PIB TrackerIMD's multi-hazard early-warning system explained
An indigenous digital forecasting platform under Mission Mausam that turns weather data into impact-based, location-specific disaster warnings.
What happened
- A government explainer has set out how India's Multi-Hazard Early Warning Decision Support System (MHEW-DSS) works — an advanced digital forecasting platform built in-house by the India Meteorological Department (IMD) using open-source technology.
- The system was launched in January 2024 and now operates under Mission Mausam, the weather-and-climate programme the Union Cabinet approved in September 2024.
- It issues impact-based, location-specific warnings that reach roughly 80% of the population across India and neighbouring regions, moving forecasting from "what the weather will be" to "what the weather will do to you."
- The note credits the platform with measurable gains: forecast preparation time cut by 50%, accuracy up 30%, and the warning lead time raised from 5 days to 7 days.
- It also profiles the companion last-mile portal Mausamgram, which delivers hyper-local forecasts down to the pin-code and village level.
- The explainer ties both tools to IMD's larger goal of a "Weather Ready and Climate Smart Nation," captured in the tagline "Har Har Mausam, Har Ghar Mausam."
Background & context
To place MHEW-DSS correctly, an aspirant needs the chain it sits inside. The India Meteorological Department, established in 1875, is the country's principal agency for meteorology, weather forecasting and seismology. It functions under the Ministry of Earth Sciences (MoES), which is the nodal ministry. IMD is also a Regional Specialised Meteorological Centre (RSMC) — one of a handful of WMO-designated tropical-cyclone centres worldwide — and in that role it issues advisories not only for India but for neighbouring countries including Bangladesh, Maldives, Myanmar, Oman, Pakistan, Qatar, Sri Lanka, Thailand, the UAE and Yemen.
MHEW-DSS is the forecasting engine; Mission Mausam is the umbrella programme. Mission Mausam, cleared by the Cabinet in September 2024, is a MoES initiative to make India's weather and climate services more accurate and accessible through better observation, modelling and dissemination — covering tools such as additional radars, wind profilers, radiosondes and a supercomputing-backed forecasting stack. MHEW-DSS predates the formal mission (it went live in January 2024) but now sits as one of its operational pillars. This lineage matters because exam questions frequently pair a platform with the scheme it belongs to.
The deeper backstory is a strategic one: for years India's high-end numerical weather prediction leaned on foreign software and vendors. MHEW-DSS was deliberately built in-house using open-source technology, which the government says removed dependence on foreign vendors and saved roughly ₹250 crore. It is therefore as much an indigenisation story (self-reliance in a critical-data domain) as a disaster-management one.
It also helps to know where this sits in India's disaster-governance architecture. The legal backbone is the Disaster Management Act, 2005, which created a three-tier structure — the National Disaster Management Authority (NDMA) chaired by the Prime Minister at the apex, State Disaster Management Authorities under Chief Ministers, and District Authorities under District Collectors — supported operationally by the National Disaster Response Force (NDRF). In that division of labour, IMD and tools like MHEW-DSS occupy the early-warning and forecasting node: they generate the alert; the NDMA-led machinery converts it into evacuation and relief. The explainer notes that the NDMA and NITI Aayog are among the 200-plus organisations consuming IMD's outputs — a reminder that the forecasting agency feeds, but does not replace, the response system.
A word on how impact-based forecasting actually differs from the older model. Traditional forecasting answers "how much rain, how strong the wind." Impact-based, risk-based warning — the method MHEW-DSS is built around — overlays that weather forecast on local exposure and vulnerability data so the output reads as consequences: which roads may flood, which low-lying habitations are at risk, where fishing should stop. The colour-coded GIS warnings (green/yellow/orange/red) that IMD now issues for districts are the visible front end of this method. This is the same philosophy the World Meteorological Organization has been pushing globally, which is part of why IMD's RSMC role and these tools fit a recognised international standard rather than a one-off national experiment.
For Prelims
- Full name: Multi-Hazard Early Warning Decision Support System (MHEW-DSS) — an advanced GIS-based digital forecasting platform.
