Research Areas

Climate-informatics : Machine learning in atmospheric and climate science   

PI: Dr. Saurabh Das

The major focus of this research group is to use and develop sophisticated statistical and Machine Learning/ Artificial Intelligence for understanding of the climate as well as developing novel techniques for extreme weather forecasting/nowcasting using satellite and ground-based systems.   

Climate change is one of the major concerns for both scientists as well as common people. One of the important consequences is the increase of extreme weather events. There is lots of research going on to understand the physics and projection of the same.  It is, however, still not fully understood or modelled due to the complex role of climate parameters. Further, the physics based models and numerical models are computationally heavy. One of the major issues is to parameterize the sub-grid scale process which ultimately helps in improving the high resolution projection. Identification of different rain climatology and its susceptibility to climate change, rainfall prediction, extreme weather like thunderstorms/lightning prediction and cyclone nowcasting are some of the key areas that we work on. Another area that we are working on is the retrieval of atmospheric parameters such as wind and precipitation from satellite data. Currently we are involved in retrieval of wind parameters by assimilating Doppler Weather Data with upcoming OceanSat 3 data. 

Fig. 1 shows one such example of developed Deep Learning based model for cyclone prediction. The leftmost figure shows the track of cyclones for the last 50 years. It can be noticed that the tracks are highly non-linear. The Right top figure shows the number of cyclones over the last 50 years. The right bottom figure shows the prediction of a developed LSTM based model one -day in advance for the track of the cyclone Amphan, which wreaked havoc in 2020.

Fig. 1: (a) Cyclone track, (b) Yearly cyclone occurrences of NIO and (c) actual and projected track of cyclone

Figure 2 shows another example of the ML application in classifying the rain region over India. It has the potential advantages of including satellite data with ground network and hence incorporating more information for efficient pattern identification. Climate change can then be further studied as well predicted efficiently for the homogenous region. 

Fig 2: New rain homogenous region based on satellite and ground data using ML technique and the inter-relationship between two parameters.

We welcome researchers interested in climate change, atmospheric science, ML/AI and data science to join us for collaboration and as PhD / Post-docs in this group. 

Radio remote sensing of the atmosphere and precipitation microphysics

PI: Dr. Saurabh Das

Experimental study of atmospheric remote sensing, particularly on the rain and related atmospheric parameters are the key area of this research group. At IIT Indore, continuous measurements are going on for different atmospheric parameters, such as electric field, lightning, water vapor, rain drop size distribution, Ionosphere etc. In addition, we are involved in field campaigns in the Arctic and other locations in India with collaboration with Indian Institute of Tropical Meteorology (IITM), Indian Space Research Organization and National Centre for Polar and Ocean Research (NCPOR). 

Figure below shows the available instruments with this group for atmospheric study.

Optical disdrometer
Lightning detector
Electric Field Mill

India is probably the place with the highest degree of spatial inhomogeneity when it comes to rainfall. Each part of the country comes with a different set of rain features, uncertainties and behavioural variations. The tropical rain has very distinct features. The occurrence of convective systems as well as the monsoon have different impacts on precipitation structure. Understanding and successful prediction of rainfall asks for detailed microphysical analysis in several rain aspects. The country being a zone of tropical convergence provides a wonderful opportunity to study severe convective systems. 

Polar (Arctic) precipitation on the other hand is very critical for understanding climate change. Polar precipitation is mostly of frozen type and has completely different microphysical characteristics.

Our research interest includes study of various microphysical aspects of both tropical and polar rain with a principal focus on quality improvement of satellite and ground radar retrieval of rainfall. Experimental observation of tropical rain is a key criterion to understand the underlying physics. Radar and satellite observations are primarily used to understand the rain and cloud microphysics.

Fig. 2 shows the rain microphysical behaviour under different rain condition. Measurements of varied rain microphysics helps in better representation of the process in NWP models.

Fig 2: Rain microphysics in (a) Stratiform rain
Fig 2: Rain microphysics in (b) Convective rain observed through Micro Rain Radar

On the other hand, understanding the atmospheric interaction with microwave and millimeter wave is crucial for both remote sensing and communication applications. Since several Indian and international satellites (GSAT-4, GSAT-14, Ka –band altimeter in SARAL-Altika, Megha Tropique, GPM etc.) are utilizing Ka band, the study on this topic is very pertinent to scientific priorities. This not only helps in improving the radar retrieval of the atmospheric parameters but also in channel modelling Ka and higher frequencies for SATCOM applications. We are also working on developing IoT based cost-effective sensors for  atmospheric instrument. Fig. 3 shows the in-house developed Ku/Ka band rain attenuation measurement system based on SDR and commercial LNA.

