AI in Weather Forecasting

Syllabus: Science and Technology

Source:  TH

Context: India is leveraging AI/ML under Mission Mausam (₹2,000 crore outlay) to boost weather prediction accuracy, especially for extreme events like heatwaves and cloudbursts.

• IIT-Delhi and IIIT-Delhi teams have developed AI-based monsoon models outperforming traditional systems.

How AI Can Assist in Weather Forecasting

• Data-Driven Predictions: AI learns complex patterns from past data to predict rainfall, cyclones, or heatwaves, unlike physics-based models which rely on fixed equations.

E.g. IIT Delhi’s ML model for monsoon showed 61.9% accuracy (2002–2022) surpassing traditional models.

• Faster, Scalable Forecasts: AI models can produce short-term forecasts quickly and at lower computational cost, ideal for nowcasting and real-time alerts.
• Better Prediction of Extremes: AI helps capture nonlinear interactions among variables—useful in predicting rare and sudden weather events like flash floods or tornadoes.
• Hybrid Modelling: Combines strengths of physics-based models and AI tools to improve reliability and interpretability of forecasts.

Challenges in AI-based Weather Forecasting

• Data Scarcity and Quality Issues: High-resolution, clean, and long-term weather datasets are essential. Historical data may be sparse or inconsistent.

E.g. Many Indian weather datasets face gaps due to poor sensor coverage in remote regions.

• Lack of Interdisciplinary Talent: Climate scientists may lack AI/ML expertise, while ML engineers often lack meteorological grounding, limiting deep collaborations.
• Black Box Nature: AI models lack transparency, making it hard to explain their outputs to policymakers or meteorologists.
• Infrastructural Constraints: Most forecasters rely on model outputs from external agencies due to lack of local computational or technical capability.
• Trust and Verification Issues: Model predictions need rigorous validation; without this, false alarms or missed warnings can reduce public trust.

Way Ahead:

• Establish Hybrid Weather Institutes: Create dedicated centres integrating meteorology and AI under one roof for seamless collaboration.

E.g. Ministry-supported AI-Climate Centre at IITM Pune already operational.

• Enhance Data Systems: Standardise and integrate real-time, historical data from Doppler radars, satellites, and ground sensors.
• Capacity Building: Train a new cadre of meteorologists fluent in AI/ML and engineers trained in earth system science.
• Model Customisation: Develop AI models tailored to India’s diverse climatic zones and terrain for hyperlocal forecasts.
• Public-Private Collaboration: Engage startups, academia, and government institutions to co-develop and deploy verified AI models.

Conclusion:

AI has the potential to revolutionise India’s weather forecasting, especially for managing extreme events. However, it requires a blend of robust data, skilled manpower, and institutional innovation. With strategic collaboration, AI can become central to India’s climate resilience planning.

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