Multimodality for rainfall measurement

Hagit Messer

doi:10.1007/978-3-319-53547-0_32

Multimodality for rainfall measurement

Hagit Messer

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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 language	English
Title of host publication	Latent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings
Editors	Petr Tichavsky, Massoud Babaie-Zadeh, Olivier J.J. Michel, Nadege Thirion-Moreau
Publisher	Springer Verlag
Pages	333-343
Number of pages	11
ISBN (Print)	9783319535463
DOIs	https://doi.org/10.1007/978-3-319-53547-0_32
State	Published - 2017
Externally published	Yes
Event	13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017 - Grenoble, France Duration: 21 Feb 2017 → 23 Feb 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10169 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017
Country/Territory	France
City	Grenoble
Period	21/02/17 → 23/02/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Data fusion
Multimodality
Rainfall monitoring

Access to Document

10.1007/978-3-319-53547-0_32

Cite this

Messer, H. (2017). Multimodality for rainfall measurement. In P. Tichavsky, M. Babaie-Zadeh, O. J. J. Michel, & N. Thirion-Moreau (Eds.), Latent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings (pp. 333-343). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10169 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-53547-0_32

Messer, Hagit. / Multimodality for rainfall measurement. Latent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings. editor / Petr Tichavsky ; Massoud Babaie-Zadeh ; Olivier J.J. Michel ; Nadege Thirion-Moreau. Springer Verlag, 2017. pp. 333-343 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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doi = "10.1007/978-3-319-53547-0_32",

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Messer, H 2017, Multimodality for rainfall measurement. in P Tichavsky, M Babaie-Zadeh, OJJ Michel & N Thirion-Moreau (eds), Latent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10169 LNCS, Springer Verlag, pp. 333-343, 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, Grenoble, France, 21/02/17. https://doi.org/10.1007/978-3-319-53547-0_32

Multimodality for rainfall measurement. / Messer, Hagit.
Latent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings. ed. / Petr Tichavsky; Massoud Babaie-Zadeh; Olivier J.J. Michel; Nadege Thirion-Moreau. Springer Verlag, 2017. p. 333-343 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10169 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Messer H. Multimodality for rainfall measurement. In Tichavsky P, Babaie-Zadeh M, Michel OJJ, Thirion-Moreau N, editors, Latent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings. Springer Verlag. 2017. p. 333-343. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-53547-0_32

Multimodality for rainfall measurement

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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