Why in the News?
The India Meteorological Department (IMD) has predicted that the rainfall during the June-September southwest monsoon season will be higher than usual, around 105% of the average rainfall over a long period.
What are the main factors that influence the Indian monsoon, as mentioned by the IMD?
- El Niño-Southern Oscillation (ENSO): El Niño, which is characterized by warming sea surface temperatures in the Pacific Ocean, tends to reduce monsoon rainfall over India. Eg, during the 2015 El Niño event, India experienced a weakened monsoon and below-normal rainfall.
- Indian Ocean Dipole (IOD): The IOD refers to temperature differences between the western and eastern Indian Ocean. A positive IOD (warmer waters in the west) is typically linked to above-average rainfall in India, while a negative IOD can lead to drought conditions. Eg,2019 saw a positive IOD, which helped counterbalance the El Niño and brought more rainfall.
- Himalayan Snow Cover: As observed by Blanford, the amount of snow accumulation in the Himalayas influences the monsoon. A thicker snow cover in the winter months often leads to increased rainfall during the subsequent monsoon. Eg, years with heavy snowfall in the Himalayas tend to see better monsoon rainfall in regions like Northwest India.
How did Blanford contribute to the development of monsoon forecasting in India?
- Identified the Snow-Monsoon Relationship: Blanford discovered an inverse relationship between the amount of snow accumulated in the Himalayas during winter and the subsequent monsoon rainfall over India. He hypothesized that greater snow accumulation led to a stronger monsoon. This was the basis for early monsoon predictions. Eg: Between 1882-1885, Blanford used Himalayan snow cover data to predict the intensity of the monsoon, marking a key step in systematic weather forecasting.
- First Long-Range Forecast (1886): Blanford made India’s first long-range monsoon forecast in 1886, predicting the seasonal rainfall across India and Burma based on his snow-rain hypothesis. This was a pioneering effort in utilizing long-term data for weather predictions. Eg: Blanford’s 1886 forecast was the first to consider annual snowfall patterns in the Himalayas to predict the monsoon’s arrival and intensity across the entire Indian subcontinent.
- Foundation for Modern Meteorology: Blanford’s work laid the foundation for further development in meteorology and forecasting. His research on snow cover influenced future meteorologists, including Sir John Eliot and Sir Gilbert Walker, who refined and expanded his methods using new data sources and statistical models. Eg: Blanford’s ideas directly influenced later meteorologists, helping to evolve more comprehensive models, including those considering global atmospheric factors.
Why were IMD’s forecasts inaccurate between 1932 and 1987?
- Outdated Predictors: The parameters identified by Sir Gilbert Walker, such as the Southern Oscillation and other atmospheric factors, had lost their significance over time, meaning their relationship with the monsoon was no longer consistent. This led to inaccurate forecasts. Eg: For instance, in the period 1932-1987, the forecast errors were significant, with average errors of 12.33 cm for the peninsula and 9.9 cm for Northwest India, indicating the failure of the existing model.
- Failure to Adapt to New Data: Despite attempts to tweak Walker’s model, the IMD did not fully integrate new meteorological data and evolving atmospheric conditions, leading to persistent inaccuracies in monsoon prediction. Eg: The model failed to predict the 1987 drought, highlighting the inadequacy of the forecasting system during this period and the inability to account for changing atmospheric patterns.
How has the IMD’s forecasting system improved since 2007?
- Introduction of Statistical Ensemble Forecasting System (SEFS): In 2007, the IMD introduced the SEFS, which combined multiple models to generate a more robust prediction. This reduced the error margin and improved the accuracy of forecasts by considering different possible outcomes. Eg: The SEFS helped reduce the average absolute error in forecasts between 2007 and 2018 to 5.95% of the long-period average (LPA), compared to a higher 7.94% error in the earlier period (1995-2006).
- Launch of the Monsoon Mission Coupled Forecasting System (MMCFS): In 2012, the IMD launched the MMCFS, which integrated ocean, atmosphere, and land data for more accurate predictions. This coupled dynamic model enabled better predictions by accounting for the interactions between various climate factors. Eg: The MMCFS contributed to more accurate monsoon forecasts in the years following its introduction, helping the IMD predict monsoon patterns with greater precision.
What impact did the Monsoon Mission Coupled Forecasting System (MMCFS) have on IMD’s accuracy?
- Improved Forecast Accuracy by Integrating Multiple Data Sources: The MMCFS combined data from the ocean, atmosphere, and land, allowing for a more holistic and accurate monsoon forecast. This helped the IMD provide more reliable predictions by considering the dynamic interactions between various climate components. Eg: After the introduction of MMCFS in 2012, the IMD was able to produce more precise monsoon predictions, particularly in terms of seasonal rainfall.
- Enhanced Long-Term Predictive Capabilities: The coupled model allowed the IMD to improve long-term monsoon predictions by simulating real-world climate interactions more accurately, reducing errors in forecasting and enhancing the reliability of predictions over longer time spans. Eg: The model helped improve predictions such as the 2014 monsoon season, where the forecast matched the actual rainfall more closely than earlier years, highlighting its effectiveness in reducing forecast errors.
Way forward:
- Integration of Artificial Intelligence and Machine Learning: Leveraging AI and ML can further refine IMD’s forecasting models by analyzing vast datasets more efficiently and identifying hidden patterns in climate behavior, improving the accuracy of short- and long-term monsoon predictions.
- Collaboration with Global Climate Agencies: Strengthening partnerships with international climate research institutions can enhance data sharing and provide more comprehensive insights into global climate drivers affecting the Indian monsoon.
Mains PYQ:
[UPSC 2015] How far do you agree that the behavior of the Indian monsoon has been changing due to humanizing landscapes? Discuss.
Linkage: Forecasting is essential for understanding the behavior of the Indian monsoon. This article explores the evolution of monsoon forecasting in India, particularly by the India Meteorological Department (IMD).
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