India Unveils AI-Powered Monsoon & Rainfall Forecasts for Hyper-Local, Impact-Based Weather Services

• AI-enabled Systems Introduced by IMD to Provide Hyper-local Weather Forecasts. 
• Advanced Forecast Systems to Provide Localised Weather Information Up to 10 Days in Advance. 
• Government Introduces AI-enabled Monsoon Forecasting Platform for 16 States and Over 3,000 Sub-districts
• Union Minister Dr Jitendra Singh Launches AI-based Monsoon Advance Forecast System and 1-km Resolution Rainfall Forecast for Uttar Pradesh
• Dr Jitendra Singh Says IMD Has Become an Essential Part of India’s Everyday Governance and Public Decision-making

India has launched two landmark AI-enabled weather forecasting systems—an AI-driven monsoon advance forecast and a high-resolution rainfall model for Uttar Pradesh—marking a decisive shift towards hyper-local, impact-based climate services designed to aid farmers, disaster managers, and policymakers.

The systems have been developed jointly by the India Meteorological Department (IMD), Indian Institute of Tropical Meteorology (IITM), Pune, and National Centre for Medium Range Weather Forecasting (NCMRWF).

The two systems are –

1. AI-enabled Monsoon Advance Forecast
   • Provides probabilistic forecasts every Wednesday up to 4 weeks in advance.
   • Covers 16 states and 3,000+ sub-districts.
   • Designed to support farmers’ sowing, irrigation, crop planning decisions.
2. High Spatial Resolution Rainfall Forecast (Pilot in Uttar Pradesh)
   • Generates 1-km resolution rainfall forecasts up to 10 days ahead.
   • Uses AI-driven downscaling techniques integrating data from Doppler radars, AWS, ARGs, and satellites.
   • Expected to expand to other states as infrastructure grows.

Benefits Across Sectors

• Agriculture: Farmers gain precise, localized forecasts for sowing, irrigation, crop protection, and harvest planning.
• Disaster Management: Improved early warnings for floods, cyclones, and extreme rainfall events.
• Urban Planning: Helps cities prepare for drainage, infrastructure resilience, and water resource management.
• Renewable Energy: Supports solar and wind energy forecasting, stabilizing grid operations.

Technological Advancements

• Expansion of Doppler Radars: From 16–17 a decade ago to ~50 today, with another 50 planned under Mission Mausam.
• Forecast Accuracy Gains: Severe weather forecast accuracy improved by 40% in the past decade; cyclone track predictions improved by 30–35% in the last five years.
• Digital Dissemination: Forecasts shared via mobile apps, SMS, WhatsApp, Kisan portals, TV, and radio for last-mile connectivity.

Comparison of New Systems

System	Coverage	Forecast Horizon	Resolution	Primary Use
AI Monsoon Advance Forecast	16 states, 3,000+ sub-districts	Up to 4 weeks	District/block level	Agriculture planning, disaster preparedness
Rainfall Forecast (UP Pilot)	Uttar Pradesh (pilot)	Up to 10 days	1 km	Hyper-local rainfall prediction, urban planning

Strategic Importance

• Strengthens climate resilience and citizen-centric governance.
• Aligns with PM Modi’s modernization drive under Mission Mausam.
• Positions IMD as a decision-support system for governance, agriculture, and infrastructure.

The monsoon advance forecasting system would now provide granular forecasts on monsoon progression at district-level scales, while the Uttar Pradesh pilot project demonstrates the capability of generating operational rainfall forecasts at 1-km resolution using dense observational networks and AI techniques.

Secretary, Ministry of Earth Sciences, Dr. M. Ravichandran said that similar services would gradually be expanded to other parts of the country as observational infrastructure continues to grow.

Dr. Jitendra Singh said the newly launched forecasting products represent another important step towards building a climate-resilient, digitally empowered and citizen-centric weather service system for the country, where scientific advancements directly contribute to societal and economic benefits.

