SDG 13: Climate Action | SDG 9: Industry, Innovation and Infrastructure | SDG 11: Sustainable Cities and Communities
Ministry of Earth Sciences (MoES) | India Meteorological Department (IMD)
Dr. M. Ravichandran, Secretary of the Ministry of Earth Sciences (MoES), has proposed a paradigm shift in meteorological science by advocating for the strategic fusion of traditional physics-based numerical models with Artificial Intelligence (AI). Speaking at the India AI Impact Summit 2026, he introduced the “Elephant and the Ant” analogy: physics models are excellent at spatial mapping (the elephant) and AI is far superior at interpreting the fine-scale “time series” variations (the ant), enabling precise forecasting where life and infrastructure are most at risk.
This hybrid architecture aims to solve the “unpredictability gap” caused by the numerous assumptions in current numerical models, which often lead to error growth. By “opening up” the IMD’s 150-year legacy data archive to multidisciplinary researchers, India intends to accelerate breakthroughs in 1-kilometer resolution downscaling, providing game-changing precision for district-level disaster response and agricultural advisories.
Key Pillars of the MoES Weather Roadmap
Hybrid Forecasting Architecture: Fusing AI with physics-based models to reduce model bias and improve “initial conditions” for simulations.
Hyper-Local Downscaling: Utilizing AI to translate coarse global data into 1-km resolution forecasts, essential for urban flood and heatwave alerts.
Data Democratization: Unlocking 150 years of IMD observational data for young researchers and data scientists to build next-generation climate tools.
Multi-Disciplinary Collaboration: Breaking scientific silos by involving biology experts, data scientists, and engineers to analyze weather through diverse lenses.
Trust & Ethics in AI: Prioritizing rigorous validation and verification to ensure AI-generated weather insights are dependable for public safety.
Policy Relevance
For India, Dr. Ravichandran’s roadmap represents a transition from “Broad Spatial Warnings” to “Hyper-Local Life-Saving Alerts,” critical for a nation facing increasing climate volatility.
Standardizing “1-km Forecasting”: Moving to a 1-km resolution acts as a standard maker move, positioning India as a global leader in high-resolution climate modeling for the Global South.
Bypassing Model Error Growth: By using AI to correct errors early in simulations, India can bypass the “assumption-heavy” limitations of older numerical models.
Operationalizing “Mission Mausam”: The roadmap directly supports Mission Mausam, a ₹2,000 crore initiative to install a wider network of radars and radiometers to feed AI-ready data into the system.
Federal Disaster Resilience: AI-assisted cyclone forecasts (like the Advanced Dvorak Technique) have already improved path accuracy up to 96 hours ahead, protecting millions in coastal states.
Relevant Question for Policy Stakeholders: How should MoES and Ministry of Agriculture utilize 1-km resolution AI downscaling to deliver hyper-local irrigation advisories to smallholder farmers?
Follow the full update here: Roadmap for Weather Prediction - February 20, 2026