The History and Evolution of Monsoon Forecasting in India
– Understanding Climate Science, Forecasting Systems, and Legal Relevance
Introduction
India’s monsoon is not just a weather event—it is the backbone of Indian agriculture, economy, water supply, and rural livelihoods. With nearly 70% of the annual rainfall occurring during the June–September southwest monsoon season, accurate forecasting is crucial.
The India Meteorological Department (IMD) recently predicted a “normal” monsoon for 2025. Behind this seemingly simple forecast lies a rich history of scientific exploration dating back to the 19th century. From colonial-era models to today’s AI-enhanced simulations, the IMD’s journey of improvement reflects advances in science, technology, and public policy.
This summary is especially useful for CLAT aspirants, where environmental governance, climate science, and institutional evolution are frequently tested in legal reasoning and GK sections.
Why in News?
- On April 15, 2025, IMD released its first long-range forecast of the year.
- It predicted “normal to above-normal” rainfall (105% of Long Period Average, or LPA).
- The article explains how forecasting methods have evolved over the decades and how India continues to refine its models.
Point-wise Summary of the Article
- IMD’s Recent Forecast (April 2025):
- Forecast: 105% of LPA, meaning above-normal monsoon expected.
- LPA = 87 cm of rainfall, calculated over a 50-year average.
- Forecast critical for planning agriculture, irrigation, disaster management, and economic strategy.
- Importance of Monsoon Forecasting:
- Monsoon contributes ~70% of annual rainfall.
- Impacts agriculture, power generation, reservoir management.
- Accurate forecasts help prevent disasters and economic shocks.
- Historical Beginnings (1870s–1880s):
- First forecast: 1877, during British rule.
- Scientist Henry Blanford, IMD’s first meteorological reporter, linked Himalayan snow cover to monsoon strength.
- Failure of monsoon in 1876–78 led to Great Famine; prompted urgency in weather prediction.
- Evolution of Predictive Models:
- Gilbert Walker (1904): Introduced statistical models based on global pressure patterns (e.g., ENSO, NAO).
- Identified 28 global predictors of rainfall.
- His theories formed the base of long-range forecasts until the late 20th century.
- From Statistics to Simulation:
- Statistical models dominated until the 1990s.
- However, forecasts often missed critical monsoons (e.g., failure in 2002).
- The IMD was forced to rethink its strategy.
- Introduction of New Models:
- Gowariker Model (1988):
- First Indian-developed empirical model using 16 parameters.
- Used extensively through the 1990s.
- Later replaced due to its limited adaptability.
- Statistical Ensemble Forecasting System (2007):
- Adopted two-stage forecasting:
- April (initial)
- June (updated)
- Reduced forecasting parameters for better accuracy.
- Coupled Dynamic Model (2012):
- Part of Monsoon Mission.
- Uses real-time ocean-atmosphere interactions.
- Integrated with international models (e.g., NOAA, UKMO).
- Widely used for short and medium-range forecasts.
- MME (Multi-Model Ensemble, 2017):
- Combines outputs of multiple models for higher accuracy.
- Integrated domestic and global datasets.
- Currently the backbone of India’s monsoon predictions.
- Performance Analysis:
- IMD predicted drought correctly in 2015 and 2019.
- Missed 2002 and 2004 monsoons.
- Still scope to improve model tuning and integration.
Relevance for CLAT 2026 Aspirants
This topic holds value in:
Exam Section | How it Applies |
Legal Reasoning | Environmental policies, disaster laws, and IMD’s role in governance |
Current Affairs | Climate prediction, government programmes, and Monsoon Mission |
GK/Science Tech | Understanding climate systems and their socioeconomic impact |
Essay/Interview | Questions on disaster preparedness, environmental legislation, scientific governance |
Notes and Key Terms
Term | Explanation |
IMD | India Meteorological Department, under Ministry of Earth Sciences |
LPA (Long Period Average) | Average rainfall over a 50-year period used as benchmark |
ENSO (El Niño–Southern Oscillation) | Irregular warming of the central/eastern Pacific that affects monsoons |
IOD (Indian Ocean Dipole) | Indian Ocean phenomenon affecting rainfall patterns |
NAO / NPO | North Atlantic and North Pacific Oscillations—global pressure patterns influencing Indian weather |
Statistical Forecasting | Based on past correlations, not real-time conditions |
Dynamical Model | Uses physics-based simulations of atmosphere and ocean |
Conceptual Understanding for Law Aspirants
- IMD’s Legal Role:
- As a government institution, IMD’s data influences:
- Disaster response (Disaster Management Act)
- Agricultural support policies (Minimum Support Prices)
- Water usage planning (River Basin Management)
- Constitutional Connection:
- Article 21 – Right to Life includes protection from environmental disasters.
- Article 48A – Directive Principle to protect the environment.
- Schedules VII (Union List) – Meteorology and weather fall under central jurisdiction.
Broader Implications
- Climate Adaptation Strategy: India must continuously adapt forecasting systems as climate variability increases.
- Food Security: Farming relies heavily on timely monsoon prediction.
- Technology Diplomacy: India collaborates globally (e.g., UK, USA) on climate modeling and ocean observation.
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Conclusion
India’s journey in monsoon forecasting—from Blanford’s snow theory to today’s AI-integrated models—shows how scientific institutions evolve to serve national needs. With agriculture, health, infrastructure, and disaster response all dependent on accurate monsoon forecasts, this field blends science with law, policy, and planning.
CLAT 2026 aspirants must recognize the role that institutions like the IMD play in nation-building. Understanding how laws and forecasts work together prepares you not only for the exam but for a career in legal policymaking.
Study climate + law together, only at CLAT Gurukul – the best online coaching for CLAT.
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