Policy Update
Tuba Athar
Background
Accurate prediction of monsoon rainfall is very crucial in a country like India, as it impacts many sectors like agriculture, water resources, power generation, transport, and even the Indian economy. Indian Summer Monsoon Rainfall (ISMR) amounts to more than 80 percent of the annual rainfall over India. It, hence, plays a significant role in the total food production of the country. Having prior knowledge of the variations in monsoon rainfall aids in preparing for droughts and floods and reduces the adverse impacts of the same.
The prediction of monsoon rainfall has been attempted for a long time, but there has been limited success. In the past, statistical models were used for monsoon prediction, but those models were unable to predict extreme weather conditions. Realising the importance of accurate prediction and weather forecasting, the Monsoon Mission was launched in 2012 by the Ministry of Earth Sciences with an allocation of 400 crore to develop the capability of dynamical model prediction systems for short-range to seasonal forecasts during the Monsoon season and to improve the monsoon forecasts.
Objective
The monsoon mission was focused on the following major objectives:
I. To build a working partnership between the Academic and Research & Development Organizations, both national and international, and the MoES to improve the monsoon forecast accuracy for the entire country.
II. To set up a state-of-the-art dynamical modelling framework for improving the prediction skill of (a) Seasonal and Extended range predictions and (b) Short and medium-range (up to two weeks) predictions.
III. Develop and implement a system for climate applications having social impacts (such as agriculture, flood forecast, extreme events forecast, wind energy, etc.)
Functioning
Four MoES institutes, namely the Indian Institute of Tropical Meteorology (IITM), Pune; National Centre for Medium Range Weather Forecasting (NCMRWF), Noida; Indian Meteorological Department (IMD), and Indian National Centre for Ocean Information Services (INCOIS), have partnered actively in this important and ambitious programme.
The execution and coordination of this mission is undertaken by IITM, which also leads the efforts for improving the extended range and seasonal predictions (for 16 days to one season). For this, IITM is collaborating with the National Centers for Environmental Prediction (NCEP), USA, and other MoES institutes and is working with the Climate Forecast System (CFS) model. This is a coupled ocean-atmosphere modelling system and combines data from the ocean, atmosphere, and land to provide long-range forecasting. Oceanic initial conditions are provided by INCOIS, and atmospheric initial conditions are provided by NCMRWF.
NCMRWF leads the efforts for short and medium-range forecasts (for up to 15 days). For this, NCMRWF is collaborating with The Meteorological Office, UK (commonly known as UK Met Office) and is working with the Unified Model (UM) for seamless prediction of weather with a special focus on short and medium range forecast of monsoons.
Monsoon Mission II
The research & development and operational (services) activities of MoES with respect to weather and climate-related phenomena are being addressed by one of the umbrella schemes, Atmosphere and Climate Research – Modelling, Observing Systems and Services (ACROSS). The entire gamut of weather/climate prediction involves assimilation of meteorological observations, understanding the processes, research and development of dynamical models, and providing the forecast services. Each of these aspects is incorporated as a sub-scheme under the umbrella scheme “ACROSS” and is being implemented through the India Meteorological Department (IMD), Indian Institute of Tropical Meteorology (IITM), Pune, and National Centre for Medium Range Weather Forecasting (NCMRWF).
There are nine sub-schemes under ACROSS, and one among them is Monsoon Mission II, including a High Resolution (12km) global ensemble forecast system (NITI Aayog identified activity) worked by IITM.
The first phase of the monsoon mission was completed successfully in 2017 and handed over the seasonal and extended range prediction system developed to the India meteorological Department (IMD) for operational use. The second phase of the monsoon mission started in October 2017. The second phase focuses on both model development and application development for agriculture, hydrology, energy, etc, sectors for the prediction of extreme weather events and developing climate applications.
Key Objectives and Activities of Monsoon Mission Phase II
Improving Forecasts: The core goal is to enhance the accuracy of monsoon forecasts across various time scales (seasonal, extended range, and short-to-medium range).
Developing a Seamless Prediction System: This involves creating a unified system that integrates forecasts from different time scales using the Monsoon Mission Coupled Forecast System (MMCFS).
Climate Applications: The mission aims to translate monsoon forecasts into practical applications for sectors like agriculture and water resource management.
Collaboration and Partnerships: MM-II fosters collaboration between national and international research institutions, including IITM, IMD, and other organizations, to improve forecast skill.
Infrastructure Development: The mission focuses on enhancing the computing infrastructure at IITM and NCMRWF to support advanced modeling and data assimilation.
Data Assimilation: The mission aims to enhance the quality of data analysis used in the forecasting models, particularly focusing on land data assimilation.
Model Development: MM-II involves further development of global and regional ocean models, including high-resolution models of the Indian Ocean.
Achievements of Monsoon Mission Phase II
According to the Annual Report of 2023-24 of the Ministry of Earth Sciences, some of the achievements of the mission are:
1. The Monsoon Mission Coupled Forecast System Version 2 (MMCFSv2) captures significant features of the Indian monsoon, including the intensity and location of the maximum precipitation centers and the large-scale monsoon circulation.
