Technical Brief

The Pluviophone: Measuring Rainfall by Its Sound

[+] Author and Article Information
Cédric Gaucherel

French Institute of Pondicherry (IFP),
11 St. Louis Street,
Pondicherry 605001, India
e-mail: cedric.gaucherel@ifpindia.org

Victor Grimaldi

French Institute of Pondicherry,
Pondicherry 605001, India

1Corresponding author.

Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received March 19, 2014; final manuscript received January 13, 2015; published online March 13, 2015. Assoc. Editor: Lonny Thompson.

J. Vib. Acoust 137(3), 034504 (Jun 01, 2015) (7 pages) Paper No: VIB-14-1085; doi: 10.1115/1.4029645 History: Received March 19, 2014; Revised January 13, 2015; Online March 13, 2015

Rainfall, one of the most important resources for all life and for the environment, is also the most difficult meteorological parameter to measure, mainly due to its nonstationnarity in space and time. Several powerful instruments exist today to measure rainfall, but they often suffer from some strong disadvantages, ranging from high costs and variable space and time coverage to low accuracy. In this study, we explain how a measure the sound of the falling rain could provide a reliable metric of the rainfall. We demonstrate its reliability in a specific case study, for a long rainy tropical event of the Indian monsoon and in noisy conditions. The final determination coefficient computed in cross validation reaches R2 = 0.9, without any treatment of the signals other than a simple smoothing window. If confirmed through more intensive research, our findings could help in the design of a highly useful acoustic rain gauge, of great value to developing countries that experience intense but poorly studied rainy seasons.

Copyright © 2015 by ASME
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Grahic Jump Location
Fig. 1

PP electronic synoptic (a), with the adjustable wave guide schema on left-hand side, and a schematic view of the experiment (b). It is simultaneously raining both on the meteorological station and the canopy tree. Both the meteorological station and the PP are transmitting data to the computer.

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Fig. 2

Diagram showing the overall principle of our analysis for a pass-band analysis. Starting from a fully recorded spectrogram (a), we extract a pass-band time series (here at the resonance frequency of the PP guide) and smooth it here with an 8 min moving window (b); we compare it to the rainfall reference measurements similarly smoothed (c), and get a regression line to which a determination coefficient is assigned for the corresponding 8 min smoothing (d); and the R2 coefficient variations along to the smoothing parameter (e) is quantitatively illustrating the final assessment of the PP.

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Fig. 3

Spectrogram (a), and 8 min smoothed pass-band time series (b) of the field experiment. They correspond to curves a, b, and c of Fig. 2, respectively. Rainfall rates (in black) are superimposed on the time domain (plain gray), 1818–2018 Hz (dashed gray) and 1718–2118 Hz (dash-dotted gray) pass-bands (in mV2). Time series are sometimes multiplied by constants to be superimposed.

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Fig. 4

Final error variations of the PP during the field experiment (a), and example of prediction of the rainfall rate (b), with the corresponding correlation (R2 = 0.84*** for a 15 min smoothing). They correspond to curves (d) and (e) of Fig. 2, respectively. The RMSE (a) between rainfall rates and PP records are displayed as a function of the moving window size (in minutes). The time domain (plain gray), 1818–2018 Hz (dashed gray) and 1718–2118 Hz (dotted gray) pass-bands are shown with their associated uncertainties. Observed and predicted rainfall time series (b) are shown in plain and dashed lines, respectively.



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