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 Prateek Kumar Dongre

Prateek Kumar Dongre

Research student,


I am currently PhD student in the Astronomy Instrumentation Group (AIG) in the School of Physics and Astronomy at Cardiff University. I have research interests in Atmospheric Radiative Transfer, Satellite Remote Sensing, Information theory, Retreival modelling. Before joining my PhD, I did my bachelors (B.Tech) in Electronics and Telecommunication Enggineering from SGSITS, Indore and Masters (M. Tech) in Satellite Remote Sensing from Indian Institute of Remote Sensing (IIRS), Indian Space Research Orgnization (ISRO), Dehradun. I am pursuing my doctoral research (PhD) under the supervision of Prof. Peter Hargrave


Research interests

My PhD research mainly focus on analysis of information from various types of new earth observation intrument concepts for meteorology and climtology applications. I also research into various mathematical techniuqes for analyzing the satellite information (in terms of information content and retrievals and optimizing the satellite system framework. Mostly, my research sits at the intersection of satellite instrument/detector technology and satellite remote sensing applications (atmosphere, oceans etc.), it is mainly because my educational background in an unqiue combination of instrument and application science. 


Atmospheric Remote Sensing: Atmospheric Radiative Transfer, Satellite Remote Sensing, Information theory, Retreival modelling.

Machine Learning and Artificial Intelligence for Satellite Earth Observation: Leveraging Artifical Intelligence and Machine Learning (ML) in the exploitation of Satellite Earth Observations and Numerical Weather Prediction. Following are the major aspects of AI and ML in which I am interested for my PhD research

  • Optimal utilization of Earth Observation data using ML techniques: Improving efficiency of environmental and satellite atmospheric data processing (processing and compresssion etc.) and exploitation for cost effectiveness using artificial intelligence and Machine Learning.

  • Application of Machine Learning and AI techniques in Atmospheric Remote sensing: Use of AI & ML applications in the area of environmental data exploitation of satellite data, high-level information extraction in the area of Numerical Weather Prediction (NWP), data assimilation and forecasting, as well as for extreme weather prediction and nowcasting.

  • Signal Processing Techniques for Atmospheric Remote Sensing: Machine Learning Techniques for Designing Retreival Algortihm for Satellite Data such as: Deep Learning, Neural Networks (NN), Probabilistic Modelling and Bayesian Inference).

  • Application of Kernel methods for Satellite Data Dimension Reducton: One of the famous method for satellite data dimensionality reduction is Principal Component Analysis (PCA), I am looking into kernel methods point of view for data compression and optimziation of information.

New Satellite Atmospheric Sounding Instruments/Detector Techonologies: Quasi-optical system, Superconducting detectors (TES, KIDs).


  • 2016-Present: PhD Researcher, at School of Physics and Astronomy, Cardiff University, UK

  • 2014-2016: M.Tech in Satellite Remote Sensing and GIS (with specialization in Satellite Image Analysis and Photogrammetry), Indian Institute of Remote Sensing (IIRS), Indian Space Research Organization (ISRO), Dehradun, Uttarkhand, India

  • 2010-2014: B.E. in Electronics and Telecommunication Engineering, SGSITS, Indore, Madhya Pradesh, India


  • 2018-Present: Visiting PhD Researcher, at Satellite Applications Division, UK Met Office, Exeter, UK

  • 2018: Visiting PhD Researcher, at Data Assimilation Research Center (DARC), Department of Meteorology, University of Reading, Reading, UK



A millimeter-wave on-chip superconducting filter bank spectrometer for atmospheric science. Lead author- Dr. Angiola Orlando and Co-authors- HyMAS Team. Submitted in SPIE Conference on Millimeter, Submillimeter and Far-IR Detectors and Instrumentation for Astronomy X

"First Characterization of a Superconducting Filter-bank Spectrometer for Hyper-spectral Microwave Atmospheric Sounding with Transition Edge Sensors". D.J.Goldie, C.N.Thomas, S.Withington, A.Orlando, R.Sudiwala, P. Hargrave and Prateek Kumar Dongre. Submitted and Under review in Journal of Applied Physics. Weblink - 


Prateek Kumar Dongre, Stephan Havemann, Peter Hargrave, Angiola Orlando, Rashmikant Sudiwala, Christopher Thomas, David Goldie, and Stafford Withington. "End-to-End Instrument Performance Simulation System (EIPS) Framework: Application to Satellite Microwave Atmospheric Sounding Systems." Remote Sensing 11, no. 12 (2019): 1412. (Online link: )


Prateek Kumar Dongre et al. 2018 "Information content analysis for a novel TES-based hyperspectral microwave atmospheric sounding instrument," Proc. SPIE 10786, Remote Sensing of Clouds and the Atmosphere XXIII, 1078608 (9 October 2018); Presented at SPIE Remote Sensing Symposium at Berlin, Germany (

Thomas, C et al 2018. Transition Edge Sensors Superconducting Filterbank Spectrometers for Hyperspectral Microwave Atmospheric Sounding. Presented at ESA Millimeter Wave Workshop in December 2018 at ESA/ESTEC, Netherlands. Soon will be published in Conference Proceedings.


Hargrave, P et al. 2017. THz spectroscopy of the atmosphere for climatology and meteorology applications. Presented at: Next-Generation Spectroscopic Technologies X, Anaheim, California, United States, 9th April 2017 Presented at Druy, M. A. et al. eds.SPIE Proceedings: Next-Generation Spectroscopic Technologies X, Vol. 10210., Vol. 102101. Proceedings of SPIE, (10.1117/12.2263626)

Flatman, B et al 2017. "An on-chip filter bank spectrometer based on transition edge sensors for meteorology and climatology". Presented at International Low Temperature Detector Workshop (LTD 2018), Fukoka, Japan (Submiited)

Upcoming research papers, will be listed here soon. Keep tune in !!



Professor Peter Hargrave

Director of Innovation and Engagement
Deputy Head of Astronomy Instrumentation Group

Simon Doyle

Dr Simon Doyle

Astronomy Instrumentation Group