The Impact of Wind on Precipitation Measurements from a Compact Piezoelectric Sensor

Enrico Chinchella aDepartment of Civil, Chemical and Environmental Engineering, Università di Genova, Genoa, Italy
bWMO Measurement Lead Centre “B. Castelli” on Precipitation Intensity, Genoa, Italy

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Arianna Cauteruccio aDepartment of Civil, Chemical and Environmental Engineering, Università di Genova, Genoa, Italy
bWMO Measurement Lead Centre “B. Castelli” on Precipitation Intensity, Genoa, Italy

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Luca G. Lanza aDepartment of Civil, Chemical and Environmental Engineering, Università di Genova, Genoa, Italy
bWMO Measurement Lead Centre “B. Castelli” on Precipitation Intensity, Genoa, Italy

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Abstract

The measurement accuracy of an electroacoustic precipitation sensor, the Vaisala WXT520, is investigated to quantify the associated wind-induced bias. The device is widely used as a noncatching tool for measuring the integral features of liquid precipitation, specifically rainfall amount and intensity. A numerical simulation using computational fluid dynamics is used to determine the bluff-body behavior of the instrument when exposed to wind. The obtained airflow velocity patterns near the sensor are initially validated in a wind tunnel. Then, the wind-induced deviation and acceleration/deceleration of individual raindrop trajectories and the resulting impact on the measured precipitation are replicated using a Lagrangian particle tracking model. The sensor’s specific measurement principle necessitates redefining catch ratios and the collection efficiency in terms of the resulting kinetic energy and quantifying them as a function of particle Reynolds number and precipitation intensity, respectively. Wind speed and direction and drop size distribution have been simulated across various combinations. The results show that the measured precipitation is overestimated by up to 400% under the influence of wind. The presented adjustment curves can be used to correct raw rainfall measurements taken by the Vaisala WXT520 in windy conditions, either in real time or as a postprocessing function. The magnitude of the adjustment at any operational aggregation level largely depends on the local rainfall and wind regimes at the site of measurement and may have a strong impact on applications in regions where wind is frequent during low- to medium-intensity precipitation.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Arianna Cauteruccio, arianna.cauteruccio@edu.unige.it

Abstract

The measurement accuracy of an electroacoustic precipitation sensor, the Vaisala WXT520, is investigated to quantify the associated wind-induced bias. The device is widely used as a noncatching tool for measuring the integral features of liquid precipitation, specifically rainfall amount and intensity. A numerical simulation using computational fluid dynamics is used to determine the bluff-body behavior of the instrument when exposed to wind. The obtained airflow velocity patterns near the sensor are initially validated in a wind tunnel. Then, the wind-induced deviation and acceleration/deceleration of individual raindrop trajectories and the resulting impact on the measured precipitation are replicated using a Lagrangian particle tracking model. The sensor’s specific measurement principle necessitates redefining catch ratios and the collection efficiency in terms of the resulting kinetic energy and quantifying them as a function of particle Reynolds number and precipitation intensity, respectively. Wind speed and direction and drop size distribution have been simulated across various combinations. The results show that the measured precipitation is overestimated by up to 400% under the influence of wind. The presented adjustment curves can be used to correct raw rainfall measurements taken by the Vaisala WXT520 in windy conditions, either in real time or as a postprocessing function. The magnitude of the adjustment at any operational aggregation level largely depends on the local rainfall and wind regimes at the site of measurement and may have a strong impact on applications in regions where wind is frequent during low- to medium-intensity precipitation.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Arianna Cauteruccio, arianna.cauteruccio@edu.unige.it
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  • Aqilah, F., M. Islam, F. Juretic, J. Guerrero, D. Wood, and F. N. Ani, 2018: Study of mesh quality improvement for CFD analysis of an airfoil. IIUM Eng. J., 19, 203212, https://doi.org/10.31436/iiumej.v19i2.905.

    • Search Google Scholar
    • Export Citation
  • Asher, W. E., A. T. Jessup, R. Branch, and D. Clark, 2014: Observations of rain-induced near-surface salinity anomalies. J. Geophys. Res. Oceans, 119, 54835500, https://doi.org/10.1002/2014JC009954.

    • Search Google Scholar
    • Export Citation
  • Baire, Q., and Coauthors, 2022: Calibration uncertainty of non-catching precipitation gauges. Sensors, 22, 6413, https://doi.org/10.3390/s22176413.

    • Search Google Scholar
    • Export Citation
  • Baker, T. J., 2005: Mesh generation: Art or science? Prog. Aerosp. Sci., 41, 2963, https://doi.org/10.1016/j.paerosci.2005.02.002.

  • Bond, N. A., M. F. Cronin, C. Sabine, Y. Kawai, H. Ichikawa, P. Freitag, and K. Ronnholm, 2011: Upper ocean response to Typhoon Choi-Wan as measured by the Kuroshio Extension Observatory mooring. J. Geophys. Res., 116, C02031, https://doi.org/10.1029/2010JC006548.