This figure is an experimental system of in house developed Ku band system
This figure is also an experimental system of in house developed Ku band system
This figure shows the rain attenuation at Ka band

We welcome researchers interested in atmospheric science, tropical meteorology, polar atmosphere, radar meteorology and instrumentation to join us for collaboration and as PhD / Post-docs in this group.

Space Based Navigation and GNSS remote sensing

PI: Dr. Saurabh Das

Precise positioning and navigation is one of the critical components of modern day’s economic and societal well-being. The use of satellite based navigation revolutionizes many areas of day-to-day life as well very important for defence, energy, transport, flights, space activities and other areas. GPS is the forefather of space based navigation systems. However, now many such systems are now available from several countries like China, European Union, Russia, Japan etc. Indian also developed its own navigation system, called NavIC.   

Moreover, planetary exploration requires an autonomous navigation system beyond the satellite based system. The use of Pulsar for a GPS like system, but on a celestial scale are currently being explored. 

The study of ionospheric effect on navigational systems is essential in regions like India with severe ionospheric activity and also very relevant to national priorities of making Indian GPS like systems, GAGAN and NavIC. Further, atmospheric components like water vapor play a key role in precise positioning. The Understanding and modelling of ionosphere, troposphere and other parameters for GNSS systems have consequences on precise position systems and navigation. Figure 1 shows the schematic of developed two shell ionospheric model and the performance of Klobuchar Like model over Indian region. 

(a) The Two Shell Ionospheric model
(b) performance of new Klobuchar-like model

GNSS signals are also useful for remote sensing of Earth’s environment. As the number of signals are increasing with addition of new satellite constellations and new frequencies, a new opportunity emerges to study both lower atmospheric as well ionospheric phenomena. Our research is specially focusing on monitoring tropospheric variables such as atmospheric water vapour by using the signal from Global Navigation Satellite System (GNSS) network as well as our newly launched Indian Regional Navigation Satellite System, NavIC. The near real time accurate estimation of atmospheric water vapour is very much helpful to predict the extreme weather events. Figure 2 shows the TEC variation observed by our algorithm as well the water vapor.

(a) The TEC variation over Kolkata
(2) performance of PWV retrieval from GPS data using our model
Figure 3: Detection of thunderstorm using NavIC satellite with Dynamic Time Warping Technique.

The impact of highly energetic lower atmospheric disturbances such as cyclones, thunderstorm generated very high intensity lightning strikes has been seen in the upper atmosphere by the interaction of generated very low frequency signals with the layers of ionosphere. The continuous monitoring of ionospheric total electron content retrieved from GNSS signal is very helpful for detection of such low frequency signals and hence the characterization of such lower atmospheric disturbances.

Figure 4: Detected low frequency signals by navigation satellites after severe lightning strikes.

We welcome researchers interested in GNSS, Satellite communication and navigation, remote sensing and ionospheric physics to join us for collaboration and as PhD / Post-docs in this group.

Machine learning in Space Weather Forecasting

PI: Saurabh Das

Space weather refers to the branch of space physics, deals with the time varying conditions of the space surrounding the Earth. It essentially includes conditions in the magnetosphere, ionosphere, thermosphere, and exosphere. Solar wind modulates the space weather and introduces several effects on the technological systems, such as atmospheric drag in satellites, power disruption, disturbance in HF communication. Hence forecasting space weather disturbances accurately is  an active area of research.

On the other hand, lately, tremendous growth has been observed in Machine Learning (ML) and Artificial Intelligence (AI). The application of ML/AI in diverse domains yields very encouraging results in both data mining and prediction. The application of ML/AI in Space weather and solar physics study is at nascent phase, but still  provides very interesting results. Given the large volume of data captured by several satellites, it gives an excellent opportunity to explore the domain from a data science perspective. 

The main research thrust of this group is to develop and use algorithms to study and forecast the space weather effects. In different phases of the solar cycle, the automatic classification and identification of active regions, Coronal Holes and it’s corresponding solar wind are key in understanding solar physics. The prediction of solar flare, CME arrival time, solar energetic particles(SEPs) ,high-energy electron fluxes, Geomagnetic index, and Ionosphere disturbances have significant importance in both scientific and economic perspectives. We have a large and open source data set of in situ and remote observations collected over several decades through various space missions . Effective use of these data through ML/AI, may provide interesting and significant changes in space weather studies.

Following Figure 1 shows the application of a developed algorithm for detection of coronal holes with other methods. We have already developed a Fuzzy based image processing technique to detect the coronal holes, which is one of the important sources of high solar winds.

