Core Idea
- AI and ML are increasingly being explored to improve weather forecasting in India, especially for extreme events like heatwaves, floods, and torrential rainfall.
- Traditional models rely on physics equations; AI/ML models start with data and learn patterns without explicit programming.
- Two major challenges: data availability and shortage of interdisciplinary human resources.
Relevance : GS 3(Technology and Disaster Management)
How AI/ML Differs from Traditional Weather Models
- Traditional Models: Use physical laws (fluid dynamics, thermodynamics); require supercomputers.
- AI/ML Models: Learn relationships directly from large datasets; can uncover hidden links and non-linear patterns.
Recent Indian Efforts
- ‘Mission Mausam’ (Sep 2024): ₹2,000 crore allocation to improve AI-based forecasting tools.
- AI/ML Centre (Ministry of Earth Sciences): Focus on short-range rainfall forecasting, urban weather datasets, and nowcasting using Doppler radar data.
- Research Initiatives: IIT-Delhi and IIIT-Delhi ML model predicted monsoon rainfall with 61.9% success rate (2002–2022), better than traditional models.
Major Challenges
Data Limitations
- Requires high-resolution, high-quality datasets (often inconsistent due to sensor errors).
- Remote areas lack adequate sensor coverage, affecting model accuracy.
- Disagreement: Some believe India now has sufficient data (10x increase); others say quality/standardisation is still lacking.
Human Resource Gap
- Lack of experts fluent in both AI/ML and climate science.
- Climate science straddles multiple disciplines, making it hard to build integrated expertise.
- Need for collaborative institutions focused exclusively on AI-Climate research.
Interpretability & Trust Issues
- AI models are often black boxes — difficult to understand how/why they made a forecast.
- Traditional models offer transparency via physics equations and error correction methods.
- Calls for hybrid models combining AI/ML with physics-based approaches.
Global Perspective
- 2024 Heidelberg Forum: ML has succeeded in weather forecasting, but climate science remains challenging due to long-term unpredictability and atmospheric chaos.
- Future climate models need to generalize to a “warmer world” — hard for ML trained on present data.
AI/ML for Extreme Events
- AI holds promise in predicting extreme weather: heatwaves, cyclones, cloudbursts.
- February 2025 Nature Communications paper highlights AI’s role in risk communication, attribution studies.
- But warns of trustworthiness, interpretability, and uncertainty quantification concerns.
Way Forward
- Develop region-specific models for India’s diverse geography.
- Promote interdisciplinary research and AI-literacy among climate scientists.
- Need for critical mass of trained professionals and improved data accessibility.
- Government initiatives must focus on institutional collaboration, data infrastructure, and hybrid model development.