    • Search Google Scholar
    • Export Citation
  • Busby, R. W., and K. Aderhold, 2020: The Alaska transportable array: As built. Seismol. Res. Lett., 91, 30173027, https://doi.org/10.1785/0220200154.

    • Search Google Scholar
    • Export Citation
  • Caracciolo, C., F. Porcù, and F. Prodi, 2008: Precipitation classification at mid-latitudes in terms of drop size distribution parameters. Adv. Geosci., 16, 1117, https://doi.org/10.5194/adgeo-16-11-2008.

    • Search Google Scholar
    • Export Citation
  • Cauteruccio, A., and L. G. Lanza, 2020: Parameterization of the collection efficiency of a cylindrical catching-type rain gauge based on rainfall intensity. Water, 12, 3431, https://doi.org/10.3390/w12123431.

    • Search Google Scholar
    • Export Citation
  • Cauteruccio, A., E. Brambilla, M. Stagnaro, L. G. Lanza, and D. Rocchi, 2021a: Wind tunnel validation of a particle tracking model to evaluate the wind‐induced bias of precipitation measurements. Water Resour. Res., 57, e2020WR028766, https://doi.org/10.1029/2020WR028766.

    • Search Google Scholar
    • Export Citation
  • Cauteruccio, A., E. Chinchella, M. Stagnaro, and L. G. Lanza, 2021b: Snow particle collection efficiency and adjustment curves for the hotplate precipitation gauge. J. Hydrometeor., 22, 941954, https://doi.org/10.1175/JHM-D-20-0149.1.

    • Search Google Scholar
    • Export Citation
  • Cauteruccio, A., M. Colli, and L. G. Lanza, 2021c: On neglecting free-stream turbulence in numerical simulation of the wind-induced bias of snow gauges. Water, 13, 363, https://doi.org/10.3390/w13030363.

    • Search Google Scholar
    • Export Citation
  • Cauteruccio, A., E. Chinchella, and L. G. Lanza, 2024: The overall collection efficiency of catching-type precipitation gauges in windy conditions. Water Resour. Res., 60, e2023WR035098, https://doi.org/10.1029/2023WR035098.

    • Search Google Scholar
    • Export Citation
  • Chinchella, E., 2023: Bluff-body aerodynamics and transfer functions for non-catching precipitation measurement instruments. Ph.D. thesis, University of Genoa, 245 pp., https://doi.org/10.15167/chinchella-enrico_phd2023-05-23.

  • Chinchella, E., A. Cauteruccio, M. Stagnaro, and L. G. Lanza, 2021: Investigation of the wind-induced airflow pattern near the Thies LPM precipitation gauge. Sensors, 21, 4880, https://doi.org/10.3390/s21144880.

    • Search Google Scholar
    • Export Citation
  • Cicoira, A., and Coauthors, 2022: In situ observations of the Swiss periglacial environment using GNSS instruments. Earth Syst. Sci. Data, 14, 50615091, https://doi.org/10.5194/essd-14-5061-2022.

    • Search Google Scholar
    • Export Citation
  • de Haij, M. J., and W. M. F. Wauben, 2010: Investigations into the improvement of automated precipitation type observations at KNMI. Proc. WMO Technical Conf. on Meteorological and Environmental Instruments and Methods of Observation (CIMO TECO 2010), Helsinki, Finland, KNMI, 12 pp., https://community.wmo.int/en/teco-2010-programme.

  • Förster, J., G. Giselher, and S. Stolte, 2004: A piezoelectrical rain gauge for application on buoys. J. Atmos. Oceanic Technol., 21, 179193, https://doi.org/10.1175/1520-0426(2004)021<0179:APRGFA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hu, R., W. Xu, W. Yan, T. Wu, X. He, and N. Cheng, 2023: Comparison between machine-learning-based turbidity models developed for different lake zones in a large shallow lake. Water, 15, 387, https://doi.org/10.3390/w15030387.

    • Search Google Scholar
    • Export Citation
  • Hunt, J. C. R., A. A. Wray, and P. Moin, 1988: Eddies, streams, and convergence zones in turbulent flows. Studying Turbulence Using Numerical Simulation Databases, 2: Proceedings of the 1988 Summer Program, NASA, 193–208, https://ntrs.nasa.gov/citations/19890015184.

  • Jarden, K. M., A. J. Jefferson, and J. M. Grieser, 2016: Assessing the effects of catchment-scale urban green infrastructure retrofits on hydrograph characteristics. Hydrol. Processes, 30, 15361550, https://doi.org/10.1002/hyp.10736.

    • Search Google Scholar
    • Export Citation
  • Jasak, H., A. Jemcov, and Z. Tukovic, 2007: OpenFOAM: A C++ library for complex physics simulations. Int. Workshop on Coupled Methods in Numerical Dynamics, Dubrovnik, Croatia, Inter-University Center, 1–20, http://csabai.web.elte.hu/http/simulationLab/jasakEtAlOpenFoam.pdf.

