“India’s weather is getting a homegrown upgrade—faster, smarter forecasts powered by BFS and AI.”
India’s battle with extreme weather is about to get some serious backup. After years of struggling with unpredictable monsoons, flash floods, and punishing heatwaves, the Ministry of Earth Sciences has unveiled the Bharat Forecasting System (BFS), a new high-resolution model designed to give meteorologists sharper tools and the public earlier warnings.
Launched on May 26, 2025, and powered by the “Arka” supercomputer in Pune, BFS runs on a 6-kilometre grid, doubling the detail of the older 12-km models. That jump in resolution could mean the difference between a vague regional alert and a pinpoint forecast for a city or district.
Meanwhile, the Indian Meteorological Department (IMD) has been testing out artificial intelligence in very practical ways, from short-term rainfall alerts to rapid “nowcasts” that track storm movement by the hour.
BFS and the science behind sharper forecasts
The Bharat Forecasting System is more than a routine software upgrade. It changes how India’s weather models see the atmosphere in motion. Like most modern systems, BFS is based on Numerical Weather Prediction (NWP), which uses physics equations to track how air, water, and heat interact. The difference lies in resolution.
Instead of the older 12-kilometre grids, BFS zooms in to 6 kilometres, giving forecasters a closer look at local variations that often decide whether a storm fizzles or turns destructive. That added detail matters in places like the Himalayas, where cloudbursts can hit one valley but spare the next, or along the Bay of Bengal, where cyclones can change track within hours. All of this runs on the Arka supercomputer in Pune, which pulls in streams of data from satellites, radars, and ground sensors to generate forecasts with far sharper granularity.
The model is also being tuned to India’s unique climate dynamics, which have been pretty erratic of late, with intense monsoons and cloudbursts up north disrupting livelihoods. Unlike global forecasting systems, which often generalize for broader regions, BFS is built with local terrain, ocean currents, and seasonal cycles in mind. That makes its output particularly useful for farmers, as well as disaster management teams that have had their hands full this year.
For agriculture, even a one-day improvement in rainfall prediction can make a huge difference, especially with regard to planting seeds on time. For cities, sharper forecasts can help plan for flash floods as well as heatwaves. And for coastal regions that make up a major part of India, even a few extra hours to prepare for a cyclone can save lives and reduce economic losses.
AI steps into India’s weather playbook
While BFS handles the heavy lifting on physics-based modeling, artificial intelligence is quietly reshaping how forecasts are delivered in real time. The IMD has already started using AI/ML tools in short-term “nowcasting,” where Doppler Weather Radars feed live data into machine learning models. These systems can predict thunderstorms or heavy showers within a window of 1–3 hours, which, as we already mentioned, is a crucial advantage in urban areas like Delhi, Bengaluru, or Mumbai, where torrential downpours can paralyze daily life.
IIT Bombay recently developed an AI-powered rainfall nowcast for Mumbai, which updates every eight minutes and gives a 90-minute lead on rainfall patterns, which may not sound like a lot of time, but in some cases can be the difference between being home safe and dry and being stuck outside in a storm.
Beyond nowcasting, AI is also entering research and policy planning. The Ministry of Earth Sciences has created working groups with IITs to build customized machine learning models for weather and climate. These include urban-scale datasets at 6 km resolution, which can map heat islands, rainfall distribution, or pollution interactions in detail.
MoES is even building a GPU-powered “virtual workspace” to develop and test AI models for forecasting. The approach isn’t about replacing physics-based models but about filling the gaps. AI can crunch past rainfall records, humidity levels, and wind data in ways that complement large-scale models like BFS. When you put the two together, what you get is a layered approach where BFS gives you broader forecasts and AI provides fast, hyperlocal updates.
Cloudy, with a chance of AI
The new forecasting system is changing how people experience India’s weather. It’s not just numbers on a screen; farmers in small villages notice when alerts come earlier, deciding when to plant or water. City planners see heatwaves and floods with a bit more lead time, giving them space to act. Coastal towns feel the difference when storms approach, and families can make small adjustments that protect lives.
All of this comes from tools built in India, for India, a clear example of an aatmanirbhar approach to modern challenges. Combining large-scale physics models with quick AI updates, the system layers knowledge in ways that feel almost intuitive. Life in a country where the weather has always been unpredictable now gains a thread of clarity, letting people respond rather than just react.
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