Discuss how high-resolution numerical weather prediction models like the Bharat Forecast System can strengthen India's disaster preparedness and agricultural resilience.

Numerical Weather Prediction (NWP) models solve atmospheric physics equations over a computational grid — the finer the grid, the sharper the forecast. The Bharat Forecast System (BharatFS), unveiled in May 2025 by IITM Pune under the Ministry of Earth Sciences, runs at 6 km resolution against its predecessor GFS T1534's ~12 km, making forecasts actionable at the local level [1].

The technological leap

• Built on a Triangular Cubic Octahedral (TCo) dynamical grid, it delivers forecasts at the panchayat or cluster-of-panchayats level for up to 10 days, covering rainfall, temperature and low-pressure genesis [1].
• The supercomputers Arka (IITM-Pune, 11.77 PetaFLOPS) and Arunika (NCMRWF-Noida, 8.24 PetaFLOPS) raised MoES computing power from 6.8 to 22 PetaFLOPS, cutting model runtime from ~12 hours to 3–6 hours — the shift that makes real-time operational use possible [3].

Strengthening disaster preparedness

• Roughly 30% improvement in extreme rainfall prediction accuracy allows flood and cloudburst warnings with usable lead time [2].
• Village-scale resolution converts blanket district alerts into targeted evacuation and relief pre-positioning, sharpening NDMA and state response.
• Localised coastal and marine forecasts protect fisherfolk and cyclone-exposed populations [1].

Building agricultural resilience

• Farm operations — sowing, irrigation, pesticide spraying, harvest timing — hinge on rainfall within days, precisely BharatFS's forecast window [1].
• Reliable hyperlocal advisories reduce weather-induced crop loss, stabilise incomes and strengthen crop-insurance assessment.
• Being indigenously developed, the system embeds tropical-monsoon dynamics that imported models capture poorly [2].

Yet resolution alone is insufficient: dense ground observation networks and last-mile dissemination in local languages determine whether a forecast reaches the farmer. Integrating BharatFS outputs with Mission Mausam, agro-advisory platforms and district disaster plans will convert computational capability into community resilience [4]. Such a model exemplifies scientific self-reliance serving Article 51A(h)'s scientific temper and SDG-13 on climate action.