AI pilot to improve local monsoon onset forecasting.

 


1. What is the AI Pilot & Why It Matters

The “AI Pilot To Improve Local Monsoon Onset Forecasting” is an initiative launched by the government (in 2025) with the aim to generate localized monsoon-onset forecasts for farmers, ahead of the Kharif season. Traditional forecasts by India Meteorological Department (IMD) typically provide long-range or regional forecasts; finer, local-level onset forecasts have historically been hard to achieve.
The AI pilot promises more precise, actionable information — giving farmers lead time to plan sowing, land preparation, and resource use, reducing risk of crop failure. 

2. How the AI Model Works — Data & Technologies

The pilot uses a blended AI model that integrates multiple advanced systems: NeuralGCM (from Google), ECMWF-AIFS (from European Centre for Medium-Range Weather Forecasts), and 125 years of historical IMD rainfall data. By combining modern ML-based global forecasting models with long-term historical data, the system can generate probabilistic, localized onset forecasts, rather than broad national-level projections. This approach leverages advances in computing and data availability — a shift from older forecasting methods which relied on relatively fewer “predictors.” Indeed, earlier, IMD used large numbers of predictors but refined their forecast models over time. 

3. Dissemination: Reaching Farmers Where It Matters

The forecast outputs from the pilot are not just kept in scientific reports — they are sent directly to farmers via SMS through the M-Kisan platform. To ensure accessibility across diverse linguistic regions, the messages were issued in multiple Indian languages: Hindi, Odia, Marathi, Bangla and Punjabi. The pilot focused on providing forecasts alone — it did not include financial subsidies or material aid. This indicates the project aims for informational empowerment (climate-smart agriculture), leaving resource decisions to farmers. 

4. Impact: How Farmers Used the AI Forecasts

Post-forecast feedback — gathered via call centres in certain states (e.g. Madhya Pradesh, Bihar) — suggested that 31–52% of surveyed farmers adjusted their agricultural decisions based on forecasts. Changes included: adjusting land preparation, shifting sowing dates, choosing different crops, and modifying input usage (fertilisers, seeds). This suggests that localized monsoon onset forecasts from AI can lead to behavioral change on the ground — not just awareness. Such adaptive responses may help reduce crop failure risk, make sowing more efficient, and improve resilience to climate variability.
More broadly, this could lead to more climate-smart agriculture, with better synchronization between sowing and rainfall, potentially improving yields and stabilising rural incomes.

#AIMonsoonForecast
#MonsoonOnset
#WeatherAI
#ClimateTech
#AgriTech
#SmartFarming
#AIForAgriculture
#IMDIndia
#MonsoonPrediction
#FarmersFirst
#DataDrivenFarming
#ClimateSmartAgriculture
#AIInnovation
#WeatherForecastin
#RainfallPrediction
#DigitalAgriculture
#DisasterPreparedness
#SustainableFarming
#AI4Good
#ClimateResilience

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