Mission Mausam: Making Bharat Weather-Ready and Climate-Smart

India’s tropical weather is notoriously complex, with sudden cloudbursts, lightning storms, and localized droughts often catching communities off guard. As climate change intensifies, the challenge of predicting these events has grown sharper. To address this, the Ministry of Earth Sciences has launched Mission Mausam, a ₹2,000 crore national initiative approved by the Union Cabinet to transform India’s weather and climate forecasting capabilities between 2024–2026.

Why Mission Mausam Matters

• Forecast accuracy has improved 40–50% in the past decade.
• Current models operate at 12 km resolution, limiting small-scale forecasts.
• Mission Mausam will reduce resolution to 6 km for Panchayat-level predictions.
• Goal: Make Bharat a Weather-ready and Climate-smart nation.

Key Objectives

• Deploy next-generation radars and satellites.
• Establish high-performance computing (HPC) systems.
• Use AI/ML tools to enhance prediction accuracy.
• Develop earth system models for atmosphere, ocean, and polar regions.
• Create last-mile dissemination systems via apps, websites, and social media.
• Foster academia-industry partnerships and incubation centres.

Strategy in Action

Initiative	Details
Weather Infrastructure	50 Doppler Radars, 60 RS/RW, 100 disdrometers, 25 radiometers
Testbeds	Urban and process testbeds for cloud and land studies
Forecasting	AI-driven nowcast systems with hourly updates
Weather Modification	Cloud seeding experiments using drones and aircraft
Air Quality	New monitoring instruments for smart cities
Decision Support	Automated DSS for disaster management authorities

Who Benefits

• Farmers: Precise rainfall forecasts for crop planning.
• Civil aviation and transport: Improved storm tracking.
• Smart cities and health services: Enhanced air quality predictions.
• Defence and disaster management: Real-time decision support.
• Energy, shipping, tourism, and urban planning sectors gain resilience.

Implementation Network

• IMD: Observations, services, dissemination.
• IITM: Field campaigns, modeling, testbeds.
• NCMRWF: Data assimilation and seamless prediction.
• Support: INCOIS, NIOT, NCPOR, CWC, GSI, DGRE, academia, industry.

Expected Outcomes

• No weather system goes undetected.
• Hourly nowcasts instead of every 3 hours.
• Forecast accuracy improved by 5–10%.
• Panchayat-level forecasts at 5–6 km resolution.
• Enhanced air quality forecasts for smart cities.
• Weather interventions: fog dispersal, hail suppression, rain enhancement.
• India positioned as a leader in impact-based forecasting for the Global South.

Conclusion

Mission Mausam is more than a weather project—it is a national resilience strategy. By combining advanced technology, AI-driven models, and grassroots-level dissemination, India is preparing to face the twin challenges of climate change and extreme weather. The mission promises to make Bharat not just weather-aware, but truly weather-ready and climate-smart.

India’s New Weather System Warns Faster, Saves Lives

India, with its vast coastline, diverse geography, and monsoon-dependent climate, faces hundreds of extreme weather events every year — from cyclones and floods to heatwaves and droughts. Over 75% of districts are exposed to multiple climate hazards, making disaster preparedness not just important, but essential.

In January 2024, the India Meteorological Department (IMD) launched the Multi-Hazard Early Warning Decision Support System (MHEW-DSS) — a landmark digital transformation under Mission Mausam. This system marks a decisive shift from fragmented forecasting to an integrated, automated, and impact-based approach that protects lives, livelihoods, and infrastructure.

What Makes MHEW-DSS Different?

• Impact-Based Forecasting: Explains how weather will affect people, sectors, and communities.
• Real-Time Alerts: Forecast preparation time cut by 50%, accuracy improved by 30%.
• Wider Reach: Location-specific warnings now cover nearly 80% of India’s population.
• Cost Savings: Evacuation costs reduced to one-third compared to 1999.
• Self-Reliance: Built in-house, saving ₹250 crore and reducing dependence on foreign vendors.

Success Stories

During Cyclone Biparjoy and Cyclone Dana, MHEW-DSS enabled timely evacuations in Gujarat and Odisha — resulting in zero casualties.

