Latest revision as of 13:19, 26 June 2025

India Meteorological Department, some facts; Graphic courtesy: The Times of India, June 13, 2016

[edit] Expertise developed

[edit] 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.

[edit] 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.


[edit] Milestones

Neha Madaan, January 15, 2025: The Times of India

India Meteorological Department- milestones
From: Neha Madaan, January 15, 2025: The Times of India

It’s 1875. Arya Samaj is born, Asia’s first stock exchange comes up in Bombay, an agrarian crisis is brewing in the Bombay presidency and Henry Blanford establishes India Meteorological Department (IMD). For 150 years, IMD’s scientific evolution has been shaped by weather, colonial preferences and wartime necessities. Its historic buildings, including the heritage structure popularly known as Shimla Office in Pune inaugurated in 1928, speak of a storied journey.


British mathematician and meteorologist Sir Gilbert Walker was its director-general when he studied India’s peculiar weather patterns between 1904 and 1924 and published his findings on Southern Oscillation, the phenomenon that influences weather patterns worldwide. It laid the groundwork for understanding El Niño.


Moving addresses 


IMD’s geographical odyssey began in Calcutta (now Kolkata) but the heat prompted a shift to the cool climes of Shimla. The hill station posed logistical challenges and IMD came to Pune in 1928, OP Sreejith, head of climate monitoring and prediction group at IMD Pune, said.


During World War II, its headquarters moved to Delhi, driven by aviation forecasting for military operations and Pune became crucial for meteorological research. SK Banerji was its first Indian director general in 1944. 
“Since then, IMD has gone from predicting weather to impact-based forecasts of extreme events,” Sreejith said.


Forecasting for crops


India's first agricultural meteorology division was set up in Pune in 1932, Kripan Ghosh, head of IMD's agrimet division, said. Under LA Ramdas, the division began experiments at College of Agriculture in Pune and put out the first farmers’ weather bulletin in 1945.


From state-level advisories in 1977 to hyperlocal forecasts, IMD's agricultural services have become precise, Ghosh said. The 127 agro-climatic zones set up in the 1980s revolutionised the approach.


IMD’s Meghdoot app and WhatsApp provide block-level weather forecasts and district-level agro-meteorological advisories to farmers. Ghosh said, “During cyclones, crucial advisories reach farmers through the Kisan portal and state agricultural platforms.”

[edit] SUNSHINE RECORDER

A glass sphere on a metal frame creates a record of daily sunshine hours. "The sun's rays passing through the glass sphere leave burn marks on these cards, like the sun's signature," Kripan Ghosh, head of agrimet division, said. Sunshine hours directly affect photosynthesis and crop growth. Sunshine duration measures the potential for crop development

[edit] 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.

[edit] Forecast models

How accurate have IMD forecasts been in the past?

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.

[edit] Monsoon forecasting

[edit] History

Alind Chauhan, April 28, 2025: The Indian Express

The first forecasts

A systematic effort to forecast monsoon rainfall began in 1877, two years after the IMD was established with the British meteorologist and palaeontologist Henry Francis Blanford as the first Meteorological Reporter to the Government of India.

Crop failure that began in the Deccan plateau in the previous year had set off the Great Famine of 1876-78, and the effects were felt across the country by 1877. The colonial administration saw an acute need to understand the arrival of the monsoon and the distribution of rain over the country.

“The success of the monsoons dictated agricultural production and the health of rivers, coasts, and shipping lanes — i.e., revenue generation for British interests,” Ramesh Subramanian of Quinnipiac University in the US wrote in his paper ‘Monsoons, Computers, Satellites: History and Politics of Weather Monitoring in India’ (2021).

SNOW & RAIN…: The first tentative forecasts of the monsoon were provided by Blanford between 1882 and 1885, who analysed the relationship between Himalayan snow cover and the amount of rainfall over the Indian region.

Blanford’s forecasts were “based on the inverse relationship between Himalayan winter and spring snow accumulation and subsequent summer monsoon rainfall over India. It was assumed that, in general, varying extent and thickness of the Himalayan snow has a great and prolonged influence on the climate conditions and weather of the plains of northwest India,” the IMD says in its official account of the evolution of meteorology in India.

In 1886, Blanford made the first long-range forecast (LRF) of monsoon rainfall for the whole of India and Burma, based on this inverse relationship hypothesis.

