Tara Prabhakaran will withstand all the air turbulence you throw at it. As a meteorologist, she flew into the heart of rain clouds, often at an altitude of 9 km, to collect samples and conduct experiments. Things can be scary out there sometimes. Once, during an experiment to understand how water droplets and ice particles form and how aerosols and pollutants affect these processes, the power went out inside the plane and the probes froze. They had to lower the altitude to melt the snow.
That didn’t stop Prabhakaran from climbing again. “These observations help refine dynamical models for predicting weather, climate and climate. Monsoon Rain,” she says.
Prabhakaran is a senior scientist at the Indian Institute of Tropical Meteorology, Pune, with the Indian National Center for Ocean Information Services in Hyderabad and the National Center for Medium-Range Weather Forecasting in Noida, where he observes meteorological phenomena — heat waves, cyclones, monsoon arrivals — and develops mathematics. Weather forecast models.
These organizations help the India Meteorological Department (IMD) complete 150 years of forecasting the monsoon and making daily, weekly and seasonal weather forecasts using satellite data, high-performance computing systems and historical data.
Evolution of Prophecy in India
The IMD was founded in 1875 by Sir Henry Blanford, the Imperial Meteorological Reporter, who gave India’s first official seasonal monsoon forecast on 4 June 1886. Before that, he had used the inverse relationship between Himalayan snowfall and monsoon rainfall to make temporal forecasts since 1882. 1885.
In 1906, Sir Gilbert Walker used a more sophisticated forecast model based on the relationship between monsoon rainfall and global circulation parameters.
Over time, Indian climate models have become richer. Vasant Gowariker’s monsoon forecast model based on 16 global and regional parameters performed well from 1988 to the end of the century. But when 2002 – predicted to be a normal monsoon – turned out to be a drought year, a better model had to be created.
An IMD team led by former secretary of MoES (Ministry of Earth Sciences) M Rajeev analyzed the existing models and came up with a two-step forecasting system in 2003. “Its first forecast in mid-April was based on eight parameters, and the second in May on 10 parameters. This was followed by a rainfall forecast for agricultural activities in July,” says Rajeevan. As the technology developed, Rajeev His team designed the Statistical Ensemble Forecasting System in 2007. But 2009 was a drought year that exposed the limitations of seasonal forecasting models, both statistical and dynamic, Rajeev and C.K. Unnikrishnan from the National Atmospheric Research Laboratory wrote in a 2011 issue of Breeze. Newsletter of Chennai Chapter of Indian Meteorological Society. “Errors persisted because the predictions were based on empirical data and dynamical models built on atmosphere-ocean coupled models,” says Rajeev.
Rajeev’s team comprising senior scientists DS Pi and OP Sreejith implemented a multi-model ensemble forecasting system in 2021 when the government sought forecasts of spatial distribution of seasonal rainfall along with regional average rainfall forecasts. Climate models from various forecasting and research centers.
A multi-model ensemble has the advantage of presenting a range of future climate possibilities. Currently, probabilistic forecasts of precipitation and temperature are available separately for every 12 months. This is in addition to seasonal forecasts for southwest monsoon (June-September), northeast monsoon (October-December) and premonsoon season (March-May), says Pai.
Progress of Monsoon Mission
Getting the monsoon forecasts right is crucial for India, as 10% more rain than normal can lead to flooding and 10% less rain can lead to drought.
That is why the MoES launched the National Monsoon Mission in 2012 to improve India’s weather and climate forecasts. It combined ocean, land, atmosphere, and sea ice models to produce long (seasonal) and extended (four weeks at a time) forecasts, and used isolated atmospheric models for short, medium (7–10 days) forecasts.
According to mission head Suryachandra Rao, they have borrowed the coupled forecasting system used by the US Meteorological Center. MoES has enhanced supercomputing facilities in India to support research and operations. Since 2017, the IMD has started using this system to generate experimental seasonal forecasts for the monsoon and an operational statistical ensemble forecasting system. “
As a result, the models became more accurate at the micro level. They can now predict the weather within a radius of 12 km, down from 38 km before the mission was launched. The IMD now has a fully dynamic seasonal forecast system serving the entire South Asia.
Why predictions are wrong
Technological innovations play a major role in improving climate models. For example, the weather bureau’s processing power has grown from one petaflop (a measure of computing speed) to 10 petaflops in the past 10 years. There are now 37 radars instead of 14, and the number of automated weather stations and rain gauges has doubled.
They also have two satellites from the previous one. “Cyclone Man” Mrityunjay Mohapatra, director general of the IMD’s Meteorological Department, says satellite observations are received every 15 minutes and analyzed every three hours to determine the state of the atmosphere, oceans and land.
Despite all these advances, forecasts still go wrong, says OP Sreejith, Head of Meteorological and Forecasting Services, IMD Pune, adding that it is difficult to make a perfect forecast in the tropics as many parameters change quickly. Weather in the mountains is equally difficult to predict. “Forecasts can be improved with better computational resources and more observational data and research. However, predicting tropical weather is challenging, as is 100% accuracy of long-range forecasting,” he says.
This is partly because even the best climate models have their biases. For example, many climate models have a dry bias towards central India during the monsoon. Sreejith’s team looks at different models and creates a forecast after correcting for their biases. He says the multi-model ensemble forecasting system, which uses models from India, the US, Japan and Europe, has been used to forecast the monsoon since 2021 “with good results”.