2. Unified model framework for Monsoon Variablity and Predictability (UMVP) had participated in a project to understand the sources of monsoon predictability and the study pinpointed spring soil and surface temperature over Tibetan Plateau as an important source of predictability of the June rainfall over several parts of the planet including the Asian Summer Monsoon Region.
3. The Global Forecast System provided accurate forecast of genesis, ensemble tracks, strike probability, intensity and landfall for the extremely severe cyclonic storm ‘MOCHA’ in May 2023 over Bay of Bengal, ‘BIPARJOY’ during June 2023 and ‘TEJ’ over Arabian Sea and ‘Hamoon’ over Bay of Bengal during October 2023.
The Annual Report of 2024-25 of the Ministry of Earth Sciences underlines some of the achievements of the mission as:
1. Global Forecasting System model runs operationally at IMD for times daily for forecasts up to ten days. This system incorporates various conventional and satellite observations, including radiance from multiple satellites.
2. The Extended Range Forecasts operationalised at IMD in 2017 runs weekly for 32 days based on Wednesday initial conditions, and it observed and forecasted weekly rainfall anomalies during monsoon 2014. On a smaller scale, the forecasts show up to two weeks and are used for agro-advisory purposes.
3. For agromet applications, a forecast for the 36 meteorological subdivisions of India is prepared for two weeks with categorizing the subdivisions as below normal, normal, or above normal category depending on the rainfall departure during the week. Which is again used for agro-advisory purposes.
Major Gap areas identified in Monsoon Mission-II
The various gaps have been provided according to each institute involved.
IITM
Short Range Prediction: Gap Areas
Current models struggle to predict the spatio-temporal variability of extreme rainfall events accurately and often fail to capture the correct probability density of rainfall.
Extended Range Prediction: Gap Areas
The predictions are not skillful beyond three weeks. There is a scarcity of reliable application-specific forecast skill evaluation/verification metrics. There is a gap in communication or user awareness on the uncertainties/limitations of the forecasts.
Seasonal Forecasting: Gap areas
Predicted spatial distribution of rainfall anomalies at seasonal time scales is not skillful in present-day climate models.
IMD
The seasonal skill of the model is poor over the Core Monsoon Zone (CMZ) and at the monthly scale. The skill of the model is poor for the Northeast Monsoon Season, as well. The skill of predicting of Indian Ocean Dipole index is moderate.
INCOIS
The model resolution of INCOIS-GODAS is very coarse (1 degree in zonal and 1/3rd of a degree in meridional) and, thus, cannot resolve mesoscale processes in most parts of the global ocean.
Way Forward
Though the Mission has greatly improved the weather forecasting and prediction system of Indian weather, however, for accurate presentation more efforts are identified in the mission. The Mission outlays that for prediction, there is a need for further improvement of forecasts at the short-range scale within the lead time of at least three days. Incorporating new methods to enhance the quality of analysis compared to what is available at present. For forecasts, also, improvement in the spatial and temporal scales. For short-range weather prediction, extreme events need more accurate prediction for the reliability of weather forecasts, and for extended range predictions, predictions need to be skillful beyond three weeks. Moreover, the communication gap between the provider and the user needs to be bridged.
The Arabian Sea has a vital role in shaping climatic patterns in the Indian Ocean region at various spatiotemporal scales. A scientific programme, Enhancing Knowledge of the Arabian Sea Marine Environment through Science and Advanced Training (EKAMSAT), is formulated as a joint research initiative between India and the USA to achieve this goal. The Indian component of EKAMSAT is already projected under Monsoon Mission-III.
References
1.https://www.tropmet.res.in/project_details.php?project_id=4&position=0, retrieved on June 21, 2025
2.https://monsoon-mission.tropmet.res.in/activity/2/Monsoon-Mission-II , retrieved on June 21, 2025
3.https://monsoon-mission.tropmet.res.in/other/major-gap-areas-identified-in-monsoon-mission-ii , retrieved on June 21, 2025
4.https://www.moes.gov.in/programmes/monsoon-mission-india?language_content_entity=en , retrieved on June 21, 2025
5.https://www.indiascienceandtechnology.gov.in/major-achievements/monsoon-mission-and-improved-monsoon-predictions , retrieved on June 21, 2025
6. Ministry of Earth Sciences. (2025). Annual report 2024–25. Government of India.
https://www.moes.gov.in/sites/default/files/AR-2024-Eng.pdf
7. Ministry of Earth Sciences. (2024). Annual report 2023–24. Government of India.
https://www.moes.gov.in/sites/default/files/Annual-Report-English-2023-2024_12mp.pdf
8. Press Information Bureau. (February 09, 2021). National Monsoon Mission. https://www.pib.gov.in/Pressreleaseshare.aspx?PRID=1696
9. National Council of Applied Economic Research. (2020). Estimating the economic benefits of Investment in Monsoon Mission and High Performance Computing facilities.
https://rsmcnewdelhi.imd.gov.in/uploads/survey/NCAER2020.pdf
About the contributor- Tuba Athar is a research intern at IMPRI. She is pursuing a PhD from the Centre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi.
Acknowledgement- The author sincerely thanks Aasthaba Jadeja and other IMPRI fellows for their valuable contribution.
Disclaimer- All views expressed in the article belong solely to the author and not necessarily to the organisation.
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