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Under Mission Mausam, IMD's observational network now has 50 Doppler Weather Radars, 1,008 Automatic Weather Stations and 6,885 rain gauge stations.
IMD forecast the 2026 southwest monsoon at 92% of the Long Period Average in April and revised it to 90%; cumulative rainfall was 12% below normal as on 2 August 2026.
Weak El Nino conditions formed over the equatorial Pacific in June 2026 and strengthened to moderate, suppressing monsoon circulation.
The National Seismological Network has grown from 86 observatories in 2014 to 174, of which 89 are in high-risk Seismic Zones IV and V.
AI/ML systems including meteoGAN, MausamVani and a CNN-based bias correction model for the Bharat Forecast System are being folded into operational forecasting.
IMD does not forecast rainfall in millimetres for the public; it forecasts it as a percentage of the Long Period Average, the average seasonal rainfall over a long reference period. That single number carries a category: broadly, rainfall between 96% and 104% of LPA is 'normal', 90-96% is 'below normal', below 90% is 'deficient', 104-110% is 'above normal' and above 110% is 'excess'. So a forecast of 92% of LPA is an explicit statement that the season will be below normal, and the revision to 90% pushed it to the edge of deficient. The forecast is issued in stages — a First Stage Long Range Forecast in April and a Second Stage update, built from coupled dynamical climate models plus statistical techniques that ingest sea surface temperatures, atmospheric circulation, the El Nino-Southern Oscillation and the Indian Ocean Dipole. El Nino is the warm phase of ENSO, an anomalous warming of the central and eastern equatorial Pacific that shifts the tropical convection pattern eastward and typically weakens the monsoon circulation over India. The Indian Ocean Dipole is the analogous east-west temperature gradient across the Indian Ocean; a positive IOD often offsets an El Nino, but in 2026 the IOD stayed neutral, leaving the El Nino signal unopposed. Two cautions the reply itself makes: the all-India figure hides spatial variation — July 2026 was 1% above LPA nationally while 47% of districts remained deficient — and the El Nino relationship is much weaker and less consistent for the North-East, where Bay of Bengal moisture transport and orography dominate.
Simple Analogy: The LPA percentage is like a school report given as 'percentage of class average' rather than raw marks: 90% tells you the season underperformed without you needing to know the millimetre baseline.
Make India 'weather ready and climate smart' by upgrading observation, modelling, forecasting and dissemination
Key: Approved by the Union Cabinet on 11 September 2024 with an outlay of Rs 2,000 crore over two years, implemented by the Ministry of Earth Sciences through IMD, IITM Pune and NCMRWF, with support from INCOIS, NCPOR and NIOT. Its slogan is 'Har Har Mausam, Har Ghar Mausam'
Bring real-time monitoring and warning for multiple hazards onto a single digital platform
Key: Developed by IMD using open-source technology and in-house expertise; AI/ML-derived products are integrated into this GIS-based system
Raise the spatial resolution of numerical weather prediction to sub-district level
Key: A high-resolution model running at 6 km, aimed at block and panchayat level forecasts, supported by the 'Arunika' and 'Arka' high performance computing systems; a CNN-based AI model has been built to correct its rainfall bias
Fuse every observation stream into impact-based forecasts and risk-based warnings
Key: Under development at IMD, integrating satellite data, Doppler radar, in-situ observations, Automatic Weather Stations, ocean and river observations, physics-based models and AI forecasting systems
Deliver weather advisories in Indian languages
Key: MausamVani is a Retrieval-Augmented Generation platform using Generative AI and Large Language Models to convert real-time forecasts into localised regional-language advisories; MausamGPT, under development, will generate concise multilingual summaries. Bhashini is used for multilingual dissemination
Certify community-level tsunami preparedness
Key: 26 coastal villages in Odisha have been recognised; INCOIS serves as Tsunami Service Provider for 28 Indian Ocean Rim nations and disseminates alerts through the Sachet platform and the SAMUDRA app
Fund India's polar, Himalayan and cryosphere research
Key: Rs 270.00 crore proposed for the National Centre for Polar and Ocean Research in 2026-27; NCPOR monitors eight benchmark glaciers in Himachal Pradesh and Arunachal Pradesh from the 'Himansh' high-altitude station at 4,080 metres
