IMD’s AI-Driven Weather Forecasting Systems, Need, Significance

The India Meteorological Department (IMD) has launched two advanced Artificial Intelligence( AI) driven weather forecasting systems to provide more accurate and localized monsoon and rainfall predictions. 

These systems will help farmers, disaster management authorities, and governments take better decisions during extreme weather events and changing climate conditions.

About the Newly Launched AI-Based Forecasting Systems

The newly launched systems have been jointly developed by the India Meteorological Department, Indian Institute of Tropical Meteorology, and National Centre for Medium Range Weather Forecasting. The two major forecasting products include:

• AI-enabled “Forecast of Monsoon Advance over Different Parts of the Country. 
• High Spatial Resolution Rainfall Forecast for Uttar Pradesh.

AI-enabled “Forecast of Monsoon Advance

The AI-enabled monsoon forecasting system developed by the India Meteorological Department and Indian Institute of Tropical Meteorology aims to provide more accurate and localized information regarding monsoon progression across India.

• District-Level Monsoon Tracking: The system can now track and forecast monsoon advancement at the district level instead of only for large geographical regions.
• Forecasts up to Four Weeks in Advance: The forecasting system provides probabilistic monsoon forecasts up to four weeks ahead for better preparedness and planning.
• Regular Weekly Updates: Forecasts regarding monsoon progression will be updated every Wednesday to provide timely information.
• Dissemination of Weather Forecasts: Weather information and alerts will be shared through mobile applications, SMS alerts, WhatsApp, Kisan portals, television broadcasts, vegetable markets, local marketplaces, and other digital platforms to ensure wider public outreach and better last-mile connectivity, including regular updates for rural self-help groups.
• Coverage of Rain-Fed Regions: More than 3,196 blocks and sub-districts across 16 States and one Union Territory are being covered, especially rain-fed agricultural areas.
• Use of Artificial Intelligence and Advanced Models: The system combines AI models, extended range prediction systems, and statistical techniques to improve forecast accuracy.

The forecasts will help farmers in crop sowing and irrigation planning while also assisting governments in disaster preparedness.

High Spatial Resolution Rainfall Forecast for Uttar Pradesh

The High Spatial Resolution Rainfall Forecast developed by the National Centre for Medium Range Weather Forecasting aims to provide highly localized rainfall predictions using Artificial Intelligence. Uttar Pradesh was selected for the pilot project because of its dense network of weather observation systems.

• Rainfall Forecast at 1-km Resolution: The system can provide rainfall forecasts for every 1-km geographical area, making the predictions highly localized and precise.
• Forecasts up to Ten Days in Advance: The forecasting model can generate rainfall predictions up to ten days ahead for better planning and preparedness.
• AI-Based Downscaling Technology: The system uses advanced AI-based downscaling techniques to convert broader weather data into local rainfall forecasts.
• Integration of Multiple Weather Data Sources: The forecasting model combines data from Automatic Rain Gauges, Automatic Weather Stations, Doppler Weather Radars, and satellites to improve forecasting quality.

The system will support farmers, water resource managers, and disaster management agencies through more accurate rainfall predictions.

Need for AI-Driven Weather Forecasting in India

Increasing climate variability, frequent extreme weather events, and India’s large dependence on monsoon-based agriculture have made accurate, timely, and highly localized weather forecasting essential for economic stability, disaster preparedness, and public safety.

• Increasing Extreme Weather Events: India is witnessing more frequent floods, heatwaves, cyclones, cloudbursts, droughts, and irregular rainfall patterns due to climate change, creating the need for more advanced and accurate forecasting systems.
• Heavy Dependence of Agriculture on Rainfall: Since a large section of Indian agriculture is rain-fed, farmers require timely weather forecasts for crop sowing, irrigation, fertilizer application, crop protection, and harvesting activities.
• Need for Hyper-Local Weather Information: Weather conditions often vary significantly within short distances, making district-level and village-level forecasts necessary for effective agricultural and administrative decision-making.
• Strengthening Disaster Preparedness and Early Warning Systems: Accurate weather forecasting helps governments and disaster management agencies issue timely warnings and reduce the loss of life and property during floods, cyclones, landslides, and extreme rainfall events.
• Improved Water Resource Management: Reliable rainfall forecasts help in reservoir operations, irrigation planning, groundwater management, urban drainage planning, and flood control activities.
• Growing Impact of Climate Change: Rapidly changing climate patterns have increased uncertainty in weather behaviour, making AI-based systems important for improving forecasting accuracy and climate resilience.
• Need for Faster and More Efficient Data Analysis: Traditional forecasting models often face limitations in processing massive weather datasets quickly, whereas Artificial Intelligence can analyse satellite data, radar observations, and real-time weather information more efficiently.
• Demand for Impact-Based Forecasting: Modern governance requires weather forecasts that not only predict weather conditions but also provide actionable information for farmers, administrators, disaster managers, and citizens.