Farmers using IMD’s agromet advisories reported 52.5% higher annual income compared to those who did not. If extended across rain-fed districts, the economic benefit could reach ₹13,331 crore annually.

How It Works

• Satellite, radar, and ocean buoy data integration
• GIS-based maps for visualization
• Multi-model forecasting with bias correction
• Colour-coded warnings for easy public understanding

Forecasts are disseminated through SMS, mobile apps (Mausam), WhatsApp, APIs, TV, radio, and official websites, ensuring last-mile connectivity even in rural areas.

National and Global Impact

• National Reach: Over 200 organizations, including NDMA and NITI Aayog, rely on MHEW-DSS.
• Global Role: IMD provides cyclone and severe weather advisories to countries across the North Indian Ocean and Asia-Pacific.
• Recognition: Awards include the National Award for e-Governance (2025), UN Sasakawa Award (2025), and ET GovTech Award (2026).

A Weather-Ready India

• Protects coastal communities from cyclones
• Helps farmers plan sowing and irrigation
• Supports renewable energy management
• Strengthens public health during heatwaves
• Saves resources and reduces environmental impact

Key Highlights Table

Feature	Impact
Forecast Accuracy	Improved by 30%
Preparation Time	Reduced by 50%
Population Coverage	80% of India
Evacuation Costs	Down to one-third

In short: India’s MHEW-DSS is not just about predicting the weather. It’s about protecting people, empowering communities, and building a safer future.

India’s Next-Gen Weather Model Uses Supercomputer for High-Resolution Forecasting Like Never Before

India has unveiled the Bharat Forecast System (BFS), the world's highest-resolution weather model, operating on a 6-kilometre grid. Developed by the Indian Institute of Tropical Meteorology (IITM), BFS aims to provide highly localized forecasts, improving predictions for disaster risk reduction, agriculture, and public safety.

The system is powered by Arka, a supercomputer with 11.77 petaflops of computational capacity and 33 petabytes of storage, significantly reducing forecast processing time from 10 hours to just 4 hours.

This development comes within a few months after Indian Space agency ISRO’s National Remote Sensing Centre (NRSC) made a major breakthrough in now-casting lightning events over India using data from geostationary satellites. This enhanced predictive accuracy with a 2.5-hour lead time.

BFS integrates data from 40 Doppler Weather Radars, which will expand to 100, improving real-time monitoring. These radars provide nowcasts—short-term forecasts for the next two hours, crucial for disaster preparedness.

Unlike traditional square grids, BFS uses a triangular cubic octahedral grid, which enhances spatial accuracy and ensures better data distribution. This allows BFS to predict extreme weather events with 30% more accuracy, including cyclones and heavy rainfall.

BFS leverages satellite imagery and AI-driven climate models to refine predictions.
AI helps in pattern recognition, improving forecasts for monsoons, heatwaves, and localized storms.

To recall, in October 2023 researchers fom the US had introduce the Recurrent Earthquake foreCAST (RECAST), a deep learning model for earthquake forecasting.

Compared to global models from the US, UK, and EU, which operate at 9–14 km resolution, BFS offers unmatched precision, covering the tropical region between 30° South and 30° North latitudes. BFS operates at 6-km resolution, surpassing global models from the US, UK, and EU. 

This breakthrough comes at a crucial time, as extreme weather events increasingly impact India's economy, particularly food inflation and crop damage.

BFS provides hyperlocal forecasts down to the panchayat level, making it one of the most precise weather models in the world.

This breakthrough positions India as a global leader in meteorology, enhancing agriculture, disaster management, and climate resilience.

A major leap for India's meteorological capabilities! What do you think—could this reshape climate resilience strategies? Do comment your opinion...

Disclaimer : All images are representational

ISRO Achieves Breakthrough in Now-Casting Lightning Events Over India Using Data From Geo Satellites

ISRO has made a major breakthrough in now-casting lightning events over India using data from geostationary satellites. This advancement, led by ISRO’s National Remote Sensing Centre (NRSC), enhances predictive accuracy with a 2.5-hour lead time.

