India’s Multi-Hazard Early Warning Decision Support System (MHEW-DSS)- Explained Pointwise

What is MHEW-DSS and what are its core objectives?

MHEW-DSS is an advanced digital forecasting platform developed entirely in-house by the India Meteorological Department (IMD) using open-source technology and domestic expertise. It operates in real time using tools such as Geographic Information System (GIS) maps to quickly analyse and share weather information.

It was officially launched in January 2024 under Mission Mausam – the Union Cabinet-approved initiative to modernise India’s weather forecasting infrastructure.

Background– India is highly vulnerable to climate hazards. Floods affect around 40 million hectares of land each year, heatwaves are increasing, and cyclones cause major losses. Earlier forecasting systems were fragmented, slow, and dependent on foreign vendors, reducing timely warnings.

Core Objectives of the MHEW-DSS

1. Integrated multi-hazard forecasting-To build a single, unified platform covering all major hazards including cyclones, floods, heatwaves, thunderstorms, droughts, and landslides, replacing the earlier fragmented, hazard-by-hazard approach.

2. Speed and accuracy-To significantly cut forecast preparation time and improve forecast accuracy so that warnings reach communities when they can still act, not after the event has already begun.

3. Self-reliance and indigenisation-To eliminate dependence on foreign meteorological technology vendors and build a fully indigenous system, aligned with India’s broader Atmanirbhar Bharat vision.

4. Universal reach-To ensure that timely, location-specific weather information reaches every household, farmer, fisherman, and emergency responder, embodying the national philosophy of “Har Har Mausam, Har Ghar Mausam.”

What are the salient features of the MHEW-DSS?

1. Automated weather data processing- Over 90% of weather data collection, quality checks, and integration are now automated, eliminating manual bottlenecks and enabling near-instant detection of emerging weather threats.

2. Greater use of forecast models- More than 95% of numerical weather prediction (NWP) model inputs are used in forecasting, a sharp improvement over earlier systems where large volumes of model data went unused due to manual processing limitations.

3. Extended forecast lead time- Lead time has been stretched from 5 days to 7 days, giving state governments, district administrations, and communities meaningfully more time to prepare and act.

4. Faster forecast preparation- Time to prepare a forecast has been cut by roughly half, from 6 hours to about 3 hours, enabling warnings to reach the public faster when every hour matters.

5. Impact-based forecasting- Rather than just predicting weather parameters, the system assesses how a weather event will affect sectors like agriculture, health, energy, and transport. Colour-coded risk levels make this information instantly understandable even for non-specialists.

6. Wide population coverage- Impact-based, location-specific warnings now reach nearly 80% of India’s population, including communities in neighbouring regions across the North Indian Ocean.

7. Cost savings and self-reliance- The system has generated approximately Rs. 250 crore in cost savings and has fully ended India’s dependence on foreign meteorological vendors.

8. Reduced evacuation costs- Improved cyclone landfall forecasting has brought evacuation costs down to one-third of their 1999 levels, a direct result of better 3–5 day ahead predictions by IMD.

What is the institutional framework supporting the MHEW-DSS?

Three institutions form the operational backbone of the MHEW-DSS:

1. Ministry of Earth Sciences (MoES)- It is the nodal ministry responsible for all weather and climate services in India. It oversees IMD and allied research institutions, providing both the policy mandate and scientific oversight that underpin the MHEW-DSS.

2. India Meteorological Department (IMD)- Established in 1875, IMD is India’s principal meteorological agency. Under the MHEW-DSS, IMD plays a central operational role by generating real-time forecasts and alerts through integrated digital systems.

3. Mission Mausam- It is the overarching policy and funding framework approved by the Union Cabinet in September 2024. It upgrades observation networks, data assimilation systems, and modelling capabilities that feed directly into MHEW-DSS operations.

How does the MHEW-DSS work? What is its operational architecture?

MHEW-DSS functions through an integrated digital pipeline:

What has been the sectoral impact of the MHEW-DSS?

1. Cyclone and marine safety- Special pre-emptive alerts are issued for fishermen when wind speeds are forecast to exceed 45 kmph or seas are expected to turn very rough. During Cyclone Biparjoy (Gujarat) and Cyclone Dana (Odisha), accurate MHEW-DSS forecasts enabled mass evacuations that resulted in zero casualties in both states.

