- Why in news
- On 2 April 2026, PIB published a explainer on India's Multi-Hazard Early Warning Decision Support System (MHEW-DSS), the IMD's digital forecasting platform, listing its results and awards.
- What it is
- MHEW-DSS is a webGIS-based forecasting platform built in-house by the India Meteorological Department using open-source technology. It automates data collection, model comparison and warning generation in one system. It issues colour-coded, impact-based warnings for cyclones, heavy rain, heatwaves, thunderstorms and marine conditions.
- Developer and launch
- IMD built it in-house under Mission Mausam; it was launched in January 2024.
- Core of the system
- The Weather Analysis and Forecast Enabling System (WAFES) lets forecasters analyse data and visualise weather on GIS maps.
- Mission Mausam
- The Union Cabinet approved Mission Mausam in September 2024 to improve the accuracy, resolution and lead time of forecasts.
- Mausamgram
- A hyper-local forecast portal under the system, giving forecasts up to 10 days for over 6.2 lakh villages.
- Impact-based forecasting
- The system assesses how severe weather may affect sectors, not only the weather itself, and uses colour-coded warnings.
- IMD as RSMC
- IMD is a Regional Specialized Meteorological Centre giving cyclone advisories to countries around the North Indian Ocean.
- IMD and MoES
- IMD was established in 1875 and works under the Ministry of Earth Sciences.
Don’t confuse with MHEW-DSS vs Mausamgram: MHEW-DSS is the overall forecasting and warning platform; Mausamgram is one application under it for hyper-local forecasts.
Don’t confuse with MHEW-DSS vs Mission Mausam: Mission Mausam is the Cabinet-approved mission (Sept 2024); MHEW-DSS is the digital system launched in January 2024.
Science and technology: 57 Prelims questions since 2013; most test the concept behind the news, not the news itself. In Mains, 12 news-triggered questions.
Why it matters
- Impact-based, location-specific warnings help early evacuation and cut disaster losses.
- In-house, open-source development reduces dependence on foreign vendors and supports self-reliance.
Problems
- Warnings must reach last-mile users, such as farmers and fishers, in local languages and in usable form.
- Accuracy of extreme-event forecasts depends on dense observation networks and modelling capacity.
Way forward
- Expand observation networks and improve modelling under Mission Mausam.
- Link warnings to local response plans through state and district disaster authorities and panchayats.
Put in your answer
Cyclone outcomes: Accurate forecasts during Cyclones Biparjoy and Dana enabled timely evacuation, with zero casualties in Gujarat and Odisha.
Lead time: Forecast lead time rose from 5 days to 7 days.
Cost savings: The system has generated about ₹250 crore in savings and removed dependence on foreign vendors.
- 2022Mains GS1 · Q5Discuss the meaning of IMD's colour-coded weather warnings for cyclone-prone areas.
- 2020Mains GS3 · Q18Discuss recent Indian disaster management measures moving from reactive to proactive approach.
- 2020Prelims Q99Which statements on jet streams and cyclone eyes and temperature are correct?
- 2026Prelims Q84Which statements about the indigenous Bharat Forecast System weather model are correct?
UPSC’s own questions that come close to this issue, from our Prelims and Mains banks.
No practice question for this one; the card may still help an answer. Why: Lasting disaster-management topic on IMD forecasting; few testable facts in the text, so no practice question.
Worth revising until Prelims 2027: most news questions come within two years of the event.