Mission Mausam(2024-26): From Weather-Aware To Weather Ready Nation

Policy Update

Ravirala Lahari

Background

India’s tropical climate makes it highly vulnerable to extreme meteorological events, complex monsoon patterns, high humidity, and rapidly changing weather conditions, making accurate weather prediction particularly challenging. To strengthen the country’s forecasting capabilities, the Ministry of Earth Sciences (MoES) introduced Mission Mausam, a central sector scheme approved by the union cabinet on 11 September 2024 with an outlay of ₹2,000 crore over two years (2024-26). It was formally inaugurated on 14 January 2025, the 150th anniversary of the India Meteorological Department (IMD) (MINISTRY OF EARTH SCIENCES, 2024). 

The introduction of Mission Mausam is driven by the following reasons

• Agrarian Vulnerability: Sowing, irrigation, and harvest decisions depend on rainfall timing, and erratic rainfall raises farmers’ vulnerability. Over 45% to 50% of India’s workforce depends on agriculture, with nearly 52% of net sown areas dependent on monsoon rainfall (Rain Dependence Poses Key Challenge to India’s Rural Economy: UN Official, 2026) .
• Economic Losses from Disasters: Cyclones, floods, heatwaves and cloudbursts are increasingly frequent under climate change. Severe climate events account for billions of dollars in infrastructure damage annually (The World Bank, 2025) Extreme weather events and their economic impact). 
• Gaps in Localized Forecasting: The operational global models ran at 12 km, conventional meso-scale numerical models struggled to predict hyper-local convective events.

Mission Mausam aims to make India “weather-ready and climate-smart” through better observation, modelling and last-mile delivery of forecasts. Phase I runs for two years (2024-26), Phase II is planned for 2026-31. The long-term vision is village-level forecasts and impact-based warnings that help sectors and disaster managers act early and build resilience to extreme weather. 

Functioning 

Mission Mausam operates through an integrated, multi-institutional architecture spearheaded by MoES. 

Institutional Roles

1. India Meteorological Department (IMD): Serves as the primary operational agency for deployment of Doppler Weather Radars (DWRs), Automatic Weather Stations (AWS), and public/sectoral alert dissemination.
2. National Centre for Medium-Range Weather Forecasting (NCMRWF): Leads high-performance computing (HPC) modeling, data assimilation, and supercomputing infrastructure.
3. Indian Institute of Tropical Meteorology (IITM): Focuses on core scientific research, cloud physics, Earth system modeling, and experimental weather modification techniques.
4. Allied Agencies:
   • INCOIS: Handles ocean-state predictions, tsunami warnings, and storm surge modeling.
   • NIOT & NCPOR: Support marine observations and polar atmospheric studies.

Components: The mission works through six linked components

1. Next-generation radars, wind profilers, and satellite payloads.
2. High-performance computing.
3. Earth-system models, including AI/ML.
4. Weather-management technologies and protocols.
5. A Decision Support System (DSS) and last-mile dissemination.
6. Capacity building and research collaboration.

Dissemination & Alert Architecture

To ensure broad public reach, IMD converts complex meteorological data into impact-based, district-level warnings via multiple digital channels:

• Government Protocols: Alerts are broadcasted using the Common Alerting Protocol (CAP) and delivered via SMS, the National Disaster Management Authority’s (NDMA) SACHET app, and the centralized UMANG platform.
• Public Ecosystems & Open Data: Integration with Apple and Google systems ensures native mobile alerts, while a public Application Programming Interface (API) allows third-party organisations to relay IMD forecasts.
• Specialised Digital Portals: Target-specific apps provide dedicated tracking, including MAUSAM (general weather), MEGHDOOT (agromet data), and DAMINI (lightning safety)(Bansal & Nath, 2025).

Last-Mile Delivery Design

The mission’s most distinctive feature is its deep integration into local governance and rural networks to ensure actionable alerts reach end-users:

• Local Governance Integration: The Panchayat Mausam Seva Portal (developed with the Ministry of Panchayati Raj) directly feeds weather data into rural governance platforms like e-GramSwaraj, the Meri Panchayat app, and e-Manchitra mapping tools.
• Community-Led Extension: Trained rural women cross-mobilized as Krishi Sakhis (agriculture companions) and Pashu Sakhis (livestock companions) under the Ministry of Rural Development manually carry these weather advisories directly to individual farmers.
• Precision Agriculture: The KALP (Krishi Advisory based on Location-Specific Weather Prediction) system translates raw data into highly customized location, crop, and crop-stage-specific advisories, while Mausam SANKALP provides the underlying agro-analytics and long-term planning tools.

Performance

The following table compares selected mission targets with the implementation status reported in the cited sources. These indicators primarily reflect implementation progress and technical capability, they should not be interpreted on their own as evidence of reduced disaster losses or improved livelihoods or as actual outcomes.

Table 1: Mission Mausam Phase I: Targets and Status (as of July 2026) 

Source: Compiled from Ministry of Earth Sciences, Mission Mausam (2024) for targets, Press Information Bureau, “Parliament Question: Implementation of Mission Mausam” (22 July 2026) for current status.