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IMD’s AI-Driven Forecasting Systems Significance 

• Improvement in Agricultural Planning: Farmers can now take more informed decisions regarding crop selection, sowing schedules, irrigation management, fertilizer application, crop protection measures, and harvesting activities with improved local precision.
• Strengthening Disaster Management Capacity: Hyper-local weather forecasts improve preparedness for floods, cyclones, landslides, urban flooding, and extreme rainfall events by enabling timely warnings and coordinated administrative action.
• Better Water Resource Management: Accurate rainfall prediction supports reservoir operations, groundwater management, irrigation scheduling, flood control planning, and efficient utilization of water resources.
• Transition Towards Impact-Based Forecasting: The initiative marks a major shift from conventional weather forecasting towards impact-based forecasting that provides actionable and decision-support information for citizens and governance institutions.
• Contribution to Climate Resilience: AI-enabled forecasting systems strengthen India’s climate resilience by improving adaptive capacity and preparedness against increasing climate uncertainties and weather-related disasters.

AI-Driven Weather Forecasting Challenges 

Despite significant advancements in Artificial Intelligence-based weather forecasting, several technological, infrastructural, and operational challenges continue to affect the accuracy, accessibility, and effective implementation of these forecasting systems in India.

• Dependence on High-Quality Data: AI forecasting systems require massive volumes of accurate and real-time weather data, and poor-quality observational inputs can reduce forecasting reliability.
• Uneven Observational Infrastructure Across India: Several regions in India still lack dense radar networks, weather stations, and rain gauge infrastructure necessary for highly localized forecasting.
• High Computational and Financial Requirements: AI-driven forecasting systems require advanced computing infrastructure, continuous data integration, skilled manpower, and significant financial investment.
• Last-Mile Dissemination Challenges: Weather advisories often fail to effectively reach remote farmers, vulnerable populations, and local stakeholders due to communication and awareness gaps.
• Uncertainty in Weather Systems: Weather systems are naturally dynamic and uncertain, so forecasting errors cannot be completely avoided even with advanced technology.

Government Initiatives for Strengthening Weather Forecasting in India

The Government of India has undertaken several initiatives to modernize weather forecasting infrastructure, improve forecast accuracy, and strengthen disaster preparedness through the use of advanced technology, Artificial Intelligence, and digital communication systems. As a result of these efforts, India’s weather forecasting capacity has improved significantly during the last decade.

• According to the Ministry of Earth Sciences, India has achieved nearly 40 per cent improvement in forecasting accuracy for severe weather events during the recent decade. 
• Similarly, cyclone track, intensity, and landfall forecasting accuracy for 72-hour predictions has improved by nearly 30-35%. 
• Seasonal forecasting errors have reduced significantly due to better observational systems, advanced numerical models, and greater use of data analytics.

Major Government Initiatives

• Mission Mausam: Mission Mausam aims to modernize India’s weather forecasting system through expansion of radar networks, stronger observation systems, advanced forecasting models, and better computing infrastructure.
• Expansion of Doppler Weather Radars: The government has increased the number of Doppler Weather Radars to improve real-time monitoring of cyclones, rainfall, thunderstorms, and extreme weather events.
• Strengthening Observational Infrastructure: India has expanded Automatic Weather Stations, rain gauges, satellite systems, and digital observation networks to improve weather data collection.
• Use of AI and Advanced Forecasting Models: Institutions such as the India Meteorological Department, Indian Institute of Tropical Meteorology, and National Centre for Medium Range Weather Forecasting are using AI-based forecasting models and data analytics to improve forecast accuracy.
• Development of Hyper-Local Forecasting Systems: The government has introduced district-level monsoon forecasting and 1-km resolution rainfall forecasting systems for more localized weather information.
• Digital Dissemination of Weather Advisories: Weather forecasts and warnings are being shared through mobile applications, SMS alerts, WhatsApp, Kisan portals, television broadcasts, and other digital platforms for wider public outreach.
• Integration with Agricultural Advisory Services: Weather forecasting systems are being linked with agricultural advisory services to support farmers in crop planning and irrigation management.
• Shift Towards Impact-Based Forecasting: The government is focusing on impact-based forecasting that provides actionable weather information for farmers, administrators, disaster managers, and citizens.