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Honours

  • Member, Editorial Advisory Board, Dynamics and Statistics of the Climate System ( a new journal being launched by Oxford University Press), February 2016 - present.
  • Adjunct Faculty, ICTS-TIFR 2011-2014.
  • Co-author of paper titled "A Gradient- Based Comparison Measure for Visual Analysis of Multifield Data" which won 3rd Prize in the Best Paper Category at EuroVis 2011.
  • Associate Editor, Journal of Earth System Sciences (published jointly by the Indian Academy of Sciences, Bangalore and Springer), 2008-2014.

Guidance of Students 

Degree No of Students Graduated Currently Guiding
Ph D. 8 7
M.Sc. (Engg)
4 0
M.Tech    
8 1

Selected Publications with Citations

*Indicates Student Author

 (Complete List of Publications attached at the end)


1. Tripathi S, V. V. Srinivas and R S Nanjundiah 2006: Downscaling of Precipitation for Climate Change Scenarios: A Support Vector Machine Approach. Journal of Hydrology, 330, 621-640. (279 citations)

2.  Sulochana Gadgil, M Rajeevan and R S Nanjundiah, 2005: Monsoon Prediction -Why yet another failure? Current Science, 88, 1389-1400. (140 citations)

3. Anandhi A* , V V Srinivas, R S Nanjundiah and D Nagesh Kumar,2008: Downscaling Precipitation to River Basin in India for IPCC SRES Scenarios using Support Vector Machine. International Journal of Climatology, doi: 10.1002/joc.1529, 28, 401-420. (125 citations)

4.  Gadgil S, J Srinivasan, R S Nanjundiah , K Krishna Kumar, A A Munot, and K. Rupa Kumar, 2002: On Forecasting the Indian Summer Monsoon :the Intriguing Season of 2002. Current Science,83, 394-403.(79 citations)

5.  Chakraborty A* , R S Nanjundiah and J Srinivasan, 2002: Role of Asian and African Orography on Indian Summer Monsoon. Geophysical Research Letters, 29, doi: 10.1029/2002GL015522.(51 citations)

6.  Rajendran K* , R S Nanjundiah, and J Srinivasan, 2002: Comparison of Seasonal and Intraseasonal Variation of Tropical Climate in NCAR CCM2 GCM With Two Different Cumulus schemes. Meteorology & Atmospheric Physics  79, 57-86.(35 citations)

7.  Vinaychandran P N and R S Nanjundiah, 2009: Indian Ocean sea surface salinity variations in a coupled model. Climate Dynamics, doi:10.1007/s00382-008-0511-6, 33, 245-263.(28 citatons)

8.  Bhat, G S, A Chakraborty* , R S Nanjundiah and J Srinivasan, 2002: Vertical Thermal Structure of the Atmosphere over North Bay of Bengal: Observation and Model Results. Current Science, 83, 296-302 (24 citations)

9.  Sahany S* , V Venugopal and R S Nanjundiah, 2010: Diurnal Scale Signatures of Monsoon Rainfall Over the Indian Region from TRMM Satellite Observations. Journal of Geophysical Research - Atmospheres, 115 D020103 doi:10.1029/2009JD012644, 2010. (22 citations)

10.  Michalakes J and R S Nanjundiah, 1994: Computational Load in Model Physics of the parallel NCAR Community Climate Model. ANL/MCS-TM-186, February 1994. (21 citations)


Industry Interaction

I am currently involved in a project sponsored by Intel Inc. titled "Intel Parallel Computing Centre for Modelling Monsoons and Tropical Climate (IPCC-MMTC)" for using computational accelerators such as Xeon-Phi for climate modelling. This research is being conducted in collaboration with National Centre for Atmospheric Research (NCAR) of USA.

Earlier I have been involved in projects sponsored by Microsoft and IBM on use of HPC for climate studies. I have also worked with India Meteorological Dept (which in our field could be considered as a user industry) on using numerical models for forecasting monsoons.


Membership of Committees

1.  Member, Project Monitoring Committee, High-resolution Operational Ocean Forecast and reanalysis System (HOOFS) of Ministry of Earth Sciences, 2013-Present

2. Member, Project Advisory and Monitoring Committee for Hydrosphere and Cryosphere of Ministry of Earth Sciences, 2013-Present.

3. Member, Expert Committee on Application Development, National Supercomputing Mission, 2016-Present

4. Domain Co-ordinator for Environment and Climate Change, Imprint-India, 2015-Present.


Details of Research Experience of Ravi S. Nanjundiah

My research has been focussed on trying to understand Indian monsoon and its variability by investiga-tions of a slew of atmospheric models including general circulation models (AGCMs), coupled Atmosphere-Ocean models (AOGCMs) as well as cloud system resolving models and on-line chemistry models , to as-sess the role of important factors and processes in the di erent observed facets of the monsoon. I have also assessed the performance of state of the art climate models in simulating/predicting the monsoon, its variability and its observed teleconnections so as to get an insight into how the skill in prediction could be improved . From the outset, I have also been deeply interested in the computational aspects and numerics of the models as well as use of machine learning methods. I have worked on improving the scalability of climate models, use of High Performance Computing, grid computing and computational accelerators for increasing computational throughput of climate models. All this has been achieved with successful collaboration with mathematicians, atmospheric scientists, oceanographers, hydrologists and computational scientists within and outside IISc, and with students.

