THE CONTEXT: India has entered an era of compound climate extremes. The first severe heat wave of 2025 struck on 15 March, a full 20 days earlier than in 2024, with Boudh (Odisha) touching 43.6 °C. Short-duration cloud bursts drenched the Western Himalaya, overwhelming district-level contingency plans. These events underscore a governance need for impact-based, probabilistic forecasts rather than deterministic, post-facto responses.
THEORETICAL FRAMEWORK — FROM PHYSICS TO DATA:
| Aspect | Numerical Weather Prediction (NWP) | Pure AI / ML | Physics-informed ML (Hybrid) |
|---|---|---|---|
| Core engine | Navier-Stokes, thermodynamics on high-performance computers | Pattern-learning from historical sensor archives | ML model constrained by energy & mass-conservation |
| Lead-time strength | Synoptic (3–7 d) & medium-range | Nowcasting (mins-hrs) & sub-seasonal | Sub-seasonal to decadal |
| Computational cost | 2–4 h per global run on a petascale HPC (e.g., IMD’s Mihir) | Minutes on TPUs/GPUs | Moderate; best of both |
WHY AI MATTERS — VALUE ADD OVER LEGACY NWP
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- Speed × Granularity: lightning-fast inference allows real-time re-initialisation with fresh Doppler-radar sweeps, critical for cloud-burst nowcasts.
- Learning Hidden Tele-connections: Data-driven models discover non-linear links (e.g., Madden-Julian Oscillation) that are often diluted in coarse-grid physics.
- Probabilistic Hazard Curves: ensemble AI offers quantifiable uncertainty, aligning with Sendai Framework risk metrics.
INDIAN INSTITUTIONAL LANDSCAPE
1. Mission Mausam (2024-26)₹ a 2,000 cr scheme to densify observation networks, deploy GPU clouds, and incubate AI-ready Earth system models.
2. AI-ML Centre @ IITM-Pune – integrating 37 coastal Doppler radars with convolutional networks for 15-minute rainfall nowcasts.
3. IIT-Delhi-MIT-JAMSTEC Monsoon Model – Long Short-Term Memory (LSTM) network trained on 1901-2001 data, 61.9 % skill (2002-22 test) versus ~45 % in CFSv2/NCUM.
4. IMD Cyclone Suite Upgrade – 72 h mean track error collapsed from 156 km (LPA 2019-23) to 116 km in 2024 after embedding AI-assimilated scatterometer winds.
5. State pilots:
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- Odisha Heat-Action Plan now couples Extreme Heat-Mortality Index with neural net forecasts;
- Hyderabad Flood Early-Warning v2.0 fuses CNN rainfall–run-off simulations with ward-level drainage inventories.
GLOBAL BENCHMARKS & KNOWLEDGE TRANSFERS
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- EU’s “Destination Earth” Digital Twin went operational in June 2024, delivering 1-km extreme-event simulations on the LUMI EuroHPC, and sharing open APIs for national met-services.
- WMO Unified EWS for All (2023) standardises AI-ready CAP formats, enabling India’s NDMA to plug AI outputs into last-mile SMS alerts.
- Private sector: Tomorrow.io radar cubesats, IBM-The Weather Company’s generative-AI briefings for grid operators.
SIGNIFICANCE FOR GOVERNANCE & SDGs
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- Lives & Livelihoods – NDMA estimates ₹ 1.8 trn annual avoided losses if forecast-based anticipatory action reaches five-state pilot scale.
- Agriculture – AI-driven “Climate Smart Advisory” pushes micro-basin rainfall outlooks to 12 mn PM-KISAN farmers, lowering indemnity outgo under PM-FMBY.
- Energy Transition – 72-h solar irradiance ensembles optimise dispatch, cutting coal-plant ramping costs by 4 %.
- Urban Resilience – city Master Plans (Delhi-MPD-41 draft) now stipulate that drainage designs use AI-derived IDF curves with ≥95 % Skill score.
THE CHALLENGES:
1. Data-Barrier: A Foundational Bottleneck: India’s climate intelligence architecture is handicapped by non-uniform, low-resolution datasets, especially in vulnerable regions like the Himalayas, Northeast, and coastal belts.
Causes:
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- Sparse deployment of Doppler radars and Automatic Weather Stations (AWS).
- Legacy data in ASCII/text format, lacking interoperability with modern ML systems.
- Meteorological data held in departmental silos (IMD, ISRO, NRSC, SASE, INCOIS).
Policy Lacuna:
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- Despite the Digital Personal Data Protection (DPDP) Act 2023, weather data remains unclassified under essential public goods.
- Absence of National Data Trustee Rules has delayed standardisation.
2. Compute Sovereignty: Strategic Tech Deficit:: AI models for weather forecasting demand high-performance computing (HPC), but India’s supply chains are import-dependent.
Facts:
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- Over 90% of GPUs used in climate models are imported, mainly from NVIDIA and AMD (USA).
- India’s domestic chip industry is in a nascent phase — only 2 fabs under development (Dholera, Mohali).
Vulnerability:
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- Geopolitical risks (e.g., US-China export controls on AI chips) may paralyse India’s AI ecosystem.
