India Meteorological Department
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Contents |
Expertise developed
1875- 2023
Ardhra Nair, July 15, 2023: The Times of India
Thara Prabhakaran can stomach all the air turbulence you throw at her. As a weather scientist she flies into the heart of rain clouds to collect samples and conduct experiments, often to a height of 9km. Things sometimes get scary there. Once, during an experiment to understand how water droplets and ice particles are formed, and how aerosol and pollution impact these processes, power went out inside the aircraft and the probes froze. They had to drop altitude to let the ice melt.
That didn’t stop Prabhakaran from going up again, though. “These observations help modify the dynamical models for forecasting weather, climate, and the monsoon rain,” she says.
Prabhakaran is a senior scientist at the Indian Institute of Tropical Meteorology in Pune, which along with the Indian National Centre for Ocean Information Services, Hyderabad, and the National Centre for Medium-Range Weather Forecasting, Noida, monitors weather phenomena – heatwave conditions, cyclones, the monsoon’s arrival – and develops mathematical weather prediction models.
These organisations help the India Meteorological Department (IMD), which will soon complete 150 years, to use satellite data, high-performance computing systems and historical data to forecast the monsoon, and make daily, weekly, and seasonal weather predictions.
Evolution Of Forecasting In India
Sir Henry Blanford, an imperial meteorological reporter who gave India its first official seasonal monsoon forecast on June 4, 1886, founded IMD in 1875. Before that, he had used the inverse relationship between Himalayan snowfall and monsoon rainfall to prepare tentative forecasts from 1882 to 1885.
In 1906, Sir Gilbert Walker used a more complex prediction model based on the link between monsoon rainfall and global circulation parameters.
With time, Indian weather models became richer. Vasant Gowariker’s monsoon prediction model based on 16 global and regional parameters served well from 1988 to the end of the century. But when 2002 – forecast to be a normal monsoon – turned out to be a drought year, a better model had to be made.
An IMD team led by M Rajeevan, former secretary of MoES (Ministry of Earth Sciences), analysed the existing models and came up with a two-stage forecasting system in 2003. “Its first prediction in mid-April was based on eight parameters, and the second in May on 10 parameters. These were followed by a rain forecast for July’s agricultural operations,” says Rajeevan. As the technology evolved, Rajeevan and his team designed the statistical ensemble forecasting system in 2007. But 2009 was a drought year which exposed the limitations of the seasonal forecast models, both statistical and dynamical, Rajeevan and CK Unnikrishnan from National Atmospheric Research Laboratory wrote in a 2011 issue of Breeze – newsletter of the Indian Meteorological Society’s Chennai chapter. “The errors persisted because forecasts were based on empirical data and on dynamical models built on atmosphere-ocean coupled models,” says Rajeevan.
When the government sought forecasts of the spatial distribution of seasonal rainfall along with regional average rainfall forecasts, Rajeevan’s team, consisting of senior scientists DS Pai and OP Sreejith, implemented a multi-model ensemble forecasting system in 2021. It was based on eight coupled global climate models from different prediction and research centres.
A multi-model ensemble has the advantage of presenting a range of future weather possibilities. At present, probability forecasts for rainfall and temperatures are made separately for all 12 months. These are in addition to the seasonal forecasts for the southwest monsoon (June-September), northeast monsoon (October-December) and the premonsoon season (March-May), Pai says.
Progress With Monsoon Mission
Getting monsoon predictions right is crucial for India because if it rains even 10% more than nor- mal, flooding fears arise, and if it rains 10% less, drought is a possibility.
That’s why 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 make long (seasonal) and extended (four weeks at a time) forecasts, and used standalone atmospheric models for shortto medium-range (7-10 days) predictions.