National meteorological service; issues forecasts, colour-coded impact-based warnings and the Long Range Monsoon Forecast; operates the Doppler radar and AWS network and the MHEW-DSS
Runs the operational Mithuna Global Numerical Weather Prediction System and integrates AI/ML forecast guidance with data assimilation, coupled Earth System modelling and ensemble prediction
Hosts a dedicated AI/ML Centre and a Virtual Centre under Mission Mausam; runs the Metropolitan Air Quality and Weather Forecasting Services project and the Gangotri-Gaumukh and Satopanth glacier observation experiment with a continuous station at Bhojbasa at about 3,500 metres
Designated Tsunami Service Provider for 28 Indian Ocean Rim nations; issues alerts via Sachet and the SAMUDRA app and projects sea-level rise under the Deep Ocean Mission
Operates the National Seismological Network, expanded from 86 observatories in 2014 to 174, with 89 in Seismic Zones IV and V
Long-term monitoring of eight benchmark glaciers in Himachal Pradesh and Arunachal Pradesh from the 'Himansh' station at 4,080 metres; funded under the PACER scheme
Maintains the ocean observational network of OMNI moored buoys, coastal HF radars and tsunami buoys
Carries out operational flood forecasting, combining IMD rainfall forecasts with river gauge data and hydrological models — the division of labour is that IMD forecasts rain and CWC forecasts floods
| Hazard | Agency responsible | System or platform |
|---|---|---|
| Rainfall, cyclones, heat and cold waves | India Meteorological Department | Colour-coded impact-based warnings; MHEW-DSS; BharatFS at 6 km |
| Floods | Central Water Commission, using IMD rainfall input | Hydrological models plus river gauge data; 25 Flood Meteorological Offices supply 7-day QPFs across 220 sub-basins |
| Tsunamis | INCOIS, Hyderabad | Tsunami Service Provider for 28 Indian Ocean Rim nations; Sachet and the SAMUDRA app |
| Earthquakes | National Centre for Seismology | 174-observatory National Seismological Network; detection threshold M3.0 nationally, M2.5 in Delhi-NCR and the North-East |
| Medium-range numerical guidance | NCMRWF | Mithuna Global NWP System, with experimental AI forecasts initialised from Mithuna-GLB analysis |
A monsoon at 90% of LPA with moderate El Nino translates into prolonged dry spells, low soil moisture, stress on rain-fed crops and lower reservoir storage — the transmission channel from an ocean anomaly to food prices
NCPOR's eight benchmark glaciers, IITM's Gangotri-Gaumukh transect work and NCESS's Garhwal Himalaya hazard modelling together form India's glacier and cascading-hazard research base, directly relevant to GLOF risk
INCOIS projects 0.62-0.87 m of mean sea-level rise along the Indian coast by 2100 under a high-emission scenario, the quantitative basis for coastal regulation zone and adaptation debates
meteoGAN downscaling to 300 m, LLM-based MausamVani advisories in regional languages and Bhashini integration are a working case study of AI deployed for last-mile public service delivery
The shift from hazard forecasts to 'impact-based forecasts and risk-based warnings' is the operational expression of the Sendai Framework's emphasis on early warning and preparedness
The concentration of 89 of 174 seismological observatories in Zones IV and V reflects India's seismic zoning map, in which Zone V covers the North-East, Kutch, parts of Himachal and Uttarakhand and the Andaman and Nicobar Islands
GS Paper 1 > Geography > Indian Monsoon; GS Paper 3 > Disaster Management > Early Warning Systems
General Awareness > Geography and Environment > Monsoon and Weather Institutions
General Awareness > Current Affairs and Science
The monsoon, ENSO/IOD and India's weather institutions appear almost every year in UPSC Prelims and are recurring Mains themes
The long-term average of seasonal rainfall against which IMD expresses its monsoon forecast; 96-104% of LPA is normal, 90-96% below normal and below 90% deficient
The coupled ocean-atmosphere oscillation in the equatorial Pacific; its warm phase, El Nino, is generally associated with below-normal southwest monsoon rainfall over most of India
The east-west sea surface temperature gradient across the tropical Indian Ocean, which can reinforce or offset the El Nino signal; it was neutral during the 2026 monsoon
The share of actual extreme events that a forecast system successfully warned about — the key skill measure for extreme-event forecasting, alongside the False Alarm Ratio and Critical Success Index
A forecast of the amount of rainfall expected over a defined area and period; 25 Flood Meteorological Offices issue daily 7-day QPFs across 220 sub-basins for the Central Water Commission
IMD's high-resolution numerical weather prediction model running at 6 km, designed to deliver forecasts down to block and panchayat level