India Meteorological Department - Revision history

Jyoti: /* Monsoon forecasting */

2025-06-26T13:19:10Z

‎Monsoon forecasting

Jyoti: /* Forecast models */

2025-06-26T13:09:13Z

‎Forecast models

Jyoti: /* Forecast models */

2025-04-10T06:44:33Z

‎Forecast models

Jyoti: /* An overview */

2025-01-27T18:52:41Z

‎An overview

Jyoti at 11:38, 16 August 2024

2024-08-16T11:38:59Z

Jyoti at 11:37, 16 August 2024

2024-08-16T11:37:35Z

Jyoti: /* 1875- 2023 */

2024-02-27T18:41:55Z

‎1875- 2023

Jyoti at 06:35, 19 July 2023

2023-07-19T06:35:59Z

Jyoti: /* Expertise developed */

2023-07-19T06:33:01Z

‎Expertise developed

Pdewan: /* Forecast models */

2023-07-15T08:52:54Z

‎Forecast models

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 [https://indianexpress.com/article/explained/explained-climate/the-history-and-evolution-of-monsoon-forecasting-in-india-9969292/ Alind Chauhan, April 28, 2025: ''The Indian Express'']
 ''' The first forecasts '''
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 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.
 [[Category:Climate/Meteorology|M INDIA METEOROLOGICAL DEPARTMENT

← Older revision		Revision as of 13:09, 26 June 2025
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	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.		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.
		+
		+	=Monsoon forecasting=
		+	==History==
		+	[https://indianexpress.com/article/explained/explained-climate/the-history-and-evolution-of-monsoon-forecasting-in-india-9969292/ Alind Chauhan, April 28, 2025: ''The Indian Express'']
		+
		+	The India Meteorological Department (IMD) has forecast ‘above normal’ rainfall — 105% of the long-period average (LPA) — during the June-September southwest monsoon season.
		+
		+	The IMD said earlier this month that all major drivers of the Indian monsoon, such as El Niño-Southern Oscillation (ENSO) in the equatorial Pacific Ocean and the Indian Ocean Dipole (IOD), were favourable.
		+
		+	The four-month southwest monsoon season brings almost 70% of the country’s annual rainfall. It is critical for agriculture and crops, for the economy as a whole, and to recharge reservoirs and aquifers. Accurate forecasts of the monsoon are key for the government to prepare for a range of eventualities.
		+	The April 15 forecast was the first of the IMD’s long-range forecasts for this year’s monsoon. A second-stage or updated forecast will be made in the last week of May, ahead of the monsoon striking the coast of Kerala. Long-range forecasts can be made for 30 days to up to two years into the future.
		+
		+	''' 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.
		+
		+	[[Category:Climate/Meteorology\|M INDIA METEOROLOGICAL DEPARTMENT
		+	INDIA METEOROLOGICAL DEPARTMENT]]
		+	[[Category:India\|M INDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENT
		+	INDIA METEOROLOGICAL DEPARTMENT]]
		+	[[Category:Pages with broken file links\|INDIA METEOROLOGICAL DEPARTMENT
		+	INDIA METEOROLOGICAL DEPARTMENT]]

	=Revenues=		=Revenues=

← Older revision		Revision as of 06:44, 10 April 2025
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	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.		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.
		+
		+	=Revenues=
		+	==2022- 25==
		+	[https://epaper.indiatimes.com/article-share?article=30_03_2025_034_010_cap_TOI 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.
		+
		+	[[Category:Climate/Meteorology\|M INDIA METEOROLOGICAL DEPARTMENT
		+	INDIA METEOROLOGICAL DEPARTMENT]]
		+	[[Category:India\|M INDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENT
		+	INDIA METEOROLOGICAL DEPARTMENT]]
		+	[[Category:Pages with broken file links\|INDIA METEOROLOGICAL DEPARTMENT]]

	=See also=		=See also=

← Older revision		Revision as of 18:52, 27 January 2025
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	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.		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.
		+
		+	===Milestones===
		+	[https://epaper.indiatimes.com/article-share?article=15_01_2025_018_016_cap_TOI Neha Madaan, January 15, 2025: ''The Times of India'']
		+
		+	[[File: India Meteorological Department- milestones.jpg\|India Meteorological Department- milestones <br/> From: [https://epaper.indiatimes.com/article-share?article=15_01_2025_018_016_cap_TOI Neha Madaan, January 15, 2025: ''The Times of India'']\|frame\|500px]]
		+
		+
		+	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.”
		+
		+	===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
		+
		+	[[Category:Climate/Meteorology\|M INDIA METEOROLOGICAL DEPARTMENT
		+	INDIA METEOROLOGICAL DEPARTMENT]]
		+	[[Category:India\|M INDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENTINDIA METEOROLOGICAL DEPARTMENT
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	==1990-2002: error margin of forecasts reducing==		==1990-2002: error margin of forecasts reducing==

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 =Expertise developed=
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 [[Monsoons: India]]
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 INDIA METEOROLOGICAL DEPARTMENT]]
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@@ Line 18: / Line 18: @@
 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.
@@ Line 49: / Line 50: @@
-Why Forecasts Go Wrong
+'''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.
@@ Line 61: / Line 62: @@
 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.
 ==1990-2002: error margin of forecasts reducing==

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 =Expertise developed=
 ==1990-2002: error margin of forecasts reducing==
 [http://epaperbeta.timesofindia.com/Article.aspx?eid=31808&articlexml=IMDs-error-margin-on-rains-reducing-13042016025025 ''The Times of India''], Apr 13 2016
@@ Line 32: / Line 82: @@
 Sources said the model's accuracy has been increasing and it could replace the statistical method in a few years.
 =Forecast models=