Multimodality for rainfall measurement

Hagit Messer*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


The need for accurate monitoring of rainfall, essential for many fields such as: hydrology, transportation and agriculture, calls for optimal use of all available resources. However, as the existing monitoring equipment is diverse, and different tools provide measurements of different nature, fusing these measurements is a challenging task. At one extreme, rain gauges provide local, direct measurements of the accumulated rainfall, and at the other end, satellite observations provide remote images of clouds, from which rainfall is estimated. In between, weather radar measures reflectivity which is non-linearly related to rainfall. In light of the new opportunities introduced by the use of physical measurements from cellular communication networks for rainfall monitoring, I first review the approaches for fusion of different rainfall direct and indirect measurements, distinguishing it from data assimilation, widely used in meteorology. I will then suggest a unified approach to the problem, combining parametric and non-parametric tools, and will present preliminary results.

Original languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings
EditorsPetr Tichavsky, Massoud Babaie-Zadeh, Olivier J.J. Michel, Nadege Thirion-Moreau
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319535463
StatePublished - 2017
Event13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017 - Grenoble, France
Duration: 21 Feb 201723 Feb 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10169 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017


FundersFunder number
Tel Aviv University


    • Data fusion
    • Multimodality
    • Rainfall monitoring


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