Impact: weather as a tool, not a threat

The mission’s documented impact lies in forecasting capability, services and outreach. Outcome evidence, such as lives saved or crop losses avoided, is not yet available.

AI-driven weather forecasting

The main change is that forecasts are now finer, faster and more local. Heavy-rain events that older models smoothed over can now be picked up at block and panchayat-cluster scale, and forecasters have AI guidance alongside physics-based models when judging severe weather. The new weekly block-level monsoon outlook and the pilot 1 km forecast for Uttar Pradesh extend this to planning horizons of weeks. Because the system was built in India and tuned for the Himalayas and Western Ghats, the country relies less on foreign global models for its own terrain(AI-enabled Systems Introduced by IMD to Provide Hyper-Local Weather Forecasts: Dr Jitendra Singh, PIB 2026) .

Agriculture

Weather information now reaches farmers at the level where they make decisions. Instead of a district forecast, a farmer can check a village forecast for sowing, irrigation or harvest timing. Panchayat functionaries and Krishi Sakhis act as local relays, so advisories no longer depend on a farmer owning a smartphone. In southern Tamil Nadu, the GKMS project shows the model at work, delivering crop-specific advisories and extreme-weather alerts through SMS, voice mail and community meetings(Mission Mausam Scheme – GKMS Project (GIA)). The AI advisory work now being planned could make this advice crop- and stage-specific at national scale.

Disaster preparedness

Warnings now say what the weather will do, not just what it will be, which helps authorities decide where to act. Short-range nowcasts give local teams time to respond to sudden storms, and alerts reach people through phones and major platforms instead of only official channels. In Uttar Pradesh, denser sensors and radars have improved monitoring of thunderstorms, hail and lightning in farm-heavy districts. In cities, the Mumbai study shows how new observation tools are beginning to explain, and eventually help predict, extreme urban rainfall.

Urban Applications and Scientific Research 

Ceilometer data was used to analyse the May 2025 Mumbai extreme rainfall, which the authors call a first for India (Natural Hazards, 2026). ICAR-CRIDA and ICRISAT are working on AI-based advisories at scale.

 Local governance

Forecasts now flow to panchayats through e-GramSwaraj and the Panchayat Mausam Seva Portal. The mission has improved how forecasts are made and delivered. It has not yet shown what those forecasts change for farmers and disaster-prone communities.

Challenges

1. Slow delivery and unclear reporting: Wind profilers and radiometers were not installed as of Dec 2025, and 86 more radars were still “ongoing” in July 2026. Radar targets also vary across documents (50, 53, 86, 87), and there is no public report on spending against the ₹2,000 crore outlay.
2. Observation gaps in difficult terrain: Remote, hilly and North-East regions are hard to cover and maintain with radars and automatic weather stations.
3. Forecast-to-farm gap: Extension workers often cannot interpret forecasts for local use, about 25 agricultural universities have no agromet component, and farmers receive overlapping advisories. Forecasts also need to give clear actions, not just probabilities.
4. Weak data and impact evidence: Local climate data is thin, and there is no method yet to measure how forecasts change yields, incomes or losses. Official claims are therefore about outreach, not outcomes.
5. The Last-Mile Communication Gap: Sophisticated probabilistic forecasts do not automatically trigger local action unless translated into simple, actionable alerts for farmers and village panchayats. For example, a forecast that says “there is a 70% probability of heavy rainfall in the next 24 hours” may be scientifically useful, but a farmer may not know whether to harvest paddy immediately, postpone pesticide spraying, or protect stored grain. Similarly, a village panchayat needs clear instructions on drain clearance, waterlogging precautions, and public alerts. 
6. Limits of technology: AI models face sparse historical data, limited computing and expertise, and “black-box” trust issues. BharatFS is built for short- and medium-range forecasts, and its 30% gain applies to extreme rainfall only.
7. Governance gaps: The mission promises protocols for weather management, but no legal framework exists for approvals, ecological monitoring or downwind effects. Coordination between IMD warnings and district or state action is also unclear.
8. Trust and last-mile resilience: False alarms can reduce compliance, and mobile networks can fail during disasters, cutting off app-based warnings.

Way forward

1. Publish a progress report: The Ministry of Earth Sciences should give instrument-wise status against each target, state-wise radar installation dates, and spending against ₹2,000 crore, and confirm the Phase II outlay and scope.
2. Close the observation gaps: Complete the wind profiler, radiometer and radiosonde components, and prioritise radar siting in the Himalayas, Western Ghats and North-East.
3. Fund the human layer: Train extension workers, panchayat functionaries and Sakhis to turn forecasts into specific actions, and add agrometeorology to the SAU curriculum.
4. Measure outcomes: Commission independent verification of forecast skill and studies of farm-level impact (yields, input costs, losses avoided) in pilot districts such as the GKMS area.
5. Hyper-local, impact-based advisories: Replace probabilities with action-oriented guidance (for example, “heavy rain in this ward from 2 to 4 PM; delay sowing”). Deliver it through WhatsApp, SMS and panchayat notice boards, and give administrators GIS maps of vulnerable roads and drains. 
6. Strengthen Grassroots Dissemination: Integrate Mission Mausam outputs with local Panchayati Raj institutions, Krishi Vigyan Kendras (KVKs), and mobile apps (e.g., Mausam, Meghdoot, Damini) to close the last-mile gap. 
7. Set the rules before weather management scales: Publish protocols covering approvals, environmental monitoring and downwind or inter-state effects.

