AI-enabled Systems introduced by IMD to provide Hyper-Local Weather forecasts: Dr Jitendra Singh

I have enough Tier 1 facts. Writing the note now.

AI-enabled Hyper-Local Weather Forecasts by IMD — UPSC Study Note

1. At a Glance

2. Why in the News

3. Background & Evolution

4. Core Static Facts

5. Multi-Dimensional Analysis

Scientific / Technological - Combines Numerical Weather Prediction (NWP) with AI/ML data-driven approaches — hybrid stack [S1]. - BharatFS uses TCo grid allowing resolution finer than typical global models (9–14 km) [S2]. - Captures sub-district phenomena: thunderstorms, hailstorms, lightning, heatwaves [S2].

Economic / Agricultural - Stakeholder-driven response to agriculture sector demand for localised forecasts — affects sowing, irrigation, crop insurance (PMFBY) [S1]. - 4-week monsoon advance probability aids kharif planning in 16 States [S1].

Administrative / Governance - Forecasts at panchayat / sub-district level operationalise weather data for disaster managers, district administration, ULBs [S1][S2]. - Cooperative federalism dimension: UP pilot signals state-tailored deployments.

Environmental / Climate Adaptation - Hyper-local data crucial for climate adaptation under NAPCC and State Action Plans on Climate Change — extreme rainfall events increasing [S2].

6. Recent Developments

7. Prelims Hooks

8. Mains Relevance

9. Related Topics to Study Next

10. Common Errors / Trap Areas

11. Sources

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