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Seasonal and Extended Range Prediction

(Chief Project Scientists: Dr. A. Suryachandra Rao, Dr. A.K. Sahai)
 Sub Projects
Model Development and Prediction: Seasonal
(Chief Project Scientist: Dr. Suryachandra Rao)
       The seasonal prediction of the Indian summer monsoon rainfall (ISMR) is very important for India, especially for planning strategies towards management of agricultural production and water resources. The seasonal prediction of the monsoon by dynamical models is based on the fact that the slowly varying boundary conditions like sea surface temperature (SST), soil moisture, snow cover etc. exert significant influence on atmospheric development on seasonal time-scales in the tropics. Although the seasonal mean monsoon seems to be potentially predictable, atmospheric GCM simulations have not shown enough skill in capturing the inter-annual variations in the monsoon rainfall. Indian Summer Monsoon has limited potential predictability. It has also been recognized that ocean-atmosphere coupling is crucial in determining the potential predictability of the monsoon. Therefore, a coupled ocean-atmosphere climate model will be required for predicting the monsoon. It is essential to develop and improve a system of fully coupled ocean-atmosphere-land modeling system for dynamical prediction of the seasonal mean monsoon rainfall. IITM will develop such a system and transfer it to the India Meteorological Department (IMD). Once fully developed, the system will give lot of spin-off in science, e.g., one should be able to study the role of air-sea interactions on monsoon variability and predictability in more details.
Recent studies have demonstrated the possibility of achieving improved skills in simulating the seasonal mean monsoon rainfall by using ocean-atmosphere coupled models. This improvement appears to result from more accurate representation of the coupled interactions between the Indian monsoon and the tropical oceans. During 11th Five Year Plan period, IITM scientists have setup an ocean-atmosphere coupled model on its IBM P6 575 (Prithvi) High Performance Computing (HPC) system and made long period free runs as well as hindcast (retrospective) experiments to test the model with set of initial conditions (e.g., with ensembles of atmospheric and oceanic initial conditions). The model outputs have been analyzed and its performance for simulating ISMR was examined. Certain biases in model simulations have been identified and efforts are being made to reduce these model biases. In addition to the research efforts on the coupled model, IITM has provided, for the first time in India, reliable experimental coupled dynamical monsoon prediction to IMD for further dissemination to general public. Certain modifications (e.g., better physical parameterizations and better representation of air-sea interaction processes, higher resolution) need to be incorporated for making this model better suitable for our region, that can lead to enhanced model skill of simulating ISMR. IITM proposes to develop the system with the following objectives:


To develop an Indian model based on CFS coupled model. (It will also be a part of national Indian Monsoon mission)

  • To implement Assimilation Modules at IITM and to start experimental assimilation of ocean and atmospheric data.
  • To carry out research for better understanding of monsoon coupled ocean-land-atmosphere interactions using observational datasets and diagnostics of coupled ocean – atmosphere model.
  • To improve prediction skill for both summer (SW) and winter (NE) monsoon over Indian region by improving the parameterization schemes of the model and replacing different modules in the system.
  • To transfer the model to IMD for operational forecast of Indian monsoon.
  • To develop Multi-Model Ensemble (MME) system for monsoon predictice.
Model Development and Prediction: Extended Range
(Chief Project Scientist: Dr. A.K. Sahai)
Indian summer monsoon season has periods of active (above normal rainfall) and break (below normal rainfall) epochs. Frequent or prolonged breaks lead to drought conditions. The long breaks in critical growth periods of agricultural crops lead to substantially reduced yield. Poor rice production in India during 1972, 1979 and 1987 appear to be due to such long breaks. Prediction of monsoon active and break spells, two to three weeks in advance, therefore assumes great importance for agricultural planning (sowing, harvesting, etc.) and water management. Hence, there is a need for development of techniques based on statistical and dynamical methods for the forecasting of active/break periods in ensuing monsoon season in the extended range time scale.
IITM has developed an empirical system to predict the active and break spells 3-4 weeks in advance and n transferred the model to IMD for issuing operational forecasts. The model is being in use at IMD for issuing operational forecasts since 2010 and the model predictions are reliable. IITM further proposes to use a fully coupled dynamical system for predicting the active/break spells of summer monsoon rainfall with the following objectives:
  • To develop new empirical techniques and improve the existing empirical models for improving the prediction skills of active and break phases of monsoon.
  • To implement, evaluate, validate and improve the forecast skills of Coupled Climate Forecast System (CFS) for extended range forecast.
  • To design, employ and test the cloud “super parameterization” concept in the CFS model.
  • To carry out basic research to understand complex atmospheric/oceanic processes, model parameterization schemes to improve the forecast skills using global and regional dynamical models.
  • To disseminate forecast in real time using both empirical and dynamical models.
  • To develop dedicated manpower to take up research challenges to improve quality of forecast.



Ocean Model Development and Data Assimilation


(Deputy Chief Project Scientist: Dr. C. Gnanaseelan)


Objectives :

  • Development of Data assimilation system for Climate Forecast System (CFS)
  • Ocean model development for Climate Forecast System (CFS)
  • Understand the ocean processes and air sea coupling in Climate Forecast System (CFS)




Parameterization and Analysis


(Deputy Chief Project Scientist: Dr. P. Mukhopadhyay)


 Objectives :

  • Identification of model (CFS) biases and its sources related to cloud and convective processes.
  • Improving the model (CFS) biases through development of cloud and convective parameterization.
  • Developing and applying new approaches of cloud and convective parameterization in CFS.