Fig. 1: Original SSO/AIA images with other techniques for detection of coronal holes. The last one is our developed technique.

We are currently working on Deep learning based models for forecasting of solar wind and CME. Figure 2 shows the result of a deep learning model for solar wind prediction. The initial work is carried out using Convolutional Neural Network(CNN) for solar wind forecasting. We developed a CNN model from scratch to forecast solar wind speed from SDO/AIA images. Following figure shows the prediction of our CNN based model for slow and fast solar wind 4-days in advance. This forecasting scheme can predict both the fast and slow wind well with a RMSE of 76.3±1.87 kms-1 and an overall correlation coefficient of 0.57±0.02 for the year 2018, while significantly outperforming bench-mark models. The threat score for the model fares around 0.56. In 65% cases, the proposed model can accurately forecast the occurrence of HSEs.

Fig. 2: Comparison of predicted solar wind speed by proposed CNN model with observed solar wind speed for the year 2018 along with High Speed Enhancements(HSEs)

Figure 3 shows the regions identified by the CNN model responsible for slow and fast wind. This indicates the advantages of using a data science approach for mining new knowledge.   

Figure 3. Activation heatmap for fast and slow wind through Grad-CAM technique. (a) shows that CHs are getting activated for fast wind. (b) shows that the vicinity of active regions and polar CHs are getting activated for slow wind. 

We welcome researchers interested in space weather, ML/AI and data science to join us for collaboration and as PhD / Post-docs in this group.

Space Weather and Ionospheric Studies

PI: Abhirup Datta

This group is involved in studying effect of ionosphere in low frequency radio astronomy. The group has recently acquired aGNSS receiver to study the ionosphere above Indore. The group is in talks to acquire IRNSS receiver from ISRO-SAC. The group isworking on studying and characterizing the ionosphere. There is already a dense grid of GNSS receivers in Northern India. Ourproposal will complement that in Central and Western India. This will allow us to predict the ionospheric conditions and modelthem with better precision. In turn, this will help in satellite and aerospace communication as well as making it possible toobserve at low radio frequencies. This study will help us to establish leadership in ionospheric research mainly in context ofastronomical observations. India’s role in SKA (Square Kilometer Array) can be used to share this information with the upcomingstate-of-the-art largest radio telescope in the world. Currently, 3 PhD students are working in this area.

Computational Astrophysics

PI: Bhargav Vaidya

Computational astrophysics opens new windows in the way we perceive and study the heavens. This rapidly growing newdiscipline in astronomy combines modern computational methods and algorithms to simulate and analyse data so as to discovernew phenomena, and to make predictions in astronomy, cosmology and planetary sciences.

Research in area of Computational Astrophysics is led by Dr Bhargav Vaidya whose research interests cover a wide range oftopics closely associated with Computational and theoretical aspects of Astrophysics. In particular, the main aim of his research isto develop synthetic observatory for multiple astrophysical sources to bridge results from state-of-the-art simulations withobservations and develop templates that can predict and or verify various features observed using existing and up-comingobservatories like ALMA, Lofar, SKA, TMT and CTA.

At present, the focus is on astrophysical jets that are a ubiquitous phenomenon seen in a wide variety of astrophysical sourceslike young stellar objects, Active galactic nuclei, Pulsar wind nebulae etc. The current goal is to study the interplay of differentprocesses that are responsible to accelerate particles to very high energies in these jets. Additionally, the goal is to combine theseacceleration mechanisms with various processes that contribute to radiative losses via synchrotron and Inverse Compton toproduce non-thermal emission commonly observed in jets. The condition for stability and the physics of magnetic energydissipation in large-scale collimated jets are also the major research interest of this group.

The synthetic observatory that will primarily be developed for Astrophysical jets will pave a new and versatile pathway toexpand research capabilities in the area of space weather modelling, simulating radio haloes, triggered star formation, accretiondisk physics and microscopic behaviour of astrophysical plasma.

Dr. Vaidya is one of the integral developer of a widely popular astrophysical code called PLUTO ( and has a strong collaborations with the developers in University of Torino (, Italy.