Now-casting is the process of predicting imminent weather events with a short lead time, usually within the next 0 to 6 hours. It’s different from traditional weather forecasting, which predicts conditions over days or weeks.

The method relies on detecting lightning signatures in Outgoing Longwave Radiation (OLR) data from the INSAT-3D satellite. A reduction in OLR strength serves as an indicator of potential lightning occurrences. To refine predictions, ISRO incorporated additional meteorological parameters like Land Surface Temperature (LST) and wind, creating a composite variable that improves forecasting accuracy.

 

The dome-shaped enclosure, or radome, for antennae at an NRSC facility

This breakthrough is crucial for disaster management and public safety, as lightning is a dominant natural hazard in tropical regions.

How It Works

• Satellite Observations: ISRO researchers detected lightning signatures in Outgoing Longwave Radiation (OLR) data from the INSAT-3D satellite. A reduction in OLR strength serves as an indicator of potential lightning occurrences.
• Additional Meteorological Parameters: To refine predictions, ISRO incorporated Land Surface Temperature (LST) and wind data, creating a composite variable that improves forecasting accuracy.
• Real-Time Monitoring: The developed composite variable effectively captures variations in lightning activity observed by ground-based measurements, allowing for improved prediction of lightning occurrence and intensity.

Why This Matters:

Lightning is a dominant natural hazard in tropical regions, causing significant damage and loss of life. This breakthrough enables early warnings, helping authorities take preventive measures to reduce risks.

For the 1st Time, AI Beats Conventional Weather Forecasting by Accurately Predicting Weather 3-10 Days Ahead, in Less Than A Minute

For the first time, Artificial Intelligence (AI) models are leading in making weather predictions 3 to 10 days ahead.

The GraphCast Al model, developed by Google DeepMind, uses a machine-learning model that has learned from more than 40 years of weather forecasts.

It outperformed the conventional forecasting method in 90% of the 1,380 metrics used, which included temperature, pressure, wind speed and direction, and humidity at different levels of the atmosphere, Google DeepMind said in a peer-reviewed paper.

Developed by Google’s AI company DeepMind in London, GraphCast outperforms conventional and AI-based approaches at most global weather-forecasting tasks. Researchers first trained the model using estimates of past global weather made from 1979 to 2017 by physical models. This allowed GraphCast to learn links between weather variables such as air pressure, wind, temperature and humidity.

The trained model uses the ‘current’ state of global weather and weather estimates from 6 hours earlier to predict the weather 6 hours ahead. Earlier predictions are fed back into the model, enabling it to make estimates further into the future.

The standard conventional method called numerical weather prediction (NWP) uses mathematical models based on physical principles. These physical models crunch weather data from buoys , satellites and weather stations worldwide using supercomputers. The calculations accurately map out how heat, air and water vapour move through the atmosphere, but they are expensive and energy-intensive to run.

DeepMind researchers found that GraphCast could use global weather estimates from 2018 to make forecasts up to 10 days ahead in less than a minute, and the predictions were more accurate than the European Centre for Medium-Range Weather Forecasts (ECMWF)'s High RESolution forecasting system (HRES) — one version of the UK's NWP — which takes hours to forecast. Notably, ECMWF provides world-leading weather predictions up to 15 days in advance.

The GraphCast model can run from a desktop computer and makes more accurate predictions than conventional models in minutes rather than hours.

“GraphCast currently is leading the race amongst the AI models,” says computer scientist Aditya Grover at University of California, Los Angeles.

The model is described in Science on 14 November.

In the troposphere, which is the part of the atmosphere closest to the surface that affects us all the most, GraphCast outperforms HRES on more than 99% of the 12,00 measurements done by Deepmind researchers.

Across all levels of the atmosphere, GraphCast model outperformed HRES on 90% of weather predictions.

Earlier in last month, IndianWeb2.com reported that researchers from the Universities of California at Berkeley and Santa Cruz, and the Technical University of Munich, unveiled the Recurrent Earthquake foreCAST (RECAST), a deep learning model for improved earthquake forecasting.