A few of the investigations carried out are described brie y here to give a avour of the problems addressed, the approach adopted and results obtained.

I started research on the Indian Summer Monsoon in 1986 with the development of a simple climate model which could simulate realistically some of the most important features of the observed intraseasonal variability of the Indian monsoon such as the northward propagations of cloud-bands/rain-belts from the equatorial Indian Ocean onto the Indian monsoon zone at iintervals of 2-6 weeks throughout the summer monsoon season. We found that in the model the propagations occur due to gradient in vertical moist static instability (Nanjundiah et al,1992). This could potentially be useful for forecasting such propagations. While working on this model, I also began to use alternative computational techniques such as parallel computing so as to enable faster simulations on the available computers. I have continued work on monsoon modelling as well as on computational aspects and numerics of models throughout my career.


I. Studies of the simulation of the mean monsoon

Role of the spectacular mountain ranges of the Indian subcontinent and Myanmar in the mean monsoon has been emphasized in one of the early theories of the monsoon in the beginning of the last century. A special feature of the Bay of Bengal which sustains the monsoon with cloud systems from the region moving onto the Indian landmass, is the low salinity water near the surface due to river runo and rainfall. River runo could decrease due to increased impounding of river-waters. It is thus important to understand the role of river runo on the monsoon. It has been suggested that increasing aerosols, particularly, black carbon, may decrease the monsoon rainfall and the importance of considering the impact of aerosols, clouds and their interaction has been pointed out.

a. Impact of orography

Our investigation of what happens to the monsoon when the mountains over di erent parts of the globe are removed in an AGCM (the NCMRWF GCM) showed that, with the exception of African orography, removal of orography in any other part of the world including remote regions such as the Rockies and the Andes reduced the monsoon rainfall and delayed its onset, these e ects being largest for Western Himalayas (Chakraborty et al 2002, 2006). The absence of African orography strengthened the low level westerly jet and the rainfall over the Indian monsoon region because of non-linear feedbacks between rainfall and circulation that had been ignored in previous studies.

b. Monsoon & Ocean-Land-Atmosphere interaction

Comparison of a 100 year run of the Community Climate System Model (CCSM3) in which the river run-o is included with another similar run in which there is no river runo showed that when the river discharge was shut o , global average sea surface temperature (SST) rose by about 0.5C and the monsoon rainfall increased by about 10% with a large increase in the eastern Bay of Bengal and along the west coast of India. In addition, the frequency of occurrence of La Nia-like cooling events in the equatorial Paci c increased and the correlation between ISMR and Paci c SST anomalies became stronger. The teleconnection between the SST anomalies in the Paci c and monsoon was e ected via upper tropospheric meridional temperature gradient and a shift of the North African Asian Jet axis (Vinayachandran et al, 2015).

c. Monsoons, Aerosols & Clouds

Aerosols and their interaction with atmosphere especially in modulating radiative heating can have a signi cant impact on monsoons. Over India black carbon aerosols (from vehicular emissions) and dust can play a signi cant role in modulating monsoons.

When we incorporated the atmospheric heating e ect of black carbon aerosols (and cooling e ect on the ground) we found that the strength of the mean monsoon actually increased. However, the regions where signi cant changes in precipitation occurred were sensitive to the cumulus parameterization used. (Chakraborty et al, 2004, Chakraborty et al 2012). Our studies have also shown that aerosol radiative forcing over geographically remote regions such as Eastern China could impact the Indian Monsoon (Chakraborty et al 2014). We have also assessed the simulation of aerosol concentrations over India in an on-line chemistry model (Govardhan et al 2015, 2016)

Cloud related processes are important for monsoons and its simulation. We examined cumulus closure in two AGCMs and found that simulation of tropical rainfall in general, and Indian summer monsoon in particular , were sensititve to the rate at which clouds reduce moist instability. Lower rates of instability reduction gave more realistic simulations (Jain et al 2013, Mishra et al 2008) . We are studying convection over Bay of Bengal using a cloud system resolving model and are now attempting LES of convection over a tropical ocean.