Blanford was succeeded by Sir John Eliot, who was appointed the first Director General of Indian Observatories, equivalent to the position of the head of the IMD today, in May 1889 at its Calcutta headquarters.

…PLUS SOME OTHER FACTORS: Eliot took forward Blanford’s work, combining data on Himalayan snow with factors such as local Indian weather conditions in April-May and conditions over the Indian Ocean and Australia to issue his LRFs.

But like Blanford, Eliot still could not effectively predict droughts or the famines that followed, bringing starvation and deaths. The Indian Famine of 1899-1900, which is estimated to have killed between a million and 4.5 million people, struck in a year for which Eliot had predicted better-than-normal rain. The first colonial official who sought to incorporate the influence of global factors on the Indian monsoon was the physicist and statistician Sir Gilbert Walker, who succeeded Eliot in 1904.

28 PREDICTORS, STATISTICAL CORRELATIONS: Walker developed the first objective models based on statistical correlations between monsoon rainfall and antecedent global atmospheric, land, and ocean parameters. To make his forecasts, Walker identified 28 parameters or predictors with a significant and stable historical relationship with the Indian monsoon.

Walker described three large-scale see-saw variations in global pressure patterns — Southern Oscillation (SO), North Atlantic Oscillation (NAO), and North Pacific Oscillation (NPO). “Among these, SO was found to have the most significant influence on the climate variability of India as well as many parts of the globe… The SO…was later linked to the unusual warming of sea surface waters in the eastern tropical Pacific Ocean or El Niño by Jacob Bjerknes in the 1960s,” says the IMD.

Walker also reasoned that the Indian subcontinent could not be considered as an undivided whole for the purpose of forecasting the measure of rainfall, and divided the region into three subregions: Peninsula, Northeast, and Northwest India.

After Independence

The IMD stayed with Walker’s model of monsoon forecasting until 1987. The forecasts were not very accurate. “The average error of the predictions for the peninsula was 12.33 cm and 9.9 cm for NW India during the period 1932-1987,” M Rajeevan, a former Secretary to the Ministry of Earth Sciences, and IMD Scientist D R Pattanaik wrote in their paper, ‘Evolution of Monitoring and Forecasting of Southwest Monsoon’ (Mausam, IMD’s quarterly journal, 2025).

The main problem was that several of the parameters identified by Walker had lost significance over time — meaning their relationship with the monsoon was no longer the same. IMD scientists attempted several tweaks to the model, but its accuracy did not improve greatly.

GOWARIKER MODEL: In 1988, the IMD began to issue operational forecasts of the monsoon based on a power regression model developed by scientists led by Vasant R Gowariker, which used 16 empirically derived atmospheric variables as predictors in a statistical relationship with the total rainfall.

The forecast for geographical regions was discontinued in favour of a forecast for the season over the country as a whole. Operational forecasts for Northwest India, Peninsular India, and Northeast India were reintroduced in 1999, but the geographical boundaries of these regions were different.

Similar issues emerged in the new model as well. “In the year 2000, it was realised that out of the sixteen parameters, four of them have lost their correlation” with the monsoon, “and hence they were replaced by other predictors”, wrote Suryachandra A Rao, Prasanth A Pillai, Maheshwar Pradhan and Ankur Srivastava in their 2019 paper, ‘Seasonal Prediction of Indian Summer Monsoon in India: The Past, the Present and the Future’ (Mausam).

The IMD’s regional forecasts remained inaccurate during this period. “The forecast error was more than model error for years like 1994, 1997 and 1999,” Rao et al wrote.

The power regression model was critically evaluated after it failed to predict the drought of 2002 that followed 14 good monsoons and was the worst since 1987.

TWO NEW MODELS: In 2003, the IMD introduced two new models of monsoon prediction, with eight and 10 parameters. It also adopted a new two-stage forecast strategy. The first stage forecast was issued in mid-April, and an update or second stage forecast was issued by the end of June. The new models accurately predicted the 2003 monsoon, but failed to forecast the drought of 2004, sending the IMD back to the drawing board.

The Department re-evaluated its models with two major objectives: “(a) a re-visit of the suitable and stable predictors, which have physical relationships with monsoon rainfall and (b) critical way of model development based upon identifying the optimum number of predictors and optimum model training period etc.,” according to the 2019 study.