3. Human Capital Gap: The Invisible Deficit: India lacks a critical mass of dual-skilled professionals in meteorology and AI.
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- Fewer than 300 professionals with expertise in both mesoscale atmospheric dynamics and deep learning (as per MoES-CECM estimates).
Structural Gap:
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- Disciplinary silos in academia — meteorologists in IITM, AI specialists in IIITs, rarely intersect.
4. Black-Box Trust & Liability: The Accountability Crisis: ML models often produce accurate results without interpretability, which can erode public trust during false positives or forecast busts.
Legal Vacuum:
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- No regulatory mandate for explainability in public weather forecasts.
Ethical Hazard:
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- During Cyclone Biparjoy (2023), conflicting forecasts between private AI models and IMD caused panic in Gujarat’s coastal belt.
5. Equity Divide: Technological Marginalisation: AI-driven hyperlocal alerts often exclude vulnerable populations, especially in Aspirational Districts and SC/ST-dominated belts.
Ground Reality:
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- 40% of India still relies on feature phones (TRAI, 2023).
- Climate dashboards, real-time mobile apps (e.g., MAUSAM, Damini) cater to internet-enabled users only.
THE WAY FORWARD:
1. Launch of a National Climate-AI Mission (N-CLIM-AI): India must institutionalize an umbrella 5-year ₹10,000 crore mission under the Ministry of Earth Sciences, akin to ISRO or BIRAC, dedicated to climate-tech innovation.
Features:
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- Establish Petascale GPU Cloud Infrastructure to host next-gen ML models.
- Build an Open Earth Data Lake, integrating satellite, radar, crowd-sourced, and IoT data.
- Introduce DARPA-style moonshot challenges to tackle region-specific climate anomalies (e.g., Konkan cloudbursts, Chennai cyclones).
2. AI-Ready Observation Grid: From Data Poverty to Data Democracy
Objective: Expand India’s sensory web for real-time, granular climate data via 5,000 low-cost IoT-based micro-meteorological stations, prioritising Aspirational and Eco-sensitive districts.
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- CubeSat SAR (Synthetic Aperture Radar) constellations for continuous cloud-agnostic monitoring.
- Blockchain-based crowd-QC (quality control) where Panchayat-level data collectors earn tokens for accuracy — Data as Digital Livelihood.
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- Reflects Rwanda’s WeatherSafe blockchain pilot where farmers monetize real-time data for micro-insurance decisions.
- Echoes Ashok Dalwai Committee on Doubling Farmers’ Income (2018), which called for decentralised agri-meteorological data systems.
3. Skill Accelerator for AI-Meteorology Convergence: Nurturing the ‘Weather-Coder’ Workforce
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- Bridge the skill vacuum by targeting 10,000 AI-fluent meteorologists by 2030.
Execution Path:
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- Launch a national “Climate-Tech Gurukul” with MOOCs and 18-month residencies in IMD regional centres.
- Modular courses to combine numerical modelling, climate physics, and deep learning.
- Tie up with Skill India, PM Kaushal Vikas Yojana, and IGNOU for blended learning.
4. Ethics-by-Design Mandate: Making AI Accountable and Transparent
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- Create a model procurement clause mandating AI solutions used in public forecasts to follow:
- Publication of Model Cards (metadata, training sets, biases).
- Display of uncertainty bars with ≥90% confidence levels.
- Annual third-party audits under Digital Public Goods Registry.
5. Regulatory Sandbox for Climate Risk Financing: Financing Resilience via AI
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- Create a SEBI-IRDA Joint Regulatory Sandbox housed within GIFT-IFSC to pilot:
- Parametric micro-insurance for farmers using AI-derived hazard indices.
- Climate catastrophe (CAT) bonds linked to early-warning triggers (e.g., drought index, flood depth).
Data-Driven Design:
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- Leverage IMD + Panchayat + satellite inputs in real-time to trigger payouts.
- Example: Cyclone Amphan (2020) damage payout delayed due to post-disaster assessments; this can be AI-automated.
6. Community-Centric Dissemination: Climate Alerts for the Last Mile
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- Rewire the Common Alerting Protocol (CAP 2.0) for bottom-up, multilingual dissemination using:
- e-GramSwaraj platform.
- Self Help Groups (SHGs).
- Anganwadi SMS hubs for maternal alerts.
- Launch hyper-local micro-grants for Heat-Action Plans in urban slums.
THE CONCLUSION:
India stands at a transformative junction—where data intelligence meets climate urgency. Leveraging AI in climate forecasting is not just a technological aspiration but a governance imperative. The shift from probabilistic models to predictive preparedness can ensure a resilient future rooted in data equity, institutional synergy, and climate justice. With Mission Mausam as a stepping stone, India can build a new paradigm of climate governance that is federated, inclusive, and globally relevant.
UPSC PAST YEAR QUESTION:
Q. Discuss the meaning of colour-coded weather warnings for cyclone prone areas given by India Meteorological Department. 2022
MAINS PRACTICE QUESTION:
Q. The future of climate governance in India lies at the intersection of artificial intelligence and anticipatory governance. Discuss the recent initiatives, such as Mission Mausam and the use of AI in extreme weather forecasting.
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