India’s Weather Forecasting Needs an Upgrade

Syllabus: GS1/ Geography

Context

• The recent extreme weather events have put the focus back on the country’s weather prediction capabilities and ways to improve them.

Weather Prediction in India

• India, at present, depends on satellite data and computer models for weather prediction. The Indian Meteorological Department (IMD) uses the INSAT series of satellites and supercomputers.
• In India three satellites, INSAT-3D, INSAT-3DR and INSAT-3DS are used mainly for meteorological observations. 
• Forecasters use satellite data around cloud motion, cloud top temperature, and water vapor content that help in rainfall estimation, weather forecasting, and tracking cyclones.

Initiatives taken to improve the efficiency

• The ‘National Monsoon Mission’ was set out in 2012 to move the nation over to a system that relies more on real-time, on-the-ground data gathering.
• The IMD is also increasingly using Doppler radars to improve efficiency in predictions. The number of Doppler radars has increased from 15 in 2013 to 37 in 2023. 
  • Doppler radars are used to predict rainfall in the immediate vicinity, making predictions more timely and accurate.
• The weather agency is now using manned and automatic weather stations, aircraft, ships, weather balloons, ocean buoys and satellites to gather information on atmospheric temperature, pressure humidity, wind speed and direction and sea surface temperatures.
  • The data is then fed into a supercomputer at the Indian Institute of Tropical Meteorology in Pune.
• The Ministry of Agriculture & Farmers Welfare have initiated the weather information network and data system (WINDS) under which more than 200,000 ground stations will be installed, to generate long-term, hyper-local weather data. 

Challenges

• Lack of weather monitoring ground stations: Currently, IMD operates around 800 automatic weather stations (AWS), 1,500 automatic rain gauges (ARG) and 37 doppler weather radars (DWR).
  • This is against the total requirements of more than 3,00,000 ground stations (AWS/ARG) and around 70 DWRs. 
• Lack of coordination: Several Indian State governments and private companies operate a significant network of ground stations (more than 20,000), many of which are not currently used by IMD due to inaccessibility and/or reliability of the data.
• Extreme Weather Events: Events, such as extreme rainfall, landslides, and cloudbursts, have become more frequent due to climate change. These events are highly localized and erratic, making them difficult to predict with existing weather simulation models. 
• Outdated Prediction Models: Currently, most of the prediction software used in forecasting are based on the global forecasting system and weather research and forecasting models, both of which are not the most modern.
• Forecasting weather in the tropics is more challenging than in areas that are farther from the equator, because of greater variability in weather phenomena.
• Predicting large-scale systems like monsoons, cyclones, or heat waves is easier due to their widespread nature. However localized events like cloudbursts and sudden, unexpected weather phenomena are much harder to forecast accurately
• Need for precision: IMD currently has the ability to forecast weather events over a 12 km x 12 km area. This grid is larger than most Indian cities.
  • For hyper-local forecasts there is a need for 1 km x 1 km forecast.

Way Ahead

• Predicting weather events with a high level of accuracy has become increasingly important in India, one of the most vulnerable nations to climate change.
• There is an urgent need for an integrated system to fill the data gaps. New ground stations will have to be installed and the available data have to be shared seamlessly.
• Also the focus should be on greater integration of artificial intelligence and machine learning (AI/ML) in weather forecasting to get more accurate results.

Source: IE