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India Meteorological Department

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Topics

  • Introduction
  • History and Development of Monsoon Prediction in India
  • Monsoon Prediction Models

Introduction:

The India Meteorological Department (IMD) was established by the British in 1875 at Shimla, and its head office is located in New Delhi. IMD has regional offices in major cities like Guwahati, Mumbai, Kolkata, Chennai, Nagpur, and New Delhi to cover weather monitoring across the country.

1. Daily weather maps are prepared and published twice every 24 hours to provide updates and predictions about weather conditions.

2. The IMD conducts continuous research on:

  • Instruments for climatic readings
  • Radar-based climate predictions
  • Seismology-related climate forecasts
  • Rainfall predictions using satellites
  • Air pollution monitoring.

3. The IMD provides crucial weather and climate-related information to various sectors like aviation, shipping, agriculture, irrigation, marine oil exploration, and production.

4. Predictions about natural calamities, such as dust storms, sand storms, heavy rainfall, hot and cold waves, and tsunamis, are communicated to various departments, mass media, and citizens.

5. To enhance its forecasting capabilities, India has launched advanced satellites equipped with high-class technology to monitor weather and climate.

6. Observatories at multiple locations analyse data received from satellites, improving the accuracy of weather predictions and climate research.

History and Development of Monsoon Prediction in India:

The tradition of predicting the monsoon season in India dates back over 100 years. After the famine of 1877, H. F. Blanford, the founder of the India Meteorological Department (IMD), made the first monsoon prediction using Himalayan snowfall as a parameter. In the 1930s, Sir Gilbert Walker, then director of IMD, highlighted the connection between global climatic factors and the Indian monsoon. Based on observations and previous data, Sir Gilbert Walker formulated a hypothesis regarding the nature of the monsoon. In the 1990s, under the leadership of Dr. Vasantrao Govarikar, a monsoon prediction model was developed. This model was based on 16 worldwide climatic parameters, which influenced monsoon patterns in India.

  • The model combined global atmospheric and climatic observations to improve the accuracy of monsoon forecasts. This monsoon model was actively used for predictions from 1990 to 2002.
  • It marked a significant advancement in monsoon forecasting by integrating multiple climatic factors.
  • The development of such models has helped India better prepare for monsoon seasons, crucial for agriculture, water management, and disaster planning.

Monsoon Prediction Models:

Aspect Mathematical Model (Dynamic) Holistic Model Statistical Model
Definition Forecasts weather and climate using supercomputers to analyse real-time events. Integrates critical parameters from multiple other models for accuracy. Compares current climatic observations with historical data over years.
Key Features Processes parameters such as temperature, wind speed, and atmospheric pressure. Predictions are a collective outcome of various models for balance. Analyses parameters like oceanic temperature, atmospheric pressure, and past monsoon trends.
Technology Used Uses advanced technology like the Param supercomputer. Combines multiple influencing factors and approaches. Relies on statistical methods to analyse relationships and trends.
Basis of Prediction Relies on physical principles like atmospheric motion, ocean-atmosphere interactions, and geographical factors. Considers complex interactions among various climatic parameters. Based on historical climatic data, trends, and observed relationships.
Adaptability Highly dynamic, adjusting continuously to real-time data and daily events. Produces reliable and balanced predictions by integrating multiple approaches. Effective for long-term trends but less adaptable than dynamic models.
Usage Duration Developed for daily predictions and real-time analysis. Provides a comprehensive prediction by combining models. Used for identifying long-term monsoon behaviour and trends.
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