AI can supercharge forecasting if it can weather some challenges, Pg20

Key Highlights:

• AI/ML models are being used in India to improve forecasting of heatwaves, heavy rainfall, and floods.
• Traditional models rely on physics-based simulations, while AI models learn from data to predict weather outcomes.
• Two major challenges: availability of clean and large datasets and shortage of domain-skilled professionals.
• The ‘Mausam Mission’ and the Centre for Excellence in AI at IITM are key steps to modernise weather prediction.
• AI can help generate faster, region-specific, and computationally lighter forecasts, aiding disaster preparedness.

Background/Context

• India’s weather has grown increasingly erratic with intense heatwaves, unseasonal rainfall, and monsoon unpredictability.
• Current weather prediction depends on numerical models that simulate atmospheric dynamics, but these are computationally heavy and slow.

Key Developments

• The Ministry of Earth Sciences and institutions like IIT Delhi, IITM Pune, and ISRO are investing in AI to boost forecast speed and accuracy.
• AI tools like machine learning models and data assimilation systems can digest high-resolution data (e.g. Doppler radar, satellite imagery) to predict short-term extreme events.
• AI’s strength lies in identifying non-linear patterns quickly, offering nowcasting advantages.

Strategic/Policy/Legal/Economic Implications

• AI-driven models can aid in early warnings, disaster preparedness, and agriculture planning.
• Helps address sectoral needs, such as urban flooding mitigation, crop insurance, and infrastructure resilience.
• Reduced computational costs can make weather forecasting more accessible to state agencies and local planners.
• Encourages a paradigm shift in India’s climate risk management, especially amid growing climate vulnerabilities.

India's Stand or Way Forward

• Policy thrusts like Mausam Mission 2.0 are geared toward integrating AI into climate science.
• Collaborative efforts between climate scientists and computer engineers are essential to bridge domain gaps.
• Creation of exclusive research units focused on AI weather modeling is crucial.
• Government should incentivise AI training in environmental sciences and fund interdisciplinary research.

Challenges Ahead

• Scarcity of high-quality, labelled climate data impedes model training.
• Lack of cross-trained scientists in both meteorology and AI slows down deployment.
• Overfitting risks and black-box nature of ML models raise transparency concerns.
• Scaling up AI models without adequate climate context may lead to errors in long-term projections.

Mains Mock Question:

“Artificial Intelligence is increasingly being applied in environmental forecasting. Discuss its role in improving extreme weather prediction and the challenges associated with its implementation in India.”