STATISTICAL FORECASTING SYSTEM: In 2007, the IMD came up with a Statistical Ensemble Forecasting System (SEFS) to support its two-stage forecast strategy, and further reduced the number of parameters in its models.

A five-parameter model replaced the eight-parameter model for the first forecast in April, and a new six-parameter model replaced the 10-parameter model for the forecast update in June. The intention was to ensure there was no “overfitting” of models, in which a model matches or memorises the training set so closely that it fails to make correct predictions based on new data.

The Department also introduced the concept of ensemble forecasts. In this method, all possible forecasting models based on all the combinations of predictors are considered to create a single, more robust prediction.

The new system helped the IMD improve its forecast significantly. The average absolute error (difference between forecast and actual rainfall) between 2007 and 2018 was 5.95% of the LPA (rainfall recorded over a particular region for a given interval) compared with the average absolute error of 7.94% of LPA between 1995 and 2006.

Forecasts in recent years

COUPLED DYNAMIC MODEL: The improvement in monsoon prediction was also due to the launch of the Monsoon Mission Coupled Forecasting System (MMCFS) in 2012. This was a coupled dynamic model, which could combine data from the ocean, atmosphere, and land to provide more accurate forecasts. The IMD used MMCFS along with the SEFS for its predictions.

MULTI-MODEL ENSEMBLE: The accuracy of forecasts was further enhanced with the launch of a system based on a “multi-model ensemble (MME)” in 2021. This new MME system used the coupled global climate models (CGCMs) from various global climate prediction and research centres, including India’s own MMCFS model.

Since the introduction of SEFS in 2007 and the MME approach in 2021, the IMD’s operational forecasts for the monsoon have improved noticeably, the Ministry of Earth Sciences informed Parliament this February.

BETTER FORECASTS, SCOPE FOR IMPROVEMENT: The absolute forecast error in all of India’s seasonal rainfall reduced by about 21% during the years 2007-2024 compared with the same number of years between 1989 and 2006, Earth Sciences Minister Dr Jitendra Singh told Rajya Sabha.

IMD’s April forecasts, too, have become more accurate. The actual rainfall in the previous four years (2021-2024) deviated from the April forecast by 2.27 percentage points, well within the forecast range of 4%.

However, there is still much scope for IMD to improve. In their paper, Rajeevan and Pattanaik pointed out that the Department should refine its dynamical models by improving systematic errors/ biases and teleconnectivity — significant relationships or links between weather phenomena — with global climate modes such as ENSO.

[edit] Revenues

[edit] 2022- 25

Vishwa Mohan, March 30, 2025: The Times of India

New Delhi : While India Meteorological Department (IMD) is largely known for providing weather alerts and warnings in the public interest, benefiting various sectors and the common people, the country’s national weather forecaster has now also emerged as a key govt scientific agency generating revenue from its range of services.


Becoming the biggest money earner of the ministry of earth sciences (MoES), IMD has earned more than Rs 226 crore since 2022-23, with a substantial amount coming to its kitty from the aviation meteorological services provided to Airports Authority of India (AAI).


MoES informed a parliamentary panel that another institution — Chennaibased National Institute of Ocean Technology (NIOT) — has developed about 42 technologies which were transferred to industry on paid basis. It earned more than Rs 24 crore in the past three years.


Besides rendering the aviation met services to AAI, IMD earned revenue by sale of meteorological data, periodical weather reports and testing/calibration of equipment. Data shared with the panel — parliamentary standing committee on science and technology, environment, forests and climate change — shows that IMD earned nearly Rs 66 crore by providing aviation met services during the first six months of 2024-25 financial year (till Sept 2024).


“This (generating reven- ue from its services) will not only help the ministry to generate additional resources for augmenting important programmes, but also transfer the benefit of these technologies to the society at large,” said the panel, headed by BJP Lok Sabha member Bhubaneswar Kalita.


Keeping in mind the specialised requirements of the renewable energy sector, a new data product suitable to the requirements of the sector has also been developed.


The medium range forecasts (up to 10 days) of near surface variables (rainfall, temperature, surface pressure, humidity and winds) and upper air variables (winds, temperature humidity and geopotential height) are used for monitoring and early warning of severe weather events.


Though these forecasts are used by IMD and other national and state level disaster agencies, special steps have been taken in the past few years to make the data available to the paid clients in a secure data access.

[edit] See also

Droughts: India

Drought of 2016: India

El Niño, La Nina and India

Monsoons: India