Learning-based rainfall estimation via communication satellite links

Gharanjik, Ahmad; Mishra, Kumar Vijay; Shankar, Bhavani; Ottersten, Björn

doi:10.1109/SSP.2018.8450726

Reference : Learning-based rainfall estimation via communication satellite links


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/38130

Title :	Learning-based rainfall estimation via communication satellite links
Language :	English
Author, co-author :	Gharanjik, Ahmad [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Mishra, Kumar Vijay []
	Shankar, Bhavani [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Ottersten, Björn [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	2018
Main document title :	2018 IEEE Statistical Signal Processing Workshop (SSP)
Publisher :	IEEE
Peer reviewed :	Yes
Audience :	International
ISBN :	978-1-5386-1571-3
Event name :	2018 IEEE Statistical Signal Processing Workshop (SSP)
Event date :	10-06-2018 to 13-06-2018
Event place (city) :	Freiburg
Event country :	Germany
Abstract :	[en] We present a method for estimating rainfall by opportunistic use of Ka-band satellite communication network. Our approach is based on the attenuation of the satellite link signal in the rain medium and exploits the nearly linear relation between the rain rate and the specific attenuation at Ka-band frequencies. Although our experimental setup is not intended to achieve high resolutions as millimeter wavelength weather radars, it is instructive because of easy availability of millions of satellite ground terminals throughout the world. The received signal is obtained over a passive link. Therefore, traditional weather radar signal processing to derive parameters for rainfall estimation algorithms is not feasible here. We overcome this disadvantage by employing neural network learning algorithms to extract relevant information. Initial results reveal that rainfall accumulations obtained through our method are 85% closer to the in situ rain gauge estimates than the nearest C-band German weather service Deutscher Wetterdienst (DWD) radar.
Permalink :	http://hdl.handle.net/10993/38130
DOI :	10.1109/SSP.2018.8450726

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