Module TOC

~Video content aligned with weekly modules

Module 12

Week 1 Videos

Introduction to Climate Science for AI Learners
Climate Systems, GHGs, and Feedback Loops Explained
Overview of Global Climate Models (GCMs) and Earth System Models
AI 101 for Environmental Research
Climate Data Types and Sources (Remote Sensing, Ground, Simulations)
Why Use AI in Climate Science? Key Opportunities
Ethical and Responsible Use of AI in Climate Work
Case Examples: Early AI Models in Climate Research
Week 1 Recap and Knowledge Check

Module 13

Week 2 Videos

Climate Data Wrangling: Temporal and Spatial Formats
Using ML for Climate Forecasting (Time Series + Regression Models)
AI for Satellite Image Analysis: Deforestation & Ice Melt
Deep Learning for Extreme Weather Pattern Detection
AI in Monitoring CO₂, Methane, and Emission Sources
Tools Demo: Google Earth Engine, Climate Net, and Copernicus APIs
Risk Mapping and Impact Models (Floods, Wildfires, Sea-Level Rise)
Week 2 Project Brief: Build a Basic AI Climate Insight App

Module 14

Week 3 Videos

AI for Climate Policy Modeling and Scenario Simulations
Using AI in Socioeconomic + Environmental Policy Tools
AI Tools for Urban Adaptation and Resilience Planning
Collaborative Platforms and Datasets: CMIP, Open Climate, UNData
Career and Research Roles in AI for Climate
Capstone Project Guidance and Toolset Overview
Expert Panel: Future of Climate Modeling with AI
Final Reflections and Takeaways

Module 15

Live Lecture Module

Live sessions combining scientific expertise, AI application, and real-world impact

Module 16

Lecture 1 (Week 1)

Title: Bridging Climate Science and AI: Foundations for a New Era
Duration: 60 minutes
Focus: Exploring the fundamentals of climate systems and how AI fits into environmental research
Guest: Climate Data Scientist or Earth System Modeler
Interactive: Live climate model walkthrough + audience Q&A on data sources

Module 17

Lecture 2 (Week 2)

Title: AI in Action: Detecting, Predicting, and Responding to Climate Events
Duration: 75 minutes
Focus: Practical applications of AI in weather forecasting, emissions tracking, and impact modeling
Guest: Remote Sensing Specialist or AI Environmental Engineer
Interactive: Hands-on image classification demo + mini climate risk mapping exercise

Module 18

Lecture 3 (Week 3)

Title: From Research to Policy: How AI Drives Climate Decisions
Duration: 90 minutes
Focus: Applying AI for sustainable planning, urban resilience, and international climate goals
Guest Panel: Environmental Policy Expert + Climate Tech Innovator + NGO Analyst
Interactive: Capstone previews + live discussion on AI ethics in environmental governance

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Module 19

Intended For

Open to students, researchers, engineers, environmental scientists, and data analysts
Basic knowledge of Python, statistics, or environmental science is helpful
Ideal for professionals in sustainability, urban planning, or ESG

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Module 20

Program Outcomes

By the end of this course, participants will:

Understand how AI can analyze and predict climate phenomena
Gain practical skills in working with environmental datasets
Build AI models to support climate forecasting and sustainability efforts
Contribute to climate resilience and action through AI tools

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Module 21

Mentors

AI Mentor

MOSES BOFAH

Ghana Telecom

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AI Mentor

Sanjay Bhargava

Ignite Consulting

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AI Mentor

Bede Adazie

Alx University

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Module 22

Fee Structure

Fee: INR 21499 USD 249

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

List of Currencies

Module 23

FOR QUERIES, FEEDBACK OR ASSISTANCE

Module 24

Key Takeaways

Access to e-LMS
Real Time Project for Dissertation
Project Guidance
Paper Publication Opportunity
Self Assessment
Final Examination
e-Certification
e-Marksheet

Module 25

Future Career Prospects

Graduates of this course will be ready to pursue roles in:

Climate Tech and Environmental AI Startups
Government or NGO Climate Data Units
AI-Driven Environmental Consulting
Research Institutions working on Climate Informatics

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Module 26

Job Opportunities

Climate Data Scientist
AI Researcher – Environmental Modeling
Remote Sensing Specialist (AI tools)
Sustainability Analyst (AI applications)
Policy Advisor (AI for climate)
Environmental Risk Modeler

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Module 27

Country

Affiliation

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Module 28