Mission head Suryachandra Rao says they borrowed the coupled forecast system used at America’s Climate Prediction Center. “Supercomputing facilities in India were enhanced by the MoES to support research and operations. From 2017, IMD started using this system to generate experimental seasonal forecasts for the monsoon along with an operational statistical ensemble forecasting system. ”
As a result, the models have become more accurate at the micro level. They can now forecast weather over a radius of 12km, down from 38km before the Mission was set up. IMD now has a full- fledged dynamical seasonal prediction system which serves the whole of South Asia.
Why Forecasts Go Wrong
Tech upgrades play a big role in improving the weather models. For example, in the past 10 years the weather bureau’s processing power has gone up from one petaflop (measure of computing speed) to 10 petaflops. It now has 37 radars in place of 14, and the number of automated weather stations and rain gauges has doubled.
They also have two satellites as against one earlier. ‘Cyclone man’ Mrutyunjay Mohapatra, IMD’s director general of meteorology, says satellite observations are received every 15 minutes and analysed every three hours to determine the status of the atmosphere, oceans and land.
Notwithstanding all these advances, predictions still go wrong, and O P Sreejith, head of climate monitoring and prediction services at IMD Pune, says it is hard to make a perfect forecast in the tropics because many parameters change quickly. It is equally difficult to predict weather in the mountainous regions. “With better computational resources, more observation data and research, predictions can be improved. Still, forecasting tropical weather is challenging, as is 100% accuracy of the long-range forecast,” he says.
This is partly because even the best of weather models have their biases. For example, many climate models have a dry bias over central India during the monsoon. Sreejith’s team looks at different models and generates 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 predict monsoons since 2021 with “good results”.
Mohapatra agrees forecasting is difficult – “we make the best educated guesses based on scientific evidence” – but says they have had a good run so far. “The landfall point error for cyclones was about 150km in 2010, it is about 25km now. The five-day forecast today for heavy rainfall is as accurate as the one-day forecast in 2010. ” Their forecast for Cyclone Biparjoy in June was spot on.
An overview
Predicting The Weather To Taming It
On the 150th anniversary of IMD’s establishment today, minister of earth sciences Kiren Rijiju writes Met has never been as important as now, when we are coping with climate change
Today is a historic day for the country as India Meteorological Department celebrates the 150th anniversary of its establishment. IMD has undergone several phases of evolution and has been a testament of progress, glory and service to the nation since 1875.
Breaking technological ground | A new era began after Independence with rapid progress in observational systems and commencement of radar age. The first radar was established in Kolkata in 1954. This radar helped in continuous monitoring of winds and thunderstorms. By the beginning of 1960, the world entered the satellite era with the launch of TIROS-1 by US. IMD became a beneficiary by receiving cloud images from December 1963. This opened a new opportunity to explore remote areas like mountains, deserts, oceans and hills where no data was available till then.
Against all odds | Our commitment to observing and forecasting weather became a tale of innovation and resilience. Starting from a simple India Hut in 1793 for measurement of temperature, we now have a network of 39 radars covering the entire country, satellite images every 15 minutes, more than 1,000 automatic weather stations, 1,350 automatic rain gauges, more than 6,000 rainfall monitoring stations, 56 upper air weather monitoring stations, radiation observatories, specific observatories with respect to aviation, navigation, renewable energy, agriculture, environment and air quality.
Speed and range | With the gradual understanding of weather and climate, IMD ventured into numerical weather prediction modelling. Though it made a very humble beginning in 1950s, operational models were in place in 1990 with establishment of NCMRWF in 1988 equipped with supercomputer to provide the forecast for next 24 hours. Starting with simple persistence-based forecast in 1886, we now have a seamless modelling system for nowcasting (forecasting up to a few hours) to shortto medium-range forecast up to seven days issued daily, extended range forecast up to four weeks issued once a week, monthly and seasonal forecasts at the beginning of every month and season.
Addressing challenges | While IMD in collaboration with its sister organisation in MoES, R&D institutes, central and state stakeholders is moving ahead, demonstrating its capability in improving the national economy and helping to minimise loss of life, there are still challenges especially with respect to predicting small-scale severe weather hazards like cloud bursts and lightning. IMD aims at addressing all these in a collaborative approach with academia, R&D institutes, public-private partnerships and stakeholders.