  • Joss, J., and A. Waldvogel, 1967: Ein Spektrograph für Niederschlagstropfen mit automatischer Auswertung. Pure Appl. Geophys., 68, 240246, https://doi.org/10.1007/BF00874898.

    • Search Google Scholar
    • Export Citation
  • Kinnell, P. I. A., 1972: The acoustic measurement of water-drop impacts. J. Appl. Meteor., 11, 691694, https://doi.org/10.1175/1520-0450(1972)011<0691:TAMOWD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Krebs, G., K. Kuoppamäki, T. Kokkonen, and H. Koivusalo, 2016: Simulation of green roof test bed runoff. Hydrol. Processes, 30, 250262, https://doi.org/10.1002/hyp.10605.

    • Search Google Scholar
    • Export Citation
  • Kuoppamäki, K., M. Hagner, M. Valtanen, and H. Setälä, 2019: Using biochar to purify runoff in road verges of urbanised watersheds: A large-scale field lysimeter study. Watershed Ecol. Environ., 1, 1525, https://doi.org/10.1016/j.wsee.2019.05.001.

    • Search Google Scholar
    • Export Citation
  • Lanza, L. G., and E. Vuerich, 2009: The WMO field intercomparison of rain intensity gauges. Atmos. Res., 94, 534543, https://doi.org/10.1016/j.atmosres.2009.06.012.

    • Search Google Scholar
    • Export Citation
  • Lanza, L. G., and A. Cauteruccio, 2021: Accuracy assessment and intercomparison of precipitation measurement instruments. Precipitation Science: Measurement, Remote Sensing, Microphysics and Modeling, S. Michaelides, Ed., Elsevier, 3–35, https://doi.org/10.1016/B978-0-12-822973-6.00007-X.

  • Lanza, L. G., and Coauthors, 2021: Calibration of non‐catching precipitation measurement instruments: A review. Meteor. Appl., 28, e2002, https://doi.org/10.1002/met.2002.

    • Search Google Scholar
    • Export Citation
  • Madden, L. V., L. L. Wilson, and N. Ntahimpera, 1998: Calibration and evaluation of an electronic sensor for rainfall kinetic energy. Phytopathology, 88, 950959, https://doi.org/10.1094/PHYTO.1998.88.9.950.

    • Search Google Scholar
    • Export Citation
  • Merlone, A., and Coauthors, 2022: The INCIPIT project: Calibration and accuracy of non-catching instruments to measure liquid/solid atmospheric precipitation. WMO/CIMO Technical Conf. on Meteorological and Environmental Instruments and Methods of Observation (CIMO TECO-2022), Paris, France, World Meteorological Organization, 12 pp., https://community.wmo.int/en/activity-areas/imop/publications-and-iom-reports/teco-2022-presentations.

  • Mikhaylovskaya, V. V., 1964: Theory of measuring the size of raindrops by acoustic method. Sov. Hydrol. Selected Papers, 1, 8590.

  • Salmi, A., and J. Ikonen, 2005a: New piezoelectric Vaisala RAINCAP precipitation sensor, 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., P2.6, https://ams.confex.com/ams/Annual2005/webprogram/Paper85522.html.

  • Salmi, A., and J. Ikonen, 2005b: Piezoelectric precipitation sensor from Vaisala. Proc. WMO Technical Conf. on Meteorological and Environmental Instruments and Methods of Observation, Bucharest, Romania, World Meteorological Organization, WMO/TD-1265, IOM Rep. 82, 4–7, https://library.wmo.int/doc_num.php?explnum_id=9293.

  • Salmi, A., Elomaa, L., Kopsala, P., Laukkanen, E., and Oyj, V., 2011: Piezoelectric Vaisala raincap rain sensor applied to drop size distribution monitoring. Technical Conf. on Meteorological and Environmental Instruments and Methods of Observation, Helsinki, Finland, World Meteorological Organization, 1–7, https://library.wmo.int/pmb_ged/wmo-td_1462_en/2(11)_Salmi_Findland.pdf.

  • TFI Pty Ltd, 2011: Cobra pressure probe. TFI Ltd., accessed 11 December 2023, https://www.turbulentflow.com.au/Products/CobraProbe/CobraProbe.php.

  • Tuukka, P., 2015: Calibration of non-catching precipitation sensors (Vaisala). MeteoMet2 Workshop, Genoa, Italy, University of Genova, 14 pp., http://www.precipitation-intensity.it/pdf/fantato.pdf.

  • Tytell, J., F. Vernon, M. Hedlin, C. de Groot Hedlin, J. Reyes, B. Busby, K. Hafner, and J. Eakins, 2016: The USArray transportable array as a platform for weather observation and research. Bull. Amer. Meteor. Soc., 97, 603619, https://doi.org/10.1175/BAMS-D-14-00204.1.

    • Search Google Scholar
    • Export Citation
  • Vaisala, 2012: Vaisala Weather Transmitter WXT520, users’ guide. Vaisala, 171 pp., https://www.vaisala.com/sites/default/files/documents/M210906EN-C.pdf.

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