Mohapatra admits that forecasting is difficult — “we make best educated guesses based on scientific evidence” — but says so far they’ve done well. In 2010, the landfall error of the cyclone was 150 km, now it is 25 km. Today in June, the five-day forecast for rain is almost as accurate as the one-day forecast in 2010.
That didn’t stop Prabhakaran from climbing again. “These observations help refine dynamical models for predicting weather, climate and climate. Monsoon Rain,” she says.
Prabhakaran is a senior scientist at the Indian Institute of Tropical Meteorology, Pune, with the Indian National Center for Ocean Information Services in Hyderabad and the National Center for Medium-Range Weather Forecasting in Noida, where he observes meteorological phenomena — heat waves, cyclones, monsoon arrivals — and develops mathematics. Weather forecast models.
These organizations help the India Meteorological Department (IMD) complete 150 years of forecasting the monsoon and making daily, weekly and seasonal weather forecasts using satellite data, high-performance computing systems and historical data.
Evolution of Prophecy in India
The IMD was founded in 1875 by Sir Henry Blanford, the Imperial Meteorological Reporter, who gave India’s first official seasonal monsoon forecast on 4 June 1886. Before that, he had used the inverse relationship between Himalayan snowfall and monsoon rainfall to make temporal forecasts since 1882. 1885.
In 1906, Sir Gilbert Walker used a more sophisticated forecast model based on the relationship between monsoon rainfall and global circulation parameters.
Over time, Indian climate models have become richer. Vasant Gowariker’s monsoon forecast model based on 16 global and regional parameters performed well from 1988 to the end of the century. But when 2002 – predicted to be a normal monsoon – turned out to be a drought year, a better model had to be created.
An IMD team led by former secretary of MoES (Ministry of Earth Sciences) M Rajeev analyzed the existing models and came up with a two-step forecasting system in 2003. “Its first forecast in mid-April was based on eight parameters, and the second in May on 10 parameters. This was followed by a rainfall forecast for agricultural activities in July,” says Rajeevan. As the technology developed, Rajeev His team designed the Statistical Ensemble Forecasting System in 2007. But 2009 was a drought year that exposed the limitations of seasonal forecasting models, both statistical and dynamic, Rajeev and C.K. Unnikrishnan from the National Atmospheric Research Laboratory wrote in a 2011 issue of Breeze. Newsletter of Chennai Chapter of Indian Meteorological Society. “Errors persisted because the predictions were based on empirical data and dynamical models built on atmosphere-ocean coupled models,” says Rajeev.
Rajeev’s team comprising senior scientists DS Pi and OP Sreejith implemented a multi-model ensemble forecasting system in 2021 when the government sought forecasts of spatial distribution of seasonal rainfall along with regional average rainfall forecasts. Climate models from various forecasting and research centers.
A multi-model ensemble has the advantage of presenting a range of future climate possibilities. Currently, probabilistic forecasts of precipitation and temperature are available separately for every 12 months. This is in addition to seasonal forecasts for southwest monsoon (June-September), northeast monsoon (October-December) and premonsoon season (March-May), says Pai.
Progress of Monsoon Mission
Getting the monsoon forecasts right is crucial for India, as 10% more rain than normal can lead to flooding and 10% less rain can lead to drought.
That is why the MoES launched the National Monsoon Mission in 2012 to improve India’s weather and climate forecasts. It combined ocean, land, atmosphere, and sea ice models to produce long (seasonal) and extended (four weeks at a time) forecasts, and used isolated atmospheric models for short, medium (7–10 days) forecasts.
According to mission head Suryachandra Rao, they have borrowed the coupled forecasting system used by the US Meteorological Center. MoES has enhanced supercomputing facilities in India to support research and operations. Since 2017, the IMD has started using this system to generate experimental seasonal forecasts for the monsoon and an operational statistical ensemble forecasting system. “
As a result, the models became more accurate at the micro level. They can now predict the weather within a radius of 12 km, down from 38 km before the mission was launched. The IMD now has a fully dynamic seasonal forecast system serving the entire South Asia.
Why predictions are wrong
Technological innovations play a major role in improving climate models. For example, the weather bureau’s processing power has grown from one petaflop (a measure of computing speed) to 10 petaflops in the past 10 years. There are now 37 radars instead of 14, and the number of automated weather stations and rain gauges has doubled.
They also have two satellites from the previous one. “Cyclone Man” Mrityunjay Mohapatra, director general of the IMD’s Meteorological Department, says satellite observations are received every 15 minutes and analyzed every three hours to determine the state of the atmosphere, oceans and land.
Despite all these advances, forecasts still go wrong, says OP Sreejith, Head of Meteorological and Forecasting Services, IMD Pune, adding that it is difficult to make a perfect forecast in the tropics as many parameters change quickly. Weather in the mountains is equally difficult to predict. “Forecasts can be improved with better computational resources and more observational data and research. However, predicting tropical weather is challenging, as is 100% accuracy of long-range forecasting,” he says.
This is partly because even the best climate models have their biases. For example, many climate models have a dry bias towards central India during the monsoon. Sreejith’s team looks at different models and creates a forecast after correcting for their biases. He says the multi-model ensemble forecasting system, which uses models from India, the US, Japan and Europe, has been used to forecast the monsoon since 2021 “with good results”.
Mohapatra admits that forecasting is difficult — “we make best educated guesses based on scientific evidence” — but says so far they’ve done well. In 2010, the landfall error of the cyclone was 150 km, now it is 25 km. Today in June, the five-day forecast for rain is almost as accurate as the one-day forecast in 2010.
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