Neutron stars, Black Holes, and Magnetars

PI: Manoneeta Chakraborty

The research in this group encompasses a variety of high energy astrophysics topics with particular emphasis on compact objectphysics. Neutron stars and black holes exhibit the most extreme physical conditions in the universe. They offer the ideallaboratories to probe strong gravity, the properties of supranuclear matter and the most intense magnetic field conditions. Thegroup is actively involved in the timing and spectroscopic studies of stellar and super-massive black holes, neutron star, pulsarsand magnetars. The research here focuses deeply on the study of accretion in X-ray binary systems and its radiative propertiesand variabilities. The spectral evolution of such compact objects in both isolated and binary systems is studied to understand thebehaviour of the accretion disk and the corona during the outburst state of the X-ray binary. A multi-wavelength monitoring ofthese objects can reveal intricacies of the disk-jet connection and the hard X-ray component. Thermonuclear bursts and burstoscillations can be used to probe the surface properties of a neutron star and thus are the most promising candidates toconstrain the equation of state of ultra-degenerate supra-nuclear neutron star matter. Research is also carried out on pulsars –rotation powered, accretion powered and magnetically powered – and how blurring of classes among the different categories ofpulsars can lead to understanding about the evolution and lifecycle of pulsars. The timing and spectral variability are also studiedacross different scales – from stellar mass black holes in X-ray binaries to supermassive black holes in active galactic nuclei. Thegroup is also interested in investigating the connection of more recently discovered class of objects like Ultra-luminous X-raysources (ULXs) and fast radio bursts (FRBs) with current understanding of the compact objects.

For pursuing the above science problems data from multiple instruments across multiple wavelengths are analyzed. The researchinvolves extensive analysis of data from missions like RXTE, Chandra, Swift, XMM-Newton, NuStar, Astrosat, GMRT, VLA, SALTand many others. Apart from the electromagnetic window, the group is also interested in the observation of these objects in thegravitational wave window as these compact objects are the primary origins of gravitational waves either through mergers orthrough steady spin-down decay of pulsars.

Cosmic Dawn and Epoch of Reionization with the redshifted 21-cm line

PI: Suman Majumdar

Cosmology is the study of the evolutionary history of our Universe. One of the most important missing pictures in this history isthe Cosmic Dawn (CD) and the Epoch of Reionization (EoR), the period during which the very first sources of light were formedand thus marked the end of the dark ages. The radiation emitted by these sources gradually heated and “re”-ionized the neutraland cold hydrogen (HI) in their surrounding inter-galactic medium (IGM). Many fundamental issues regarding this era, such as itstiming, duration, and the properties of the sources driving it, are still unresolved. Observations of the redshifted 21-cm signal,emitted by the HI from this era, hold a great promise to resolve many of these puzzles. Several radio telescopes around theworld are currently in a race to detect this signal in great detail (for example — GMRT, LOFAR, MWA, PAPER, 21CMA). Veryrecently, scientists may have got the very first glimpses of this signal through the EDGES experiment. The upcoming enormousSquare Kilometre Array (SKA), an international radio telescope of which India is also a member, is expected to be able to imagethe HI distribution at different cosmic times from the beginning to the end of this era, owing to its great sensitivity. Once theredshifted 21-cm signal from the CD and the EoR is detected, one would require a robust data analysis and interpretation pipelineto answer the unresolved questions regarding this epoch.

My main research interest is focused on the development of such interpretation pipelines. As the actual signal is yet to bedetected, such pipelines need to be trained and tested on simulated signal data sets. A major part of my past research focused onthe development of detailed but fast computer simulations of the signal, to study different models of reionizations at a relativelylow computational cost. Additionally, in the recent past and present, I have been studying different statistical estimators (such asthe power spectrum, bispectrum, etc.) of the signal to identify its various unique characteristics, which can be used for theconfirmative detection as well as for constraining the different reionization model parameters. As a member of the internationalScience Working Group for the CD and EoR with the SKA, I am involved in the development of such interpretation tools andpipelines along with my international collaborators. As soon as the SKA becomes operational and starts observing this epoch, it will open up a new exciting phase of cosmological exploration, which will enable us to better understand the Epoch ofReionization. Cosmologists around the world are eagerly waiting for this upcoming new era of “21-cm cosmology” that promisesto revolutionize our understanding of the history of the universe.

Radio Astronomy Instrumentation

PI: Siddharth Malu and Abhirup Datta

Having set up a Radio Frequency (RF) laboratory, the Centre has received funds from DST-SERB to make a 4-element radio interferometer at 1.4 and 5 GHz. Other than constructing this pathfinder, the RF lab also helps characterize RF properties of novel materials (made by one of the Associate members, Dr. Somaditya Sen ( ). Four dishes, of 2.5 metre diameter, are being readied for the interferometer. Currently, 1 PhD student and 1 JRF are working in this area.

