• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 18, Pages: 908-913

Original Article

Survey of Radiometric Techniques for Prediction of Meteorological Phenomena

Received Date:09 February 2022, Accepted Date:08 April 2022, Published Date:25 May 2022


Objectives: The present work illustrates an extensive review of the field of prediction of meteorological phenomena using radiometric measurements and machine learning with specific focus on rain events and their effects on satellite communication. Methods: Multiple types of prediction systems and mechanisms are reviewed, with focus on estimation of atmospheric phenomena using standard statistical models and radiometric measurements, with additional focus on the application of modern machine learning-based techniques for accurate estimate generation. Recent work in the domain has been compared, largely in tabular format, with respect to critical statistics such as correlation coefficient, root mean square error, and computational complexity of the techniques. Findings: The systems and mechanisms reviewed allow the identification of opportunities in establishment of novel techniques for prediction of meteorological effects and their influence on parameters such as communication signal attenuation. It is also established through the work that there is lack of a suitably accurate model for prediction of rain attenuation for geographical regions prone to greater variations in weather, such as the tropical regions. Consequently, some of the most recent work in this domain has been analyzed in this paper with a view to determine optimal techniques for different scenarios. Novelty: The survey identifies the opportunity to improve upon established models for prediction of rain phenomena and their effects on microwave and millimeter wave communication signal attenuation, as well as surveys modern estimation techniques in detail, with a specific focus on statistical and machine learning based methods which can guarantee greater accuracy with significant variation in the observed parameters such as brightness temperature and rain rate. The work seeks to clearly compare some of the newest techniques in the domain with respect to their efficiency as well as complexity, for practical applications.

Keywords: meteorological phenomena; prediction; radiometric measurements; microwave; millimeter wave; brightness temperature; machine learning


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© 2022 Bhattacharyya et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)


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