- Built by / how: developed in-house by IMD using open-source technology; over 90% of weather-data collection automated and more than 95% of numerical-model inputs drawn into it.
- Launched: January 2024; brought under Mission Mausam (Union Cabinet approval, September 2024).
- Core engine: the Weather Analysis and Forecast Enabling System (WAFES) — the analysis-and-forecast backbone that drives the colour-coded, impact-based, risk-based warnings.
- Performance claims: preparation time down 50% (about 3 hours, from 6); accuracy up 30%; lead time up from 5 to 7 days; warnings reach about 80% of the population.
- Method: moves from ordinary weather forecasting to impact-based forecasting and risk-based warning — i.e., predicting consequences (flooded roads, blown-down structures) rather than only rainfall or wind figures.
- Field record: forecasts in Cyclone Biparjoy and Cyclone Dana were credited with enabling zero casualties in Gujarat and Odisha respectively.
- Companion portal — Mausamgram: a web-based hyper-local portal, also launched January 2024, giving location-specific forecasts up to 10 days for 1.5 lakh+ pin codes, 5,700 blocks and 6.2 lakh+ villages.
- Institutional spine: Ministry of Earth Sciences (nodal) → IMD (est. 1875; RSMC). More than 200 organisations use IMD's applications, including NITI Aayog and the NDMA.
- Recognitions: National Award for e-Governance 2025; the UNDRR Sasakawa Award for Disaster Risk Reduction (2025) to IMD Director-General Mrutyunjay Mohapatra; and the Economic Times GovTech Award 2026.
What it is NOT. MHEW-DSS is not a satellite or a radar — it is the decision-support software layer that ingests data from those instruments and converts it into warnings. It is not the same as Mission Mausam (the umbrella programme) nor the same as Mausamgram (the public-facing dissemination portal); MHEW-DSS is the internal forecaster's engine, Mausamgram is the citizen's window. It is not run by the Ministry of Home Affairs or the NDMA — the nodal ministry is Earth Sciences, with NDMA only a downstream user. And "impact-based forecasting" is not the same as ordinary forecasting: the distinction is exactly what such warnings predict (consequences vs raw weather values).
The full set (so "how many / match the pairs" survive). Keep the MoES weather-and-disaster family together: IMD (forecasting, est. 1875, under MoES) · Mission Mausam (umbrella, Cabinet Sept 2024) · MHEW-DSS (in-house decision-support engine, Jan 2024) · WAFES (its core analysis engine) · Mausamgram (hyper-local public portal, Jan 2024). Adjacent but distinct bodies an aspirant should not confuse with these: the NDMA (apex disaster-management body under the Disaster Management Act, 2005, MHA) and the WMO-linked RSMC role IMD plays for the region. One peer comparison: where a flood or earthquake early-warning system targets a single hazard, MHEW-DSS is deliberately multi-hazard — cyclones, heavy rain, heatwaves, thunderstorms and more on one platform.
Why it matters
India sits on one of the most hazard-exposed footprints on earth: a long cyclone-prone eastern and western coastline, a flood-prone Indo-Gangetic plain, an earthquake-active Himalaya and a rising burden of heatwaves. The classic gap in disaster response has never been knowing a cyclone is coming — it is converting that knowledge into a specific, trusted, last-mile instruction that an ordinary household or a district administrator can act on in time. MHEW-DSS targets precisely that gap. By shifting to impact-based, location-specific warnings and pushing the lead time to seven days, it gives evacuation, fishing-ban and resource-positioning decisions a longer and sharper runway. The claimed outcomes — zero casualties in Cyclones Biparjoy and Dana — are the kind of evidence Mains answers on disaster management reward, because they show a forecasting upgrade translating into lives saved rather than a technical metric in isolation. The indigenisation angle adds a second layer of significance: a sovereign forecasting stack, free of foreign-vendor dependence in a strategically sensitive data domain, with ₹250 crore of recurring savings. Together these make MHEW-DSS a clean example of technology used as a governance instrument — the State delivering a public good (safety) more cheaply and more reliably than before.