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Mission Mausam is a Central Sector umbrella scheme of the Ministry of Earth Sciences aimed at making India a 'Weather-ready and Climate-smart Nation'; it was approved by the Union Cabinet in 2024 with a ₹2,000 crore outlay for 2024-25 and 2025-26 and launched by the Prime Minister on 14 January 2025.
Its measurable outcomes are a 10-15% improvement in forecast accuracy by 2030, severe weather forecasting at 5 km x 5 km resolution, and last-mile dissemination of early warnings to every household by 2030.
IMD's adoption of Multi-Model Ensemble forecasting has cut the average absolute error of the seasonal monsoon forecast from 7.8% of the Long Period Average in 2016-2020 to 3.1% for the first-stage and 2.2% for the second-stage forecast in 2021-2025.
The observation network now comprises 50 Doppler Weather Radars, 1,008 Automatic Weather Stations and 6,885 rain gauge stations, and the National Seismological Network runs 174 observatories with an earthquake early warning system in the pilot phase.
For 2026-31 the Mission is proposed to run through six verticals — OBSERVE_ALL, DEVELOP, Weather_MOD, ATCOMP, FRONTIER and NEAT — with a Budget Estimate of ₹1,342.29 crore for 2026-27.
Make India a 'Weather-ready and Climate-smart Nation' by strengthening Earth system observation, prediction and early warning
Key: A Central Sector umbrella scheme of the Ministry of Earth Sciences, approved by the Union Cabinet in 2024 with ₹2,000 crore for 2024-25 and 2025-26 and launched on 14 January 2025. It funds next-generation Doppler Weather Radars, wind profilers, satellites with advanced payloads, high-performance computing, Earth system models with AI/ML, weather modification research and decision support systems. Its infrastructure is benchmarked against a gap analysis of India's network against those of major developed countries.
Reduce uncertainty in seasonal monsoon forecasts by combining several coupled dynamical climate models
Key: Cut the average absolute error of the seasonal forecast from 7.8% of the Long Period Average during 2016-2020 to 2.2% at the second stage during 2021-2025, and improved the representation of large-scale drivers such as the El Nino-Southern Oscillation and the Indian Ocean Dipole. IMD issues a first-stage forecast in April, a second stage in May, monthly forecasts for June to September, and an updated August-September forecast in late July.
Turn forecasts into actionable, sector-specific alerts through a single geospatial platform
Key: A Web-GIS platform developed by IMD that integrates geospatial technologies, meteorological observations, numerical weather prediction models and impact-based forecasting for cyclones, heavy rainfall, thunderstorms and other severe weather, and issues sector-specific advisories to central and state authorities, disaster agencies, communities and industry.
Reduce lightning deaths through early warning, risk assessment and community preparedness
Key: A centrally sponsored pilot initiative launched by the National Disaster Management Authority with IMD as technical partner, comprising community-based risk assessment and a Lightning Early Warning System, awareness and capacity building, preparedness and mitigation, lightning R&D, and a Lightning Hazard Management Unit.
Deliver weather advisories directly to panchayat representatives
Key: Integrates weather forecasts with the eGramSwaraj and e-Manchitra platforms and pushes advisories through WhatsApp; IMD's impact-based forecasts also reach farmers through KALP and Mausam SANKALP with crop-specific and crop-stage-specific advisories.
Provide a few seconds of warning before strong ground shaking arrives
Key: Being developed by the National Centre for Seismology in a pilot phase. The method detects the initial, non-damaging primary waves (P-waves) at sensors placed in the source region and issues an alert before the stronger, slower waves reach a target site — the lead time depending on distance from the source.
The national meteorological service — observation, weather forecasting and warnings; runs the DWR, AWS and rain gauge network and the numerical weather prediction suite
Ocean information and advisory services; prepared the Coastal Vulnerability Index for the entire Indian coastline at 1:100,000 and Coastal Multi-Hazard Vulnerability Mapping at 1:25,000; hosts the Indian Tsunami Early Warning Centre, operational since October 2007
Monitors earthquakes across India round the clock through the National Seismological Network of 174 observatories and is developing the earthquake early warning system
Developed the DAMINI lightning alert app, available in 17 regional languages, and the Bharat Forecast System — India's indigenous high-resolution global forecast model that improved resolution from 12 km to 6 km and was adopted for operational use by IMD in 2025
NIOT maintains the Ocean Moored Network for the Northern Indian Ocean (OMNI), coastal buoys, High-Frequency radars — including the pair at Wasiborsi and Jegri in Gujarat — and tsunami buoys. NCCR provides urban rainfall forecasts and flood inundation mapping over about 400 sq km of the Mumbai Municipal Corporation area
The Long Period Average (LPA) is the benchmark rainfall computed over a long reference period; a seasonal forecast is expressed as a percentage of it, and forecast error is measured as the absolute deviation in percentage points of the LPA. A Multi-Model Ensemble runs several coupled ocean-atmosphere models and combines their outputs, so errors peculiar to any single model partly cancel — which is how the average absolute error fell from 7.8% of LPA in 2016-2020 to 2.2% at the second stage in 2021-2025. The Spring Predictability Barrier is the reason a single forecast will not do: models find the evolution of the El Nino-Southern Oscillation hardest to predict across the boreal spring, precisely when a first monsoon forecast must be made. IMD's answer is a sequential strategy — a first-stage forecast in April, an updated second-stage forecast in May once spring conditions have resolved, monthly forecasts through the season, and a fresh August-September outlook in late July.
Simple Analogy: Forecasting the monsoon in April is like calling a match result at the toss; the May update is the call after the first few overs, when the pitch has revealed itself.
Mission Mausam's target of last-mile early warnings to every household by 2030 maps directly onto the global push for universal early warning coverage, a standard disaster management answer.
The improvement in seasonal forecasts is attributed to better representation of these two drivers, tying a scheme question to core physical geography.
Three AI-based global weather models are operational at MoES and hybrid physics-AI techniques are used for nowcasting — one of the few areas where AI has moved into routine government operations rather than pilots.
The INCOIS Coastal Vulnerability Index and Multi-Hazard Vulnerability Mapping feed coastal zone planning and cyclone preparedness, linking this to coastal regulation zone questions.
GS Paper 3 > Disaster and Disaster Management; GS Paper 1 > Important Geophysical Phenomena
General Awareness > Government Schemes and Science
General Awareness > Current Affairs
Monsoon forecasting, IMD and disaster early warning appear in Prelims most years and recur in GS1 and GS3 Mains
The long-term reference rainfall against which seasonal monsoon forecasts and their errors are expressed as percentages.
A forecasting approach combining several coupled dynamical climate models so that individual model errors partly cancel.
The recognised difficulty of forecasting ENSO evolution across the boreal spring, which is why the April forecast is updated in May.
Very short-range forecasting of severe weather, typically over the next few hours, where AI and radar data are most useful.
The international standard for exchanging emergency alerts across channels, on which the SACHET dissemination platform is built.
A forecast expressed in terms of likely consequences for a location or sector rather than only meteorological values.