II Assessment of the performance of climate models in monsoon simulation and prediction

An analysis of the retrospective predictions by seven coupled oceanatmosphere models from major fore-casting centres of Europe and USA showed that they had reasonable skill in predicting the extremes of the Indian summer monsoon rainfall, particularly those associated with ENSO. However, despite large inter-model di erences in the parameterizations, numerics etc., there was also a remarkable coherence between the failures of the models to predict the Equatorial Indian Ocean Oscillation (EQUINOO) and the monsoon in years in which EQUINOO played an important role. It was found that models were able to simulate ENSOmonsoon link realistically, whereas the link of monsoon rainfall with EQUINOO was simulated realis-tically by only one model the ECMWF model. Furthermore, in most models the simulated link is opposite to the observed, suggesting that prediction of EQUINOO and its links with the monsoon need to be improved for improving monsoon predictions by these models (Nanjundiah et al, 2013). We found that while the CGCMs used in IPCC AR4 were also able to simulate the ENSO-Monsoon relationship realistically, almost all models failed to simulate the EQUINOO-Indian monsoon relationship (Rajeevan and Nanjundiah, 2009).


III Machine Learning Techniques for Climate Change & Seasonal Prediction:

We have studied the impact of possible anthropogenic climate on smaller scales (such as that of a river basin or a meteorological subdivision) than those that can be resolved by IPCC scenario models by using statistical downscaling techniques ( e g Tripathi et al, 2006, Anandhi et al, 2012). We have shown that forecasts of the Indian summer monsoon rainfall on the seasonal scale using similar machine learning techniques have skills comparable to the operational statistical models (Saha et al, 2016)


IV Numerical Techniques for AGCMs:

We have investigated the impact of numerics and developed a framework for deriving splitting methods for semi-linear ordinary di erential equations and partial di erential equations (Murthy and Nanjundiah, 2000). We developed a technique for grid re nement for spectral technique using reparameterisation maps tailored for studying tropical convection and teleconnections. Grid re nement in spectral models is a much more challenging task than in grid-point models and only a handful of such e orts have been attempted (Janakiraman et al, 2012). We have developed a technique for analysis of space-time variation of tropical precipitation (Shanker and Nanjundiah, 2004, Chakraborty and Nanjundiah, 2012).


V HPC & Climate Modelling:

Scalability and throughput are important issues while using High Performance Computing. Any increase in these in e ect implies a faster and more e cient computer at no additional cost. My research (in collabo-ration with computational scientists) has shown that these can be improved through load-balancing, message compression and use of computational accelerators. Our work on load balancing (Sundari et al, 2009) for a climate-system model showed that moving high computational load related to radiation to less-loaded processors computing ocean and other components leads to 15% improvement in overall throughput. This was perhaps the rst of its kind for a multi-component climate system model.

The trend in computational architecture is to move towards accelerators such as GPUs (for lowering of energy requirement). I have worked on exploiting GPUs for climate modelling (Korwar et al, 2014). Radiation computations can be a signi cant part of a climate models execution time. By o oading the most time-consuming routines of radiation onto GPUs and exploiting asynchronous computing due to the fact that it is a slowly varying function of time, we could completely o set the time consumed in this part of execution. In the process these parts of the codes could be called more frequently and hence could result in higher accuracy.

Interprocessor communication is one of the biggest bottlenecks in parallel computing. Bigger the size of the communicated message higher could be the overhead. My work (Kumar et al ,2008) on message-compression exploits the fact that the model state does not change signi cantly between two consecutive timesteps and this could be exploited to reduce inter-processor communication. By sending information only about the changed parts of the message the speedup increases by about 18%. This was the rst application of message-compression to a climate model.

VI Grid Computing for Climate Modelling

Grid computing helps to agglomerate resources at geographically distinct locations. In a resources-starved country like India it is a good alternative to installing a large computer at a single location. We built a computational grid consisting of clusters at CAOS and SERC that could be used for climate-system model simulations (Sundari et al 2012, 2011) . This grid exploited the feature that a climate-system model consists of loosely coupled sub-components running concurrently with inter-component communication being low (in contrast to intra-component communication which could be much higher). The middleware for this purpose could recon gure computations whenever new/existing systems became available/unavailable. The ideas thus developed can also be used on future exa-scale computers which could consist of loosely-coupled computers (to prevent frequent failure).

VII Climate & Big Data

Datasets related to climate are now getting bigger due to higher availability of data from satellite and conventional observations, and due to higher resolution of simulations. Analysisng this data is now considered as one of the biggest challenges in Big Data. It is no longer possible for a researcher to manually search for a pattern within such large datasets. Hence automated techniques are a necessity. I have worked on developing algorithms (Doraiswamy et al, 2013) based on computational topology for tracking cloudsystems such as cyclones and the eastward moving cloudbands in the equator related to Madden Julian oscillations. It could also resolve movement of clouds within such cloudsystems . Another automated method using gradient based multi- eld comparison measure could identify hurricanes and its tracks in a large dataset such as a multi-century simulation from a climate model (Nagaraj et al 2011).