The Ministry of Earth Sciences has launched two AI-enabled weather forecasting systems delivering hyper-local, impact-based decision-support services nationwide.
About AI-Enabled Weather Forecasting Systems
- Meaning: AI-enabled weather forecasting uses Artificial Intelligence (AI), Big Data Analytics, and Numerical Weather Prediction (NWP) for accurate, hyper-local weather predictions.
- Key Feature: AI continuously learns from historical and real-time data, improving forecasts of localized extreme weather events.
Need for AI-enabled Weather Forecasting in India
- Monsoon Uncertainty: Nearly 75% of India’s annual rainfall occurs during the monsoon, requiring accurate AI-based forecasting.
- Rising Extreme Events: Increasing floods, cyclones, heatwaves, and cloudbursts demand AI-powered forecasting for timely disaster preparedness.
- Rapid Urbanization: Cities like Mumbai and Bengaluru need hyper-local forecasts for flooding and Urban Heat Island management.
- Early Warning Systems: AI strengthens early warnings, reducing disaster risks and improving timely evacuation and emergency response.
Significance of AI-enabled Weather Forecasting
- Agricultural Resilience: Enables precision farming, benefiting 52% of rainfed agriculture through accurate weather-based crop advisories.
- Disaster Preparedness: Improves forecasts for floods, cyclones and heatwaves, supporting NDMA’s Impact-Based Forecasting framework.
- Water Security: Optimizes reservoir operations and irrigation planning, strengthening schemes like PMKSY and hydropower management.
- Urban Resilience: Predicts urban floods and heat islands, aiding Smart Cities Mission and climate-resilient infrastructure.
- Climate Governance: Strengthens evidence-based decisions under Mission Mausam, enhancing India’s climate resilience and public service delivery.
Key Government Initiatives for AI-enabled Weather Forecasting
- Mission Mausam: Expanding 100 Doppler Weather Radars, modernizing forecasting infrastructure and strengthening nationwide early warning systems.
- AI-based Forecasting: IMD launched AI-enabled monsoon forecasting for 16 States and 3,000+ sub-districts in 2026.
- Hyper-local Services: Introduced 1-km rainfall forecasting for Uttar Pradesh, with phased expansion to other States.
- Impact-Based Forecasting: IMD’s Impact-Based Forecasting (IBF) and Common Alerting Protocol (CAP) provide actionable disaster warnings.
- Digital Dissemination: Mausam App, SMS, WhatsApp, Kisan Portal and TV ensure last-mile delivery of weather advisories
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Challenges in AI-enabled Weather Forecasting
- Data Deficit: Sparse Himalayan & Northeast weather stations reduce hyper-local forecasting accuracy.
- Infrastructure Gap: India has 50 Doppler Radars; Mission Mausam will add 50 more.
- AI Transparency: Black-box AI models require continuous validation using IMD’s observational and satellite datasets.
- Last-mile Disconnect: Around 52% of India’s net sown area is rainfed, yet many farmers lack timely digital advisories.
- Institutional Fragmentation: Coordination gaps among IMD, NDMA, State governments and local agencies affect effective forecast utilization.
Way Forward
- Radar Revolution: Deploy 50 additional Doppler Weather Radars under Mission Mausam for nationwide high-resolution forecasting.
- Smart Observation: Integrate INSAT satellites, AWSs, ARGs, Doppler Radars and IoT sensors for real-time weather intelligence.
- Trustworthy AI: Develop explainable AI models using IMD, IITM Pune, & NCMRWF datasets for reliable forecasts.
- Integrated Governance: Link forecasts with PM Fasal Bima Yojana, Digital Agriculture Mission, and NDMA’s Impact-Based Forecasting.
- Forecast for All: Expand multilingual advisories through Mausam App, SMS, WhatsApp, and Kisan portals to ensure last-mile outreach.
“From Forecast to Foresight,” AI-enabled weather forecasting strengthens climate resilience, disaster preparedness, and sustainable development nationwide.
Reference: PIB
PMF IAS Pathfinder for Mains – Question 772
Approach
- Introduction: Write a contextual introduction about AI-Enabled weather forecasting systems.
- Body: Write the significance of AI-enabled weather forecasting systems for agriculture and disaster management in India, also mention challenges and suggest measures to strengthen AI-enabled weather forecasting.
- Conclusion: Emphasise the 3P (Predict, Prepare, Protect) approach through AI-enabled forecasting, robust early warning systems, and climate-resilient decision-making.
