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Journal of Vibration and Acoustics | Volume 137 | Issue 3 | Technical Brief
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,
IFP-CNRS,
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
Article
References
Figures

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.
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References

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Sankaran, M., Hanan, N. P., Scholes, R. J., Ratnam, J., Augustine, D. J., Cade, B. S., Gignoux, J., Higgins, S. I., Le Roux, X., Ludwig, F., Ardo, J., Banyikwa, F., Bronn, A., Bucini, G., Caylor, K. K., Coughenour, M. B., Diouf, A., Ekaya, W., Feral, C. J., February, E. C., Frost, P. G. H., Hiernaux, P., Hrabar, H., Metzger, K. L., Prins, H. H. T., Ringrose, S., Sea, W., Tews, J., Worden, J., and Zambatis, N., 2005, “Determinants of Woody Cover in African Savannas,” Nature, 438(7069), pp. 846–849. [CrossRef] [PubMed]
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Millan, M. M., Estrela, M. J., Sanz, M. J., Mantilla, E., Martin, M., Pastor, F., Salvador, R., Vallejo, R., Alonso, L., Gangoiti, G., Ilardia, J. L., Navazo, M., Albizuri, A., Artinano, B., Ciccioli, P., Kallos, G., Carvalho, R. A., Andres, D., Hoff, A., Werhahn, J., Seufert, G., and Versino, B., 2005, “Climatic Feedbacks and Desertification: The Mediterranean Model,” J. Clim., 18(5), pp. 684–701. [CrossRef]
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von Hardenberg, J., Ferraris, L., Rebora, N., and Provenzale, A., 2007, “Meteorological Uncertainty and Rainfall Downscaling,” Nonlin. Processes Geophys., 14(3), pp. 193–199. [CrossRef]
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Gaucherel, C., 2001, “Pluviophone,” P.Intellectuelle, ed., INPI, I. N. d. l., Paris, p. 7.
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Arthur-Hartranft, S. T., Carlson, T. N., and Clarke, K. C., 2003, “Satellite and Ground-Based Microclimate and Hydrologic Analyses Coupled With a Regional Urban Growth Model,” Remote Sens. Environ., 86(3), pp. 385–400. [CrossRef]
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Mechoso, C. R., Robertson, A. W., Barth, N., Davey, M. K., Delecluse, P., Gent, P. R., Ineson, S., Kirtman, B., Latif, M., Letreut, H., Nagai, T., Neelin, J. D., Philander, S. G. H., Polcher, J., Schopf, P. S., Stockdale, T., Suarez, M. J., Terray, L., Thual, O., and Tribbia, J. J., 1995, “The Seasonal Cycle Over the Tropical Pacific in Coupled Ocean–Atmosphere General Circulation Models,” Mon. Weather Rev., 123(9), pp. 2825–2838. [CrossRef]
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Gaucherel, C., 2005, “Pluviophone Actif,” P.Intellectuelle, ed., INPI, I. N. d. l., Paris, p. 6.
10
Nystuen, J. A., 2001, “Listening to Raindrops From Underwater: An Acoustic Disdrometer,” Atmos. Oceanic Technol., 18(10), pp. 1640–1657. [CrossRef]
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Lemon, D. D., Farmer, D. M., and Watts, D. R., 1984, “Acoustic Measurements of Wind Speed and Precipitation Over a Continental Shelf,” J. Geophys. Res., 89(C3), pp. 3462–3472. [CrossRef]
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de Jong, S., 2010, “Low Cost Disdrometer,” Ph.D. thesis, Delft University of Technology, Delft, The Netherlands.
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Harris, C., 1966, “Absorption of Sound in Air Versus Humidity and Temperature,” J. Acoust. Soc. Am., 40(1), pp. 148–159. [CrossRef]
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Ma., B. B., and Nystuen, J. A., 2005, “Passive Acoustic Detection and Measurement of Rainfall at Sea,” J. Atmos. Oceanic Technol., 22(8), pp. 1225–1248. [CrossRef]
15
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Figures

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.

+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.
View Large  |  Save Figure  |  Download Slide (.ppt)
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.
Grahic Jump Location
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.

+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.
View Large  |  Save Figure  |  Download Slide (.ppt)
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.
Grahic Jump Location
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.

+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.
View Large  |  Save Figure  |  Download Slide (.ppt)
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.
Grahic Jump Location
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.

+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.
View Large  |  Save Figure  |  Download Slide (.ppt)
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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