The Ministry of Earth Sciences has developed and implemented the Bharat Forecast System (BharatFS), India's indigenous high-resolution global weather prediction model. Based on the Triangular Cubic Octahedral (TCo) dynamical grid, the system operates at 6 km horizontal resolution, significantly surpassing India's previous GFS T1534 system (~12km resolution) and typical global operational models that operate at 9-14 km resolution.
The enhanced resolution enables BharatFS to generate forecasts every 6 km, allowing capture of local weather features and providing forecasts at the cluster of panchayats level. This capability is particularly valuable for agricultural applications, helping farmers with crop planning, irrigation scheduling, and harvesting decisions. Water authorities can better manage reservoirs during monsoon seasons, reducing flood risks and improving yield resilience.
The system demonstrates significant improvement in predicting extreme rainfall events in the core monsoon region, which is crucial given the increasing frequency and severity of extreme events due to climate change. These enhancements contribute to faster and more targeted disaster response, increasing the country's overall disaster preparedness.
BharatFS was developed by a team of scientists from Indian institutions including IITM-Pune, with support from NCMRWF-Noida and the India Meteorological Department. The modeling system is powered by indigenous MoES supercomputing facilities - Arka (IITM-Pune) and Arunika (NCMRWF-Noida) - which have reduced runtime enabling real-time weather prediction. The development represents India's capability to build world-class weather and climate forecasting systems locally, aligned with the Make in India initiative.
The system has positioned India as the only country running a global weather prediction model at 6 km resolution for real-time weather prediction, upgrading India's meteorological services and enabling support to neighboring countries, thereby reinforcing regional leadership and self-reliance in weather forecasting technology.