References  

1. IMD’s new AI-Driven Weather Forecasting systems. (n.d.). Drishti IAS. https://www.drishtiias.com/daily-updates/daily-news-analysis/imds-new-ai-driven-weather-forecasting-systems 
2. India has significantly expanded its Doppler Weather Radar (DWR) network since 2014, increasing from 14 operational units to 50, marking over 250% rise: Dr Jitendra Singh. (n.d.).  https://www.pib.gov.in/PressReleasePage.aspx?PRID=2256637&reg=48&lang=2 
3. Indian National Centre for Ocean Information Services. (n.d.). INCOIS | Ocean Information Services. https://incois.gov.in/site/mausam_misson.jsp 
4. MINISTRY OF EARTH SCIENCES. (2024). MISSION MAUSAM. https://mausam.imd.gov.in/event/mission_mausam.pdf 
5. MISSION MAUSAM. (2024, December 23). PIB. https://www.pib.gov.in/PressReleseDetailm.aspx?PRID=2087273&reg=48&lang=2 
6. Mission Mausam | India Science, Technology & Innovation – ISTI Portal. (n.d.).   https://www.indiascienceandtechnology.gov.in/st-visions/national-mission/mission-mausam 
7. PARLIAMENT QUESTION: BHARAT FORECAST SYSTEM. (n.d.). https://www.pib.gov.in/PressReleasePage.aspx?PRID=2147258®=48&lang=2 
8. PARLIAMENT QUESTION: IMPLEMENTATION OF MISSION MAUSAM. (n.d.). https://www.pib.gov.in/PressReleasePage.aspx?PRID=2287610®=48&lang=2 
9. PROCEEDINGS & RECOMMENDATIONS BRAINSTORMING WORKSHOP on MISSION MAUSAM : FORECAST TO FIELD (TECHNIQUES AND TARGETS FOR AMRITKAAL). (2024). [PROCEEDINGS & RECOMMENDATIONS]. In Mission Mausam (p. 675). https://www.agrimetassociation.org/file/267378582Final_Proceedings_BSW_MM_29_Dec.pdf 
10. Timely Localised Weather Information for 2.6 Lakh Panchayats is Essential for Nation’s Growth, Says Dr. Jitendra Singh. (n.d.). https://www.pib.gov.in/PressReleasePage.aspx?PRID=2067907®=48&lang=2 
11. Agromet Advisory Services Division, New Delhi, Agrimet Division, Pune, Rajeevan, M., & Mohapatra, M. (2020). Standard Operating Procedure for Agromet Advisory Services. India Meteorological Department. https://mausam.imd.gov.in/imd_latest/contents/pdf/gkms_sop.pdf 
12. AI-enabled Systems introduced by IMD to provide Hyper-Local Weather forecasts: Dr Jitendra Singh. (n.d.). https://www.pib.gov.in/PressReleasePage.aspx?PRID=2260258®=3&lang=1 
13. Bansal, P., & Nath, S. (2025). ROLE OF COMMON ALERT PROTOCOL-BASED WARNING DISSEMINATION SYSTEM AND EFFECTIVE DISSEMINATION METHODOLOGY. MAUSAM, 76(4). https://doi.org/10.54302/mausam.v76i4.7118 
14. Rain dependence poses key challenge to India’s rural economy: UN Official. (2026, October 9). BusinessLine. https://www.thehindubusinessline.com/economy/agri-business/rain-dependence-poses-key-challenge-to-indias-rural-economy-un-official/article71562798.ece 
15. The World Bank. (2025). Extreme weather events and their economic impact. https://fincomindia.nic.in/asset/doc/commission-reports/16th-FC/studies/commission/Extreme%20Weather%20Events%20and%20Their%20Economic%20Impact%20by%20WB.pdf?utm_source=chatgpt.com 
16. Mission Mausam Scheme – GKMS Project (GIA). (n.d.). Mssrf. https://www.mssrf.org/projects/mission-mausam-scheme-gkms-project-gia 

About the Contributor

Lahari is a Public Administration and Governance postgraduate from the Central University of Karnataka, with a keen interest in Urban Governance, Public Policy, and Sustainable Development. Committed to making governance more inclusive, sustainable and having a tangible impact on citizens’ lives.

Reviewed by 

Himanshi singh and Gargi 

Acknowledgement 

I am writing to express my sincere gratitude to IMPRI (Impact and Policy Research Institute) for providing me with the opportunity to prepare this policy update article and for fostering a rigorous learning environment that connects research with public policy practice.

Disclaimer

The views and opinions expressed in this article are solely those of the author and do not necessarily reflect the views, policies, or positions of IMPRI (Impact and Policy Research Institute) or any other affiliated organisation.

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