Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Climate Modeling, Extreme Events & Policy Scenario Analysis shows how machine learning augments the science of understanding and preparing for a changing climate. You learn to work with climate and Earth-observation data, build models that forecast climate variables and predict extreme events like floods and heatwaves, and analyse the outcomes of different policy and emissions scenarios. The course emphasises uncertainty and the careful interpretation climate decisions demand. You finish able to apply AI to a climate-modelling or scenario-analysis question. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to climate modeling — forecasting climate variables, predicting extreme events and analysing policy scenarios to support climate decisions.
📋 Course Objectives
1. Work with climate and Earth-observation data.
2. Forecast key climate variables.
3. Predict extreme events like floods and heatwaves.
4. Analyse policy and emissions scenarios.
5. Quantify and communicate uncertainty.
👥 Who Should Enroll?
• Climate scientists and modellers
• Environmental and policy analysts
• Data scientists in climate
• Students of climate science
🚀 Key Learning Outcomes
• The ability to apply AI to climate modelling.
• A scenario-analysis project.
• An uncertainty-aware climate perspective.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Module 1 — Foundations of Climate Science
Climate systems, drivers, and feedback mechanisms • Weather vs. climate: timescales and variability • Climate change, trends, and uncertainty • Introduction to climate risks and extreme events
Module 2 — Climate Data & Environmental Datasets
Observational, satellite, and reanalysis datasets • Climate model outputs and scenario datasets • Geospatial and temporal data structures • Data preprocessing, cleaning, and quality assessment
Module 3 — Introduction to AI for Climate Analytics
AI and machine learning concepts for environmental systems • Supervised and unsupervised learning approaches • Feature engineering for climate variables • Dimensionality reduction and data preparation workflows
Module 4 — Modeling Extreme Events
Heatwaves, floods, droughts, storms, and climate hazards • Event detection and classification methods • Forecasting and anomaly identification • AI-based pattern recognition in extreme-event analysis
Module 5 — Scenario Analysis & Forecasting
Climate scenarios and emissions pathways • Forecasting future environmental conditions • Sensitivity analysis and uncertainty interpretation • Comparing scenario outcomes under different assumptions
Module 6 — Policy Scenario Analysis
Linking climate outputs to policy and planning questions • Adaptation and mitigation strategy evaluation • Climate-risk interpretation for infrastructure and communities • Decision-support frameworks for environmental governance
Module 7 — Tools, Workflows & Reproducibility
Python and R workflows for climate analysis • Data visualization, dashboards, and impact communication • Reproducible computational practices • Workflow design for research, consulting, and institutional use
Module 8 — Applied Projects & Case Studies
Extreme-event prediction case studies • Regional climate-risk assessment exercises • Policy-oriented scenario simulation projects • Final reproducible workflow for climate and policy analysis
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python libraries: pandas, NumPy, xarray, scikit-learn, TensorFlow/Keras, PyTorch |
| Covered Tool / Platform | R packages: tidyverse, caret, terra, raster, sf, forecasting and environmental modeling packages |
| Covered Tool / Platform | Climate datasets: satellite observations, reanalysis products, climate model outputs, emissions and impact datasets |
| Covered Tool / Platform | Geospatial tools: GIS-compatible workflows, spatial analysis libraries, raster and vector data handling |
| Covered Tool / Platform | AI techniques: regression, classification, clustering, anomaly detection, time-series forecasting, neural networks |
| Covered Tool / Platform | Visualization and reporting tools: dashboards, maps, policy reporting visuals, statistical graphics |
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