Precision in Prediction: IMD Officials Detail How AI is Revolutionizing India’s Weather Forecasts

The India Meteorological Department (IMD) is undergoing a technological renaissance, fundamentally shifting from traditional physics-based models to a hybrid environment that integrates Artificial Intelligence (AI) and Machine Learning (ML). Senior officials, including Deputy Director Abhishek Anand, recently emphasized that these advanced tools are enabling more precise data analysis while requiring significantly less computational power. This evolution is not merely about faster processing; it is about the "hyper-localization" of weather data. By utilizing massive datasets spanning over a century, IMD is now capable of detecting short-term atmospheric changes with unprecedented reliability, ensuring that warnings for thunderstorms, lightning, and heavy rainfall reach vulnerable communities well before they strike.

A cornerstone of this advancement is the Bharat Forecasting System (BharatFS), which was fully integrated in May 2025. This system features a high-resolution 6-km grid—a significant upgrade over the previous 12-km model—resulting in a documented 30% rise in the accuracy of extreme rainfall predictions. Complementing this is the Mithuna-FS model, a global coupled system that integrates data from the atmosphere, ocean, and land surface to reduce biases in temperature and fog visibility. When paired with AI post-processing, these models allow for district-level extreme event probabilities that were previously impossible to calculate with such granular detail.

The speed of dissemination has also seen a dramatic improvement. Thanks to supercomputing upgrades like 'Arka' and 'Arunika', the time required to run a full forecast has been slashed from 12 hours to between three and six hours. This rapid turnaround is critical for short-range forecasts related to monsoons and western disturbances. To bridge the linguistic divide, IMD has integrated an AI-powered tool called 'Bhashini', which translates these complex weather alerts into regional languages. This ensures that approximately 15.6 million farmers across India receive real-time, location-specific agromet advisories through platforms like Mausamgram, Meghdoot, and e-Gramswaraj.

Looking ahead, the IMD is in the process of inducting cutting-edge global AI models such as GraphCast, Pangu, and FourCastNet. These tools, combined with an expanding network of Doppler radars in regions like Ranchi and Deoghar, are expected to further strengthen real-time monitoring of severe weather events. By moving toward a model of "impact-based forecasting," the department is no longer just predicting the weather; it is informing citizens of the potential impact on their health, transport, and livelihoods. This integrated approach, supported by the national Mission Mausam, is positioning India as a global leader in climate resilience and disaster preparedness