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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2018-447
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-447
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Nov 2018

12 Nov 2018

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This preprint has been withdrawn by the authors.

Using Snowfall Intensity to Improve the Correction of Wind-Induced Undercatch in Solid Precipitation Measurements

Matteo Colli1,2, Mattia Stagnaro2,3, Luca Lanza2,3, Roy Rasmussen4, and Julie M. Thériault5 Matteo Colli et al.
  • 1Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department, University of Genoa, Genoa, 16145, Ital
  • 2WMO/CIMO Lead Centre "B. Castelli" on Precipitation Intensity, Genoa, 16145, Italy
  • 3Department of Civil, Chemical and Environmental Engineering, University of Genoa, Genoa, 16145, Italy
  • 4Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montreal, H2X 3Y7, Quebec, Canada
  • 5Research applications Laboratory, National Center for Atmospheric Research, Boulder, 80307-3000, Colorado, USA

Abstract. Transfer functions are generally used to adjust for the wind-induced undercatch of solid precipitation measurements. These functions are derived based on the variation of the collection efficiency with wind speed for a particular type of gauge, either using field experiments or based on numerical simulation. Most studies use the wind speed alone, while others also include surface air temperature and/or precipitation type to try to reduce the scatter of the residuals at a given wind speed. In this study, we propose the use of the measured precipitation intensity to improve the effectiveness of the transfer function.

This is achieved by applying optimized curve fitting to field measurements from the Marshall field-test site (CO, USA). The use of a non-gradient optimization algorithm ensures optimal binning of experimental data according to the parameter under test. The results reveal that using precipitation intensity as an explanatory variable significantly reduce the scatter of the residuals. The scatter reduction as indicated by the Root Mean Square Error (RMSE) is confirmed by the analysis of the recent quality controlled data from the WMO/SPICE campaign, showing that this approach can be applied to a variety of locations and catching-type gauges.

We demonstrate the physical basis of the relationship between the collection efficiency and the measured precipitation intensity, due to the correlation of large particles with high intensities, by conducting a Computational Fluid-Dynamics (CFD) simulation. We use a Reynolds Averaged Navier-Stokes SST k-ω model coupled with a Lagrangian particle-tracking model. Results validate the hypothesis of using the measured precipitation intensity as a key parameter to improve the correction of wind-induced undercatch.

Findings have the potential to improve operational measurements since no additional instrument other than a wind sensor is required to apply the correction. This improves the accuracy of precipitation measurements without the additional cost of ancillary instruments such as particle counters.

This preprint has been withdrawn.

Matteo Colli et al.

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Matteo Colli et al.

Matteo Colli et al.

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Short summary
Our results provide geoscience scientists, meteorological and hydrological services with an improved method to correct the snow measurements from its main source of uncertainty (the wind-induced undercatch of snow particles). The correction builds upon existing approaches developed during the WMO SPICE program and proposes the use of the snowfall intensity variable. The analysis takes advantage of both field datasets provided by SPICE and results of computational fluid-dynamics simulations.
Our results provide geoscience scientists, meteorological and hydrological services with an...
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