grounding-of-planes-affects-dynamical-models-of-weather-forecasts-summary

Context: In the ongoing COVID-19 lockdown,the grounding of the country’s civilian aircrafts has deprived a key source of weather data that the India Meteorological Department (IMD) uses for its forecasts.

Data provided by aircrafts to meteorological agencies and its significance

  • Data collected from upper atmosphere:
    • Aircraft data about temperature and wind speed in the upper atmosphere is provided to meteorological agencies which is used in the dynamical models.
    • Accurate weather forecasts: These dynamical models are run on supercomputers which ultimately give weather forecasts three days, or even two weeks ahead.
  • Data on early developing of thunderstorms or swings in temperatures:
    • Inputs from aircraft are important for the dynamical models as it determines the initial conditions for weather models.
    • This data is also helpful to warn of developing thunderstorms or swings in temperatures that often begin at the heights aircraft traverse.

Implications of non availability of this data on IMD

  • Use of traditional statistical forecast: This year, the IMD will likely rely on its traditional statistical forecast system which is developed on the basis of historical data.
  • Impact on short term forecasts: Lack of data for a prolonged period of time is a big loss for calculating weather trends and future climate patterns.
  • Not affecting Monsoon prediction significantly:
    • The monsoon forecast, which is a long-term forecast, is not going to be significantly affected.
    • Reasons: A major factor for gauging the performance of the monsoon is the El Nino which is measured by observational data buoys located in the sea and relayed via satellite. 

About the weather forecast in India

Statistical models of forecasts:

  • Until about the 2010, the only method employed by the IMD to forecast the monsoon was statistical models. 
  • These statistical models involved identifying climate parameters linked to the performance of the monsoon — for instance the following
    • The sea surface temperature gradient between North Atlantic and North Pacific, 
    • The volume of warm water in the equatorial Pacific and
    • The Eurasian snow cover etc
  • Based on extrapolation of values: Their values in February and March are correlated to values of actual rainfall over a hundred years and then, using statistical techniques, extrapolated to forecast a particular year’s monsoon. 
  • No accuracy in the prediction: These statistical models have proved wrong and the IMD missed its mark on forecasting major droughts and rain-deficits many times particularly 2002, 2004 and 2006. 

Dynamic model of forecasts:

  • Only around 2015 India started testing a dynamical system. 
  • This simulates the weather at a chosen set of locations on a given day, the land and ocean temperature, moisture, wind speeds at various heights, etc .
  • Role of supercomputers: Powerful supercomputers calculate how these weather variables will change over days, weeks, months. 
  • It's able to do this by solving physics equations that show how each of these weather variables is related to each other. 
  • The IMD and several private weather agencies are increasingly relying on more sophisticated and high-resolution computer models to give localised forecasts, or warn farmers of changes in weather 10-15 days ahead. 
  • Benefits of the model:
    • Rather than long-range forecasts that only give a broad tenuous picture of the likely performance of the monsoon, these shorter forecasts are far more reliable and help farmers make decisions about sowing. 
    • These models are also useful for anticipating heat-wave or a cold-wave and therefore useful to urban planners and government. 
  • Not considered entirely reliable: Though meteorological agencies around the world are shifting to such techniques, they still aren’t considered still entirely reliable for forecasting the monsoon. 
     

About El-Nino

  • El Nino is a climate pattern that describes the unusual warming of surface waters in the eastern tropical Pacific Ocean.
  • Off the coast of Peru in the Eastern Pacific and Central Pacific, there is normally cool surface water because of the cold Peruvian current. But El Nino makes it go warm.
  • Due to this warm water, the air gets up and surface air pressure above the Eastern Pacific gets down.
  • On the other hand, the waters cool off in western pacific and off Asia. This leads to a rise in surface pressure over the Indian Ocean, Indonesia, and Australia.
  • El Niño has an impact on ocean temperatures, the speed and strength of ocean currents, the health of coastal fisheries, and local weather from Australia to South America and beyond.The interrelation is shown as follows

Source:National geographic

  • The warm water causes lots of clouds getting formed in that area, causing heavy rains in the Peruvian desert during El Niño years. This robs the Indian subcontinent of its share in the Monsoon rains.