Monsoon Forecasting in India

Let’s imagine you’re a policymaker or a farmer. The most crucial question before the monsoon hits is—“Kitni barish hogi iss saal?” (How much rainfall will there be this year?) And the answer to that question is not just academic—it affects agriculture, food security, GDP, disaster preparedness, and even stock markets.

Ancient Foundations: India’s Early Meteorological Wisdom

Before science as we know it developed, India already had rich textual traditions that hinted at advanced weather knowledge. Consider:

• Upanishads and Brihatsamhita – Spoke of cloud formations and seasonal changes.
• Arthashastra (by Kautilya) – Discussed rain forecasting for agrarian planning.
• Meghdoot (by Kalidasa) – Though poetic, reflects deep understanding of monsoon behavior.

👉 These were not ‘scientific forecasts’ in the modern sense, but they reflect that even ancient India recognized rain as predictable to some degree.

Scientific Meteorology: Arrival of Western Science

Modern meteorology entered the scene in the 17th century with Edmund Halley, who first attempted a scientific explanation of the monsoon as a large-scale sea breeze.

Then came the British era, which brought:

• Observatories (Calcutta, Madras, Bombay).
• Captain Piddington, who coined the term “cyclone” while studying Bay of Bengal storms.

Institutionalization: Birth of Monsoon Forecasting in India

The Great Famine of 1876–78 was a wake-up call.

• British administrators realized the need to predict rainfall to prevent such humanitarian disasters.
• This led to the Indian Meteorological Department (IMD) starting monsoon forecasts in 1877.

Pioneers of Monsoon Science

Let’s now walk through the contributions of key figures:

(a) Henry Francis Blanford (1880s)

• Studied correlation between Himalayan snow cover and Indian rainfall.
• Gave India’s first long-range monsoon forecast in 1886.

(b) Sir John Eliot

• Expanded data scope—factored in Indian Ocean and Australian observations.
• Forecasting improved slightly, but accuracy still limited.

(c) Sir Gilbert Walker (1904 onwards)

• Developed statistical models using 28 parameters.
• Discovered Southern Oscillation (SO) — a key piece of the ENSO puzzle.
• Divided India into three sub-regions to refine forecasts.

Post-Independence Era: From Statistics to Supercomputers

(a) Walker Model Era (Till 1987)

• IMD continued using Walker’s statistical models.
• But changing climate patterns and weakened correlations made forecasts unreliable over time.

(b) Gowariker Regression Model (1988)

• Introduced a new 16-variable regression model.
• Promised better accuracy, but regional performance remained inconsistent.

(c) Ensemble Forecasting Era Begins (2007)

• IMD moved to a Statistical Ensemble Forecasting System.
• Fewer parameters, but combined multiple simulations to improve robustness.

Technological Leap: Coupled Models & Multi-Model Systems

Let’s understand two revolutionary transitions here:

(a) Monsoon Mission – 2012

• Launched by the Ministry of Earth Sciences.
• Introduced the MMCFS – Monsoon Mission Coupled Forecasting System.
  • Integrates atmosphere, ocean, and land data.
  • Uses dynamical models (physics-based, not purely statistical).

(b) Multi-Model Ensemble System – 2021

• Combines multiple global models, including MMCFS.
• Greatly enhanced forecast precision, even for sub-seasonal and regional scales.

Forecast Accuracy: Has It Actually Improved?

Yes. Here’s some concrete evidence:

• Since 2007, the absolute error in monsoon rainfall prediction has reduced by 21%, compared to the 1989–2006 period.
• India’s forecasting capability is now comparable to many developed nations, especially in seasonal and sub-seasonal predictions.

Government Initiatives to Strengthen Forecasting

(a) Monsoon Mission (2012)

• Core goal: scientific advancement in monsoon modeling and forecasting.
• Involved institutions like IITM (Pune), IMD, NCMRWF, and INCOIS.

(b) National Supercomputing Mission

• Supports high-speed computing infrastructure to run complex weather models efficiently.

(c) ICAR–IMD Collaboration

• Provides Agromet Advisory Services to farmers based on weather forecasts.
• A great example of science reaching the grassroots.

Key Takeaway: India’s Monsoon Science is Now Data-Driven, Dynamic, and Evolving

From ancient intuition to coupled dynamical models, India’s journey in monsoon forecasting is a story of:

• Resilience in the face of natural disasters,
• Scientific evolution over centuries, and
• Public utility, especially for sectors like agriculture, disaster management, and rural planning.

Analytical Insight for UPSC:

“Forecasting the monsoon is not merely about predicting rainfall—it is a multi-dimensional exercise involving atmospheric science, oceanography, computing, and human welfare.”