How AI is Helping India in Climate Adaptation and Weather Prediction

Advanced technology is reshaping how we predict weather patterns and prepare for natural disasters.

Forecasting System: Cyclone and Extreme Weather Modelling

India has significantly enhanced its cyclone forecasting capacity through AI-assisted tools, such as the Advanced Dvorak Technique, which is used by the Indian Meteorological Department (IMD) and other government agencies to estimate cyclone intensity.

Furthermore, High-Power Computing Systems with a capacity of 22 PetaFLOPS have been installed by India’s Ministry of Earth Sciences. About 10% of this system uses special Graphics Processing Units for AI work.

There are also dedicated GPUs solely for AI and machine learning research in weather forecasting. These systems help develop better weather prediction models.

Comparative studies of global AI systems (including GraphCast, PanguWeather, Aurora, and FourCastNet) have demonstrated improved path-prediction accuracy up to 96 hours ahead of cyclone landfall, with 200-kilometre accuracy in seconds. These advancements are strengthening evacuation planning and infrastructure protection.

Developed by IIT Bombay, the Spatially Aware Domain Adaptation Network (SpADANet) is an AI model that improves cyclone and hurricane damage assessment from aerial images.

The spatially aware model achieved over 5% better accuracy than existing methods in classifying damage levels using limited labelled data. It addresses key constraints faced by disaster agencies, such as a lack of labelled data and limited computing power, and enables faster and more reliable disaster response.

Reliability Ensemble Averaging (REA) is used by IIT Madras to improve climate predictions for India. They combined 26 climate models and scored each based on accuracy in predicting current weather and future changes.

Testing on four Indian cities (Coimbatore, Rajkot, Udaipur, and Siliguri) showed that most models poorly predicted rainfall. However, REA provides more reliable results, thereby reducing uncertainty in climate planning in monsoon-prone regions.

To strengthen AI and machine learning research, IMD has established a dedicated team and signed agreements with IITs, NITs, ISRO, DRDO, and other institutions to facilitate collaboration.

Landslide, Flood and Glacial Monitoring

An indigenous AI-based landslide early-warning system provides alerts up to three hours before slope failures in the Himalayan region. The system employs low-cost sensors to measure soil moisture, rainfall, humidity, temperature, and ground displacement.

Data are fed into a machine learning model, achieving over 90% accuracy. Installed at more than 60 sites across Himachal Pradesh, it detects millimetre-level slope movements.

Built with locally sourced components at a fraction of the cost of imported technology, the system strengthens disaster preparedness. It enables timely evacuations in India’s landslide-prone areas.

Indian Land Data Assimilation System (ILDAS), funded by ISRO (2021-24), estimates land surface states and floodplain inundation using coupled models and remote sensing data.

Flood forecasting systems that integrate physics-based modelling and AI techniques enhance river basin management in the Ganga and Brahmaputra regions. 

BrahmaSATARK provides impact-based flood forecasts for the Brahmaputra Basin, while GBM-CLIMPACT is a climate-impact toolbox assessing water sector readiness in the Ganga, Brahmaputra, and Meghna basins.

Together, these AI-enabled climate solutions enhance early warning lead times, strengthen evacuation planning, reduce infrastructure losses, and safeguard vulnerable communities across climate-sensitive regions

Gram Panchayat Level Weather Forecasting (GPLWF) was launched by IMD in collaboration with the Ministry of Panchayati Raj. This service covers nearly all gram panchayats across India.

It uses multiple weather prediction models simultaneously. Farmers can access these forecasts through apps like e-Gramswaraj, Meri Panchayat, and Mausam Gram.

The forecasts include temperature, rainfall, humidity, wind, and cloud information. This helps farmers make better decisions about planting, harvesting, and irrigation.

Launched in 2025, Bharat Forecasting System (BharatFS) is an Indian-built weather prediction model. It provides very detailed forecasts at the village level.

BharatFS has a 6km resolution, which is better than the previous 12km resolution. It can predict rainfall up to 10 days in advance. This helps farmers, disaster managers, and the public prepare more effectively.

As the world prepares for net-zero emissions goals, AI-enabled climate solutions can support multiple areas and serve as essential tools for building climate resilience.

From renewable energy optimisation to sustainable agriculture and disaster prediction, India is advancing as a global leader in AI-enabled climate solutions. 

India is demonstrating that AI can be a powerful tool in combating climate change and could serve as a benchmark for vulnerable communities in the Global South.