Why in the news?

Marking 150 years of IMD, the Prime Minister launched Mission Mausam.

History and Background of Meteorology in India

• Background: In 1636 Halley, a British scientist published treatise on the Indian monsoon, which he attributed to a seasonal reversal of winds due to the differential heating of the Asian land mass and the Indian Ocean.
• History of IMD
  • Genesis: 1875.
  • Headquarters: New Delhi. (Initially it was Calcutta).
  • The first Director General of Observatories was Sir John Eliot, who was appointed in May 1889 at Calcutta headquarters.
• Evolution post 1947
  • Ministry: Ministry of Earth Sciences (MoES).
  • First organization in India to have a message switching computer: For supporting its global data exchange. 
  • One of the first few electronic computers introduced in the country was provided to IMD for scientific applications in meteorology. 
• India was the first developing country in the world to have its own geostationary satellite, INSAT, for continuous weather monitoring of this part of the globe and particularly for cyclone warning.
• International help: It provides Cyclone forecast and warning services to 13 north Indian Ocean countries along with forecast and warning services to SAARC nations.

Major achievements of the IMD

• Pioneering accurate Weather Observations: From manual observations to the deployment of state-of-the-art Automatic Weather Stations (AWS), IMD collects reliable weather data forming the backbone of our forecasts and services.
  • Automatic Weather Stations increased from 675 in 2014 to 1,208 in 2024. Rainfall Monitoring Stations have increased from 3,995 to 6,095.
• Advances in Numerical Weather Prediction: Provide accurate forecasts for up to 7 days with the outlook for 15 days, 1 month and a season. 
• Monsoon Predictions: Over the years, IMD has easily perfected the art of monsoon forecasting, to predict seasonal rainfall patterns since 1886.
• Disaster Preparedness and Mitigation: Accurate cyclone warnings by IMD have reduced the number of deaths from 10,000 in 1999 to near zero in 2020-2024.
  • There are two geostationary satellites, viz., INSAT-3D and INSAT-3DR in 2023 against only one such satellite INSAT-3D in 2014 for more advanced information about disasters.
• Boost to telecommunication: Directorate of telecommunication was established in 1970 along with high speed switching computers in 1970 and Delhi became the Regional Telecommunication Hub.
• Support to Aviation, Agriculture, and Other Sectors: Specialized services for aviation, agriculture, energy, and water resource from flight safety to crop advisories.
  • Tropical Cyclone Advisory for International Civil Aviation commenced in 2003 from IMD New Delhi and it acted as one of the seven Tropical Cyclone Advisory Centre (TCAC) as per the requirement of ICAO.
• Meteorological support for inland water and surface transport: Flood Meteorological Offices (FMOs) have been set up by IMD at fifteen locations and they provide valuable meteorological support mainly in the form of river subbasin-wise Quantitative Precipitation Forecast (QPF).
  • It played pivotal role in predicting the heavy to very heavy rainfall in Mumbai and north Konkan areas during the monsoon season IN 2017 and avoid casualties.

Challenges in front of IMD

• Climate change: Unpredictable weather events such as extreme rainfall highlight the need for accelerated improvements in observational and communication systems to tackle emerging challenges.
  • The IMD's 12 km x 12 km grid means each cell covers 144 square kilometers. While this broader coverage is beneficial, it significantly hampers the accuracy of localized weather predictions, such as hailstorms or heavy rainfall, which can vary greatly within small areas of 2-3 square kilometers.
• Early warning in case of disasters: Need to account for the increasing frequency and intensity of extreme weather events, particularly on smaller spatial and temporal scales, such as thunderstorms.
  • IMD can predict heat waves with 97-99% accuracy 24 hours in advance, but its accuracy for heavy rainfall events is under 80%, highlighting a gap in forecasting that could lead to disasters.
• Weather data limited regions: India has 56 RS/RW (Radiosonde/Radiowind) observation stations, which are insufficient to accurately monitor upper air observations in a tropical India. They are as high as 120 in China.
• Unpredictability of monsoon: There are more weather systems in the mid-latitudes or at the poles, which are more predictable. Monsoon, which are more transient and chaotic and hard to predict.
  • In 2012 IMD predicted normal rain; a drought plan was needed due to lack of rain. However, after downgrading the forecast, heavy rains reemerged; deficit in North India was 12%.
• Instruments quality: None of the Indian radiosondes are WMO certified. The WMO recommends RS/RW with a minimum score of 3.0 or higher for all parameters in regular operation.
• Overall data unavailability for AI/ML models: Lack of local level data can pose a fundamental problem. 
  • The Geological Survey of India has recorded over 9,575 glaciers in the Himalayas yet detailed glaciological studies cover less than 30. This data scarcity undermines the development of AI-based early warning systems.

Way forward

• A better understanding and study of physical processes: It will allow more accurate predictions on different temporal and spatial scales. 
  • Two good examples are land-atmosphere interaction (land surface processes) and convective parameterization (How different clouds are treated in weather prediction models).
• Three-dimensional observations of the Earth system: These are essential components for an observation system for accurate data especially related to complex systems such as monsoon.
• Strengthening Early Warning Systems: We aim to enhance our forecasting capabilities for extreme weather events, reducing lead times and increasing accuracy.
  • The IMD has been trying out experimental forecasts for 3 km x 3 km grids, but the eventual objective is to get hyper-local forecasts for 1 km x 1 km areas.
• Improve the last mile connection: There is still a gap between the forecasters and the users. At many times, users do not fully understand what the forecasters speak. Also, the forecasters are not fully aware the scope of their requirements.
• Leveraging Artificial Intelligence and Machine Learning (AI & ML): Integrating AI and ML into weather models will allow us to process available data for more precise predictions.