Galaxy Clusters: Mergers and Interaction with CMB

PI: Siddharth Malu and Abhirup Datta

The group led by Dr. Datta and Dr. Malu studies mergers or collisions between clusters of galaxies, which leads to copiousamounts of radio emission, with a characteristic power law spectrum. Cluster-wide radio emission, which is found in centralregions of the cluster, is known as a radio halo, and when found in peripheral regions of clusters/mergers, is known as a radiorelic. There are several models that may explain the spectrum of this diffuse radio emission; however, the physical mechanismsthat accelerate particles in these cluster mergers, and cause enhancements of the magnetic fields in the clusters, are not entirelyunderstood. The group studies this diffuse radio emission at frequencies ranging from a few hundred MHz to ~ 20 GHz, tocharacterize its spectrum across a wide a range of frequencies, in order to gain insight into particle acceleration.

We also study inverse Compton scattering of cosmic microwave background photons from galaxy cluster electrons – known as theSunyaev-Zeldovich Effect (SZ effect), as a way to characterize pressure structures. With X-ray observations of thermal plasma, andradio observations of non-thermal plasma in clusters, it is possible to characterize the energy distribution of the galaxy clusterplasma. This is the aim of SZ effect observations at cm-wavelengths – these are the lowest frequencies at which SZ effect can bemeasured. Currently, 2 PhD students are working in this area.

Epoch of Reionization

PI: Abhirup Datta

The research in this group includes cosmological observations at low radio frequency with uGMRT, VLA, ATCA and in future SKA.Dr. Datta is a science team board member for Epoch of Reionization key science project in International SKA collaboration fromIndia. His current interest in this field is in foreground characterization and removal from reionization data from radiointerferometers using machine learning techniques like neural networks. This group is also working on using the state-of-the-artradio imaging/calibration algorithms to achieve high dynamic range imaging with low-frequency radio data-sets.

Dr. Datta is also collaborator in the international DARE (Dark Ages Radio Explorer) experiment which is currently a conceptmission being proposed to NASA for flight. This involves probing cosmic reionization using the global 21cm signal. Currently,three PhD students are working in this area.

Satellite-Based Navigation of Flight Vehicles

PI: Hari Hablani

ISRO has invested significant resources in establishing and providing navigation signals to users from national satellites systemNavIC (Navigation with Indian Constellation). Our research is concerned with developing algorithms to use these signals forprecise navigation of land vehicles, aeroplanes, satellites, launch vehicles, and missiles.

Remote sensing, communication and navigation satellites have payloads with mirrors, radars and infrared sensors that scan theearth, ocean and space with satellite platforms as their base. But this motion causes the platforms to reorient, which interfereswith the operation of the payloads. Additionally, due to environmental perturbing forces, the satellites deviate from theirintended ideal position and velocity, which interferes with the spatial registration of images acquired with remote sensing. Theobjective of this research is to understand the mechanics of these interferences and minimize them.

Agile Maneuvers of Reconnaissance and Surveillance Spacecraft with Control Moment Gyros

As spacecraft become heavy with their imaging payloads, the high-torque control moment gyros (CMGs) become the actuators ofchoice so that the spacecraft can be reoriented rapidly to acquire and track successive objects or areas of interest on the ground.But CMGs have spinning wheels and their momentum is turned around to produce the desired spacecraft control torque. But thisoccasionally results in alignment of the CMGs momentums, and the CMGs then cannot produce the desired torque, a state knownas a singularity. The objective of this research is to develop control algorithms for CMGs so that they steer away from singularitywhile acquiring and tracking the targets of interest. An additional objective is to demonstrate these controllers on a CMG testbedat ISRO Inertial Systems Unit.

Navigation of Precision Munition with Infrared and Millimeter-Wave Radar Sensors Homing inon Moving Ground Targets

The objective of this research is to develop navigation and guidance algorithms for dual-sensor air-to-surface precision munitionto neutralize moving tanks in mountainous areas covered with snow and trees or in deserts in inclement weather. The infraredand millimetre-wave radar dual-sensor is particularly apt for targets with electronic countermeasures, suppressed radar signature,and small temperature difference with cluttered surroundings. Navigation algorithms and Kalman filters are developed to meet orexceed the specified performance metrics of a probability of detection, a probability of false alarm and circular error probableradius of miss distance.

Dynamics and Guidance of Reentry Spacecraft

The objective of this research is to develop an understanding and a simulation of reentry dynamics under classic guidance laws,namely, the constant drag deceleration, and the constant descent rate to land at a specific site. Furthermore, the objectives are toovercome the limitations of these guidance laws by treating reentry as a two-point boundary value problem with initialconditions at the interface of the final orbit or approaching interplanetary trajectory and the reentry trajectory ending at thedesired landing latitude and longitude on the land or ocean with a desired touchdown velocity.

Entry and Descent Navigation of Lunar Lander

The objective of this research is to develop a high-accuracy inertial navigation system aided with a radar altimeter for entry anddescent on the Moon’s surface.