11 February 2026·PIB

PARLIAMENT QUESTION: ACCURACY OF WEATHER FORECASTS

In this note

1. At a Glance

Parliament Question answered by Ministry of Earth Sciences (MoES) on 11 Feb 2026, reporting accuracy of India Meteorological Department (IMD) forecasts in 2025 [1].
Topic links science & technology (GS-III) with disaster management and agriculture (monsoon-dependent economy).
Centres on IMD's Long Range Forecast (LRF) accuracy and the umbrella Mission Mausam modernization push [1][2].

MoES tabled data showing 2025 southwest monsoon LRF was highly accurate: April forecast = 105% of LPA (±5%); May update = 106% of LPA (±4%); actuals = 108% of LPA [1][4].
Reinforces government claim of measurable gains from Mission Mausam (₹2,000 crore, approved Sept 2024) [2].

IMD founded 1875; oldest scientific department of GoI; under MoES (formed 2006).
Long Range Forecast for SW Monsoon issued since 1988; two-stage forecast system since 2003 (April first stage; May update).
Multi-Model Ensemble (MME) based dynamical forecasts adopted; Monsoon Mission launched 2012 to develop coupled dynamical model.
Mission Mausam approved by Union Cabinet on 11 Sept 2024 with ₹2,000 crore outlay over 2 years [2].

Implementing Ministry: Ministry of Earth Sciences (NOT MoEFCC) [1][2].
Nodal Agency: India Meteorological Department (IMD) [1].
Mission Mausam implementing institutes: IMD, NCMRWF (National Centre for Medium-Range Weather Forecasting), IITM (Indian Institute of Tropical Meteorology, Pune) [2].
Mission Mausam outlay: ₹2,000 crore over 2 years (2024–2026) [2].
Infrastructure target: 50 Doppler Weather Radars, 60 Radio Sonde/Wind stations, 100 disdrometers, 10 Wind Profilers, 25 radiometers, 1 Urban testbed, 1 Ocean Research station, 10 Marine AWS [2].
LPA (Long Period Average) of SW Monsoon rainfall = benchmark for monsoon classification.
2025 SW Monsoon: April LRF 105% (±5%); May update 106% (±4%); actuals 108% of LPA [1][4].
Forecast track record (2023–2025): all 3 years within model error; average absolute error = 1.9% of LPA [4].

Scientific / Technological

Forecast uses coupled ocean-atmosphere dynamical model under Monsoon Mission + statistical ensemble [4].
Mission Mausam adds AI/ML, high-resolution atmospheric models, cloud chamber, weather modification research [2].
Dense Doppler radar + radiosonde network expansion to plug observational gaps [2].

Economic

~50% of net sown area is rainfed; ~18% of GDP from agriculture depends on monsoon accuracy.
Accurate LRF informs MSP, sowing decisions, reservoir management, crop insurance (PMFBY) payouts.
Better nowcasts reduce disaster losses (cyclones, lightning, urban floods).

Administrative / Governance

IMD issues colour-coded warnings (Green/Yellow/Orange/Red) for districts; integrated with NDMA SOPs.
Common Alerting Protocol (CAP) dissemination via SMS, DD, Mausam app.

Environmental / Climate

Rising extreme rainfall events under climate change demand convective-scale forecasting — core Mission Mausam goal [2].
Supports climate services delivery under WMO Global Framework for Climate Services (GFCS).

Geopolitical / Strategic

IMD provides forecasts to SAARC neighbours via RSMC New Delhi (Regional Specialised Meteorological Centre) for tropical cyclones over North Indian Ocean.

11 Sept 2024: Cabinet approves Mission Mausam (₹2,000 cr) [2].
April 2025: First-stage LRF for SW Monsoon — 105% of LPA [1].
May 2025: Updated LRF — 106% of LPA [1].
Sept–Oct 2025: Actual SW Monsoon = 108% of LPA (above normal) [1].
11 Feb 2026: MoES tables Parliament reply confirming 2025 accuracy and ongoing Mission Mausam rollout [1].

IMD comes under Ministry of Earth Sciences, NOT Ministry of Environment [1].
Mission Mausam outlay: ₹2,000 crore over 2 years [2].
Mission Mausam approved by Cabinet in September 2024 [2].
Three implementing institutes: IMD, NCMRWF, IITM [2].
NCMRWF = National Centre for Medium-Range Weather Forecasting (Noida) [2].
IITM: Indian Institute of Tropical Meteorology, Pune [2].
2025 SW Monsoon actual rainfall: 108% of LPA (above normal) [1].
Model error band for All-India LRF: ±5% of LPA [1][4].
Average absolute forecast error 2023–2025: 1.9% of LPA [4].
Mission Mausam target: 50 new Doppler Weather Radars [2].
IMD founded 1875; RSMC for tropical cyclones over North Indian Ocean is at New Delhi.
Two-stage LRF: first stage in April, update in late May/early June [1].
LPA = average of monsoon rainfall over a fixed reference period (currently 1971–2020 = 87 cm).
"Above normal" = >104% to ≤110% of LPA.

GS-III: Science & Technology — indigenization, achievements of Indians in S&T; Disaster Management; Agriculture & food security.
GS-II: Government policies and interventions (Mission Mausam).
Possible question stems: 1. "Accurate weather and climate forecasts are increasingly central to India's economic resilience. Discuss in light of Mission Mausam." (GS-III) 2. "Examine the role of IMD's Long Range Forecast in mitigating agrarian risk in a climate-stressed India." (GS-III) 3. "Evaluate India's institutional architecture for weather services and identify gaps in last-mile dissemination." (GS-II/III)

Mission Mausam — parent flagship for the forecast accuracy story [2].
Monsoon Mission & Deep Ocean Mission — sibling MoES initiatives.
NDMA & CAP-based early warning — disaster-side counterpart.
PMFBY (Pradhan Mantri Fasal Bima Yojana) — uses IMD data for payouts.
El Niño / La Niña / IOD — drivers of monsoon variability.
WMO and RSMC New Delhi — international framework.
Cyclones in North Indian Ocean (e.g., Biparjoy, Remal) — operational forecast case studies.
Climate Change & Indian agriculture — links accuracy to adaptation policy.

IMD is under MoES, not MoEFCC.
NCMRWF ≠ IITM: NCMRWF does medium-range numerical weather prediction (Noida); IITM does climate/monsoon research (Pune).
LPA is not a fixed number forever — it is revised every decade; current LPA period is 1971–2020 (≈87 cm), not 1951–2000.
"Above normal" vs "Excess": 104–110% is above normal; >110% is excess.
Mission Mausam outlay is ₹2,000 crore over 2 years, not annual.
RSMC for tropical cyclones is at New Delhi, not Pune or Mumbai.