Evolution of Monsoon Forecasting in India
Kartavya Desk Staff
Syllabus: Geography
Source: IE
Context: The IMD has predicted an ‘above normal’ monsoon for 2025, at 105% of the Long Period Average (LPA).
• This highlights the progress made in monsoon forecasting models, especially dynamic and ensemble-based systems like MMCFS and MME.
About Evolution of Monsoon Forecasting in India:
• What is Weather Forecasting?
• Weather forecasting is the scientific estimation of atmospheric conditions (e.g., rainfall, temperature, humidity) at a specific location and time using observational data and mathematical models.
• Weather forecasting is the scientific estimation of atmospheric conditions (e.g., rainfall, temperature, humidity) at a specific location and time using observational data and mathematical models.
• Types of Forecasts:
• Nowcasting (0–6 hrs): Provides ultra-short-term weather updates using real-time data from radars and satellites. Short-range (1–3 days): Useful for agriculture and planning; relies on numerical weather prediction (NWP) models. Medium-range (4–10 days): Uses dynamic models to simulate atmospheric conditions; forecasts moderate-term patterns. Long-range (10 days–2 years): Focuses on seasonal trends like monsoon; involves ocean-atmosphere interactions. Ensemble Forecasting: Combines multiple models and parameters to offer more reliable and probabilistic forecasts.
• Nowcasting (0–6 hrs): Provides ultra-short-term weather updates using real-time data from radars and satellites.
• Short-range (1–3 days): Useful for agriculture and planning; relies on numerical weather prediction (NWP) models.
• Medium-range (4–10 days): Uses dynamic models to simulate atmospheric conditions; forecasts moderate-term patterns.
• Long-range (10 days–2 years): Focuses on seasonal trends like monsoon; involves ocean-atmosphere interactions.
• Ensemble Forecasting: Combines multiple models and parameters to offer more reliable and probabilistic forecasts.
About Evolution of Monsoon Forecasting in India:
• Pre-Independence Era:
• 1875 – IMD Established: Founded after the 1876 famine to monitor weather and predict monsoons scientifically. Henry Blanford (1882–85): Linked Himalayan snow cover to monsoon strength; laid early forecasting foundation. Sir John Eliot (1889): Added Ocean and Australian conditions; began regional forecasts based on April-May indicators. Sir Gilbert Walker (1904): Introduced 28 global predictors and statistical correlations to forecast monsoon patterns.
• 1875 – IMD Established: Founded after the 1876 famine to monitor weather and predict monsoons scientifically.
• Henry Blanford (1882–85): Linked Himalayan snow cover to monsoon strength; laid early forecasting foundation.
• Sir John Eliot (1889): Added Ocean and Australian conditions; began regional forecasts based on April-May indicators.
• Sir Gilbert Walker (1904): Introduced 28 global predictors and statistical correlations to forecast monsoon patterns.
• Post-Independence Era
• 1947–1987 – Walker Model Continued: IMD retained statistical models with high errors due to outdated predictors. 1988 – Gowariker Model: Used 16 climatic variables in a power regression model for seasonal monsoon prediction. 2003 – Parameter Reduction: Introduced two simpler models and two-stage forecasts to enhance accuracy. 2007 – SEFS Launched: Developed a five-parameter (April) and six-parameter (June) model to prevent overfitting.
• 1947–1987 – Walker Model Continued: IMD retained statistical models with high errors due to outdated predictors.
• 1988 – Gowariker Model: Used 16 climatic variables in a power regression model for seasonal monsoon prediction.
• 2003 – Parameter Reduction: Introduced two simpler models and two-stage forecasts to enhance accuracy.
• 2007 – SEFS Launched: Developed a five-parameter (April) and six-parameter (June) model to prevent overfitting.
• Recent Developments:
• 2012 – MMCFS Introduced: Dynamic coupled model combining ocean, land, and atmospheric variables for holistic prediction. 2021 – Multi-Model Ensemble (MME): Integrates forecasts from global climate models to improve monsoon accuracy.
• 2012 – MMCFS Introduced: Dynamic coupled model combining ocean, land, and atmospheric variables for holistic prediction.
• 2021 – Multi-Model Ensemble (MME): Integrates forecasts from global climate models to improve monsoon accuracy.
Limitations of Current Forecasting:
• Model Biases: Systematic errors in simulations lead to regional inaccuracies and underperformance in extreme events.
• Weak Teleconnections: Climate signals like ENSO and IOD are not consistently linked to rainfall outcomes in India.
• Regional Discrepancies: Forecast precision drops at the micro-level, making district-wise prediction unreliable.
• Changing Predictors: Long-used predictors have lost statistical relevance, affecting model reliability.
• Extreme Event Forecasting: Current models still struggle with predicting droughts, floods, or sudden monsoon failures.
Way Ahead:
• Refine Dynamic Models: Improve calibration of MMCFS and MME to reduce structural errors in simulations.
• Integrate AI & ML Tools: Adopt machine learning to refine pattern recognition and climate correlations.
• High-Resolution Modelling: Build district-level models to support local disaster management and agriculture.
• Upgrade Observational Systems: Expand coverage of Doppler radars, buoys, and automatic weather stations (AWS).
• Global Collaboration: Share data and align with international agencies for broader and accurate forecasting.
Conclusion:
The journey of monsoon forecasting in India reflects scientific perseverance and technological evolution. While the IMD has made commendable strides, future accuracy hinges on upgrading models, data assimilation, and global partnerships. Reliable monsoon predictions are not just about climate—they are vital for India’s agriculture, water security, and economic stability.
• Discuss the meaning of colour-coded weather warnings for cyclone prone areas given by India Meteorological Department. (UPSC-2022)