Evolution of Monsoon Forecasting in India

The Indian Meteorological Department (IMD) has made advancements in monsoon forecasting. Recently, it predicted above-normal rainfall for the southwest monsoon season. This forecast is crucial as the monsoon season accounts for nearly 70% of India’s annual rainfall. Accurate predictions help in agricultural planning and managing water resources.

Historical Context of Monsoon Forecasting

The systematic effort to forecast monsoon rainfall in India began in 1877. This was spurred by the devastating Great Famine of 1876-78. The British administration recognised the need for reliable rainfall predictions to safeguard agricultural production. Henry Francis Blanford was the first Meteorological Reporter to the Government of India. He initiated forecasts based on the relationship between Himalayan snow cover and monsoon rainfall.

Development of Forecasting Models

Blanford’s early forecasts (1882-1885) were based on snow accumulation patterns. In 1886, he provided the first long-range forecast for India and Burma. His successor, Sir John Eliot, expanded the model by incorporating local weather conditions. However, predicting droughts remained a challenge. Sir Gilbert Walker later improved forecasting accuracy by identifying 28 parameters linked to monsoon variability.

Post-Independence Forecasting

After India’s independence, the IMD continued using Walker’s model until 1987. This model had errors. In 1988, the IMD introduced a new power regression model developed by Vasant R. Gowariker. This model used 16 atmospheric variables but faced similar accuracy issues.

Introduction of New Models

In 2003, the IMD launched two new forecasting models with fewer parameters. A two-stage forecast strategy was adopted. The first forecast was made in April, with an updated forecast in June. Despite initial successes, these models struggled to predict severe droughts.

Statistical Ensemble Forecasting System

In 2007, the IMD implemented the Statistical Ensemble Forecasting System (SEFS). This reduced the number of parameters and introduced ensemble forecasts. The new system improved accuracy . The average absolute error between forecasts and actual rainfall decreased notably.

Modern Forecasting Techniques

The Monsoon Mission Coupled Forecasting System (MMCFS) was introduced in 2012. This model integrates data from the ocean, atmosphere, and land. In 2021, the Multi-Model Ensemble (MME) approach further enhanced prediction accuracy using global climate models.

Current Status and Future Directions

As of 2025, the IMD’s forecast accuracy has improved by 21% compared to previous decades. The April forecasts have also shown a marked improvement. However, there is still room for enhancement, particularly in refining dynamical models and understanding global climate influences.

Questions for UPSC:

1. Examine the impact of monsoon variability on Indian agriculture and economy.
2. Discuss the historical significance of the Great Famine of 1876-78 in shaping meteorological policies in India.
3. Critically discuss the role of statistical models in improving weather forecasting accuracy.
4. With suitable examples, discuss the influence of global climate patterns on regional weather phenomena.

Answer Hints:

1. Examine the impact of monsoon variability on Indian agriculture and economy.

1. Monsoon variability directly influences crop yields, affecting food security and farmer incomes.
2. It accounts for approximately 70% of India’s annual rainfall, crucial for irrigation.
3. Erratic monsoons can lead to droughts or floods, impacting agricultural production and economic stability.
4. The agricultural sector is part of the Indian economy, employing a large portion of the population.
5. Monsoon patterns affect water resource management, impacting reservoirs and aquifers, critical for both agriculture and urban areas.

2. Discuss the historical significance of the Great Famine of 1876-78 in shaping meteorological policies in India.

1. The Great Famine brought into light the need for reliable rainfall predictions to prevent agricultural crises.
2. It prompted the British administration to establish systematic meteorological efforts, leading to the formation of the IMD.
3. Henry Francis Blanford’s work in monsoon forecasting was initiated to mitigate similar future famines.
4. The famine’s impact on revenue generation for the British telld the economic importance of accurate weather predictions.
5. It set a precedent for integrating scientific forecasting into agricultural and economic planning in India.

3. Critically discuss the role of statistical models in improving weather forecasting accuracy.

1. Statistical models identify relationships between historical weather data and forecasting variables, enhancing prediction accuracy.
2. Models developed by figures like Sir Gilbert Walker improved understanding of monsoon variability through statistical correlations.
3. The IMD’s transition to models like the Statistical Ensemble Forecasting System (SEFS) reduced forecast errors.
4. Modern models incorporate multiple parameters and ensemble forecasting, leading to more robust predictions.
5. Despite improvements, ongoing adjustments are necessary as some predictors lose relevance over time, affecting model accuracy.

4. With suitable examples, discuss the influence of global climate patterns on regional weather phenomena.

1. El Niño and La Niñly impact the Indian monsoon, altering rainfall patterns and intensity.
2. Walker identified global atmospheric pressure patterns like the Southern Oscillation that influence Indian climate variability.
3. The Indian Ocean Dipole (IOD) affects monsoon strength, with positive phases leading to enhanced rainfall.
4. Global warming trends are shifting monsoon patterns, causing unpredictable weather events and extremes.
5. Regional phenomena, like cyclones, can also be influenced by broader climate changes, demonstrating interconnectedness.