Taming extreme weather | In a world besieged by the spectre of flash floods, heatwaves, and intense cyclones, the climate crisis is no longer a distant menace. The risks posed by extreme weather events transcend mere environmental concerns. Anticipating and preparing for these risks becomes imperative as human-induced climate change amplifies the intensity and frequency of such events. In response, we need a transformative solution that moves beyond the conventional playbook of weather management – an approach that not only predicts weather at different time and spatial scales, but actively shapes the weather in our favour.
From predicting to modifying | With over 56 countries engaged in weather modification activities, interventions like seeding or dispersing substances into clouds or fog, altering drop size distribution, producing ice crystals, coagulating droplets and influencing the natural development cycle of clouds are gaining traction. Though controversial, these interventions offer a potential key to weather resilience.
1990-2002: error margin of forecasts reducing
The Times of India, Apr 13 2016
IMD's error margin on rains reducing
Amit Bhattacharya
The India Meteorological Department has often faced public criticism for getting its monsoon predictions wrong despite the complexities involved in the process.Data, however, suggests IMD may be getting better at the exercise. The average error in IMD's monsoon forecasts in 2003-2015 has come down to 5.9%, from 7.9% in the previous 13-year period (1990-2002), according to the department's analysis.
An error of nearly 6% suggests that the difference between the forecast and actual rainfall is routinely beyond IMD's stated error margin of 5%. Hence, there's certainly room for improvement.
But given the high degree of difficulty in getting forecasts that are spread over four months correct within 6% of the prediction, the performance isn't too bad. Consider this: In the 27 years since IMD began making all India predictions in terms of percentages of normal rainfall, it has been way off the mark seven times -years when the difference between forecast and actual rainfall was 10% or more.
Its worst prediction came in the drought year of 2002, when the forecast-actual rainfall difference was a gaping 20%. The department again erred majorly in 2004, another drought year, when the forecast was off by 14%. These mistakes lead to a relook at the IMD's forecast method. In 2007, a new statistical approach, with eight variables being considered during a two-stage forecast system, was unveiled.
Although IMD was again off the mark in the very first year -having predicted below normal rains (93%) while the actual was above normal at 106% -the department has since got the “direction“ of the monsoon more or less correctly, even though the margins have been high in at least three of the eight years. The year 2015 was another milestone for IMD, when it predicted a drought for the first time and got it right.
Simultaneously , the Indian Institute of Tropical Meteorology has been fine-tuning a dynamical computer climate model, CFS, borrowed from the US in 2012.
Sources said the model's accuracy has been increasing and it could replace the statistical method in a few years.
Forecast models
Monsoon may be below par: IMD
TIMES NEWS NETWORK The Times of India
There are several models that the India Meteorological Department (IMD) refers to. For 2014 they issued forecasts by two such models. The monsoon mission model predicted 96% of the long period average while the ESSO IMD seasonal forecast showed 88% of the LPA.
The second model was very close in its assessment in 2013, However, “these are only experimental models and we cannot use their data with any kind of certainty.”
IMD has been periodically updating its methodology for the complex task of predicting the monsoon. The department, however, continues to battle the impression that its accuracy drops markedly when rains fail (see box).
In keeping with the latest updates from international agencies, IMD said that chances of an El Nino occurring in 2014 summer were high. “Latest forecast from a majority of the models indicate a warming trend in sea surface temperatures over the equatorial Pacific reaching to El Nino level during the southwest monsoon season, with a probability of around 60%,” the IMD said.
2013 was an ENSO neutral year — that is, there neither an El Nino nor its mirror opposite, La Nina. The southwest monsoon arrived 15 days before its normal date in June and continued till much after its scheduled date of withdrawal. Against a prediction of 98%, the country recorded an above-normal 106% rainfall.