2. Public health- Heatwave forecasts feed directly into Heat Action Plans, enabling early activation of cooling centres and hospital preparedness. Weather data also supports advance prediction of vector-borne diseases such as dengue and malaria, helping health authorities pre-position resources before outbreaks peak.

3. Agriculture and farmer incomes- Twice-weekly agromet advisories guide farmers on sowing, irrigation, and harvesting decisions. Farmers who adopted these advisories reported 52.5% higher annual income than those who did not. If crop-weather advisories reach all rain-fed districts, the estimated annual economic benefit is Rs. 13,331 crore.

4. Energy sector- Forecasts for solar radiation, wind speed, and temperature directly support renewable energy production planning. Automation within the system has also cut energy consumption, saving approximately 2,10,240 kWh annually.

5. Water resource management- Accurate monsoon prediction and rainfall forecasting support reservoir operations, flood control scheduling, and irrigation planning at the state level. Digital workflows have additionally saved approximately 63 kilolitres of water annually by eliminating paper-based chart production.

6. Environment- Digital forecasting has ended manual chart-plotting across 40 IMD offices, saving 23.4 tonnes of paper annually and avoiding 2.57 tonnes of CO₂ emissions, with associated annual cost savings of approximately Rs. 1.40 crore.

7. Governance and institutional efficiency- Over 200 organisations including NDMA, NDRF, and State Disaster Management Authorities use IMD’s applications. Annual manpower savings from digital transformation have reached approximately Rs. 57.6 crore.

What is the national and international significance of the MHEW-DSS?

National significance

1. Atmanirbhar forecasting- MHEW-DSS is India’s first fully indigenous, end-to-end digital forecasting system, eliminating foreign vendor dependence in a domain critical to national disaster preparedness and food security.

2. Sendai Framework alignment- The system directly operationalises India’s commitments under the Sendai Framework for Disaster Risk Reduction (2015–2030), particularly the goal of universal early warning coverage, with India now reaching nearly 80% of its population.

3. Scalable and replicable design- Built on open-source standards, the system can be extended to cover new hazards, new regions, and new sectors without structural overhaul, making it a long-term platform rather than a one-time project.

International significance

1. Regional early warning leadership- As a Regional Specialized Meteorological Centre (RSMC), IMD uses MHEW-DSS to provide severe weather advisories and tropical cyclone forecasting to countries including Bangladesh, Sri Lanka, Myanmar, Oman, and the UAE, making India a first responder for South and South-East Asian climate disasters.

2. Global institutional recognition- The system supports frameworks under the World Meteorological Organization (WMO) and the Economic and Social Commission for Asia and the Pacific (ESCAP)/WMO Panel on Tropical Cyclones, cementing India’s role in global early warning architecture.

3. Awards and recognition- MHEW-DSS has received the UN Office for Disaster Risk Reduction (UNDRR) Sasakawa Award for Disaster Risk Reduction 2025, the Award of Excellence at the Digital Transformation Summit 2026, and the Economic Times GovTech Award 2026, establishing India as a globally credible innovator in disaster risk reduction technology.

What are the challenges and limitations of the MHEW-DSS?

1. Last-mile connectivity gaps- In remote tribal, hilly, and deep coastal areas, poor internet penetration and low smartphone ownership limit the reach of digital channels like Mausamgram and the Mausam app, risking exclusion of the very communities most vulnerable to climate hazards.

2. Low weather literacy- Colour-coded alerts and sector-specific advisories are only useful if recipients understand them. A large share of farmers and fishermen still lack the weather literacy needed to translate a warning into a timely, appropriate action.

3. Observational network gaps- MHEW-DSS outputs are only as reliable as the data feeding them. Gaps in radar coverage, automatic weather station density, and ocean buoy networks, particularly in the North-East, central India, and the deep Indian Ocean, can degrade forecast quality in critical situations.

4. Uneven state-level implementation- The system delivers warnings efficiently at the national level, but converting those warnings into ground-level preparedness action depends on state and district administrative capacity, which varies enormously across India.

What should be the Way Forward?

Conclusion

Weather warnings matter only if they lead to action. MHEW-DSS shows India can build and scale world-class, indigenous forecasting systems. The key challenge now is last-mile delivery-ensuring farmers, fishermen, and local leaders receive and act on warnings in time, as this will determine India’s climate resilience.