Why in news?
On 12 May 2026 the India Meteorological Department (IMD) unveiled two artificial‑intelligence (AI)‑enabled weather models: a block‑level monsoon onset forecast and a high‑resolution rainfall forecast pilot. The new system can predict monsoon onset up to four weeks in advance for specific blocks, with an error margin of four days. It currently covers 3,196 blocks across 15 states and one union territory.
Background
India’s agriculture depends heavily on the timing and distribution of the southwest monsoon, which supplies about 70 per cent of annual rainfall. Farmers traditionally rely on district‑level forecasts, which are often too coarse for precise sowing decisions. Advances in machine learning now allow weather agencies to downscale broad atmospheric models to smaller areas.
The IMD developed the block‑level model in collaboration with the Indian Institute of Tropical Meteorology (IITM) and the National Centre for Medium Range Weather Forecasting (NCMRWF). The model synthesises data from automatic rain gauges, weather stations, Doppler radars and satellites. It uses AI algorithms to analyse multi‑decadal monsoon patterns and to predict the onset of the monsoon for each block.
Key features
- Provides weekly updates on the expected arrival of monsoon rainfall up to four weeks in advance for each block. Onset is defined as a continuous five‑day rainfall spell without long dry periods in the following 30 days.
- Covers 3,196 blocks in 15 states and one union territory, mostly in rain‑fed regions where timely rainfall is critical for sowing.
- High‑resolution rainfall pilot in Uttar Pradesh produces forecasts at a 1‑km grid for up to 10 days ahead using AI downscaling.
- Forecasts will be disseminated through application programming interfaces (APIs), the Agri Stack digital platform and state agriculture departments, enabling farmers to receive localised alerts.
Benefits
The new system can help farmers decide when to sow crops, apply fertiliser or protect fields from heavy rain. It also supports disaster management and water‑resource planning. As coverage expands, hyper‑local forecasts will reduce agricultural risk and improve